diff --git a/doc/LectureNotes/DataFiles/cancer.dot b/doc/LectureNotes/DataFiles/cancer.dot index 2699d824e..2a2517518 100644 --- a/doc/LectureNotes/DataFiles/cancer.dot +++ b/doc/LectureNotes/DataFiles/cancer.dot @@ -10,13 +10,13 @@ edge [fontname="helvetica"] ; 2 -> 3 ; 4 [label="gini = 0.0\nsamples = 239\nvalue = [[239, 0]\n[0, 239]]", fillcolor="#e58139"] ; 3 -> 4 ; -5 [label="worst compactness <= 0.085\ngini = 0.444\nsamples = 3\nvalue = [[2, 1]\n[1, 2]]", fillcolor="#fdf6f0"] ; +5 [label="worst smoothness <= 0.107\ngini = 0.444\nsamples = 3\nvalue = [[2, 1]\n[1, 2]]", fillcolor="#fdf6f0"] ; 3 -> 5 ; -6 [label="gini = 0.0\nsamples = 1\nvalue = [[0, 1]\n[1, 0]]", fillcolor="#e58139"] ; +6 [label="gini = 0.0\nsamples = 2\nvalue = [[2, 0]\n[0, 2]]", fillcolor="#e58139"] ; 5 -> 6 ; -7 [label="gini = 0.0\nsamples = 2\nvalue = [[2, 0]\n[0, 2]]", fillcolor="#e58139"] ; +7 [label="gini = 0.0\nsamples = 1\nvalue = [[0, 1]\n[1, 0]]", fillcolor="#e58139"] ; 5 -> 7 ; -8 [label="mean texture <= 20.84\ngini = 0.397\nsamples = 11\nvalue = [[8, 3]\n[3, 8]]", fillcolor="#fae9dd"] ; +8 [label="worst texture <= 29.455\ngini = 0.397\nsamples = 11\nvalue = [[8, 3]\n[3, 8]]", fillcolor="#fae9dd"] ; 2 -> 8 ; 9 [label="gini = 0.0\nsamples = 8\nvalue = [[8, 0]\n[0, 8]]", fillcolor="#e58139"] ; 8 -> 9 ; @@ -30,11 +30,11 @@ edge [fontname="helvetica"] ; 11 -> 13 ; 14 [label="worst texture <= 20.645\ngini = 0.202\nsamples = 167\nvalue = [[19, 148]\n[148, 19]]", fillcolor="#f0b68c"] ; 0 -> 14 [labeldistance=2.5, labelangle=-45, headlabel="False"] ; -15 [label="worst radius <= 17.74\ngini = 0.375\nsamples = 16\nvalue = [[12, 4]\n[4, 12]]", fillcolor="#f9e3d4"] ; +15 [label="worst concavity <= 0.318\ngini = 0.375\nsamples = 16\nvalue = [[12, 4]\n[4, 12]]", fillcolor="#f9e3d4"] ; 14 -> 15 ; 16 [label="gini = 0.0\nsamples = 11\nvalue = [[11, 0]\n[0, 11]]", fillcolor="#e58139"] ; 15 -> 16 ; -17 [label="fractal dimension error <= 0.002\ngini = 0.32\nsamples = 5\nvalue = [[1, 4]\n[4, 1]]", fillcolor="#f6d5bd"] ; +17 [label="radius error <= 0.251\ngini = 0.32\nsamples = 5\nvalue = [[1, 4]\n[4, 1]]", fillcolor="#f6d5bd"] ; 15 -> 17 ; 18 [label="gini = 0.0\nsamples = 1\nvalue = [[1, 0]\n[0, 1]]", fillcolor="#e58139"] ; 17 -> 18 ; diff --git a/doc/LectureNotes/DataFiles/cancer.png b/doc/LectureNotes/DataFiles/cancer.png index 2160d130c..621604b4a 100644 Binary files a/doc/LectureNotes/DataFiles/cancer.png and b/doc/LectureNotes/DataFiles/cancer.png differ diff --git a/doc/LectureNotes/_build/.doctrees/chapter1.doctree b/doc/LectureNotes/_build/.doctrees/chapter1.doctree index 36c4510c9..f90b9e2b6 100644 Binary files a/doc/LectureNotes/_build/.doctrees/chapter1.doctree and b/doc/LectureNotes/_build/.doctrees/chapter1.doctree differ diff --git a/doc/LectureNotes/_build/.doctrees/chapter10.doctree b/doc/LectureNotes/_build/.doctrees/chapter10.doctree index 2c328263e..d3b2a4831 100644 Binary files a/doc/LectureNotes/_build/.doctrees/chapter10.doctree and 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b/doc/LectureNotes/_build/html/_sources/week42.ipynb @@ -3,9 +3,7 @@ { "cell_type": "markdown", "id": "50ce4eae", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "\n", @@ -15,9 +13,7 @@ { "cell_type": "markdown", "id": "f46bd6b4", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "# Week 42 Constructing a Neural Network code with introduction to Tensor flow\n", "**Morten Hjorth-Jensen**, Department of Physics, University of Oslo and Department of Physics and Astronomy and Facility for Rare Isotope Beams, Michigan State University\n", @@ -28,9 +24,7 @@ { "cell_type": "markdown", "id": "8c0fa4d7", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Plan for week 42\n", "\n", @@ -70,9 +64,7 @@ { "cell_type": "markdown", "id": "89b6b637", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Lecture Thursday October 19" ] @@ -80,9 +72,7 @@ { "cell_type": "markdown", "id": "3a32ad82", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Review of the back propagation algorithm\n", "\n", @@ -95,9 +85,7 @@ { "cell_type": "markdown", "id": "4f9291ee", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Setting up the Back propagation algorithm\n", "\n", @@ -118,9 +106,7 @@ { "cell_type": "markdown", "id": "7753981f", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "\\delta_j^L = f'(z_j^L)\\frac{\\partial {\\cal C}}{\\partial (a_j^L)}.\n", @@ -130,9 +116,7 @@ { "cell_type": "markdown", "id": "8b093c71", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "Then we compute the back propagate error for each $l=L-1,L-2,\\dots,2$ as" ] @@ -140,9 +124,7 @@ { "cell_type": "markdown", "id": "96ca25bd", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "\\delta_j^l = \\sum_k \\delta_k^{l+1}w_{kj}^{l+1}f'(z_j^l).\n", @@ -152,9 +134,7 @@ { "cell_type": "markdown", "id": "a156d8bd", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "Finally, we update the weights and the biases using gradient descent for each $l=L-1,L-2,\\dots,2$ and update the weights and biases according to the rules" ] @@ -162,9 +142,7 @@ { "cell_type": "markdown", "id": "f35c8afe", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "w_{jk}^l\\leftarrow = w_{jk}^l- \\eta \\delta_j^la_k^{l-1},\n", @@ -174,9 +152,7 @@ { "cell_type": "markdown", "id": "ffa6d322", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "b_j^l \\leftarrow b_j^l-\\eta \\frac{\\partial {\\cal C}}{\\partial b_j^l}=b_j^l-\\eta \\delta_j^l,\n", @@ -186,9 +162,7 @@ { "cell_type": "markdown", "id": "7b6e59f6", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "The parameter $\\eta$ is the learning parameter discussed in connection with the gradient descent methods.\n", "Here it is convenient to use stochastic gradient descent (see the examples below) with mini-batches with an outer loop that steps through multiple epochs of training." @@ -197,9 +171,7 @@ { "cell_type": "markdown", "id": "e93ff00c", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Setting up a Multi-layer perceptron model for classification\n", "\n", @@ -225,9 +197,7 @@ { "cell_type": "markdown", "id": "3c437395", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "P(y = 0 \\mid \\boldsymbol{x}, \\boldsymbol{\\theta}) = \\frac{1}{1 + \\exp{(- \\boldsymbol{x}})} ,\n", @@ -237,9 +207,7 @@ { "cell_type": "markdown", "id": "3d7b1140", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "and" ] @@ -247,9 +215,7 @@ { "cell_type": "markdown", "id": "e3df5aec", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "P(y = 1 \\mid \\boldsymbol{x}, \\boldsymbol{\\theta}) = 1 - P(y = 0 \\mid \\boldsymbol{x}, \\boldsymbol{\\theta}) ,\n", @@ -259,9 +225,7 @@ { "cell_type": "markdown", "id": "63345646", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "where $y \\in \\{0, 1\\}$ and $\\boldsymbol{\\theta}$ represents the weights and biases\n", "of our network." @@ -270,9 +234,7 @@ { "cell_type": "markdown", "id": "6ac465b3", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Defining the cost function\n", "\n", @@ -282,9 +244,7 @@ { "cell_type": "markdown", "id": "cf06b4a0", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "\\mathcal{C}(\\boldsymbol{\\theta}) = - \\ln P(\\mathcal{D} \\mid \\boldsymbol{\\theta}) = - \\sum_{i=1}^n\n", @@ -295,9 +255,7 @@ { "cell_type": "markdown", "id": "719f761f", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "This last equality means that we can interpret our *cost* function as a sum over the *loss* function\n", "for each point in the dataset $\\mathcal{L}_i(\\boldsymbol{\\theta})$. \n", @@ -320,9 +278,7 @@ { "cell_type": "markdown", "id": "342de1d9", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "P(y_{ic} = 1 \\mid \\boldsymbol{x}_i, \\boldsymbol{\\theta}) = \\frac{\\exp{((\\boldsymbol{a}_i^{hidden})^T \\boldsymbol{w}_c)}}\n", @@ -333,9 +289,7 @@ { "cell_type": "markdown", "id": "211a69ba", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "which reduces to the logistic function in the binary case. \n", "The likelihood of this $C$-class classifier\n", @@ -345,9 +299,7 @@ { "cell_type": "markdown", "id": "5f0cd5a2", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "P(\\mathcal{D} \\mid \\boldsymbol{\\theta}) = \\prod_{i=1}^n \\prod_{c=0}^{C-1} [P(y_{ic} = 1)]^{y_{ic}} .\n", @@ -357,9 +309,7 @@ { "cell_type": "markdown", "id": "fe018e32", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "Again we take the negative log-likelihood to define our cost function:" ] @@ -367,9 +317,7 @@ { "cell_type": "markdown", "id": "9d48faca", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "\\mathcal{C}(\\boldsymbol{\\theta}) = - \\log{P(\\mathcal{D} \\mid \\boldsymbol{\\theta})}.\n", @@ -379,9 +327,7 @@ { "cell_type": "markdown", "id": "897c8b0c", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "See the logistic regression lectures for a full definition of the cost function.\n", "\n", @@ -391,9 +337,7 @@ { "cell_type": "markdown", "id": "68347a7f", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Example: binary classification problem\n", "\n", @@ -403,9 +347,7 @@ { "cell_type": "markdown", "id": "8425d868", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "\\mathcal{C}(\\boldsymbol{\\beta}) = - \\sum_{i=1}^n \\left(y_i\\log{p(y_i \\vert x_i,\\boldsymbol{\\beta})}+(1-y_i)\\log{1-p(y_i \\vert x_i,\\boldsymbol{\\beta})}\\right),\n", @@ -415,9 +357,7 @@ { "cell_type": "markdown", "id": "9108d4ac", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "where we had defined the logistic (sigmoid) function" ] @@ -425,9 +365,7 @@ { "cell_type": "markdown", "id": "77e0ec3b", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "p(y_i =1\\vert x_i,\\boldsymbol{\\beta})=\\frac{\\exp{(\\beta_0+\\beta_1 x_i)}}{1+\\exp{(\\beta_0+\\beta_1 x_i)}},\n", @@ -437,9 +375,7 @@ { "cell_type": "markdown", "id": "64ed867c", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "and" ] @@ -447,9 +383,7 @@ { "cell_type": "markdown", "id": "51819578", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "p(y_i =0\\vert x_i,\\boldsymbol{\\beta})=1-p(y_i =1\\vert x_i,\\boldsymbol{\\beta}).\n", @@ -459,9 +393,7 @@ { "cell_type": "markdown", "id": "db6532a5", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "The parameters $\\boldsymbol{\\beta}$ were defined using a minimization method like gradient descent or Newton-Raphson's method. \n", "\n", @@ -472,9 +404,7 @@ { "cell_type": "markdown", "id": "24e5e213", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "a_i^l = y_i = \\frac{\\exp{(z_i^l)}}{1+\\exp{(z_i^l)}},\n", @@ -484,9 +414,7 @@ { "cell_type": "markdown", "id": "d398c961", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "with" ] @@ -494,9 +422,7 @@ { "cell_type": "markdown", "id": "236d161c", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "z_i^l = \\sum_{j}w_{ij}^l a_j^{l-1}+b_i^l,\n", @@ -506,9 +432,7 @@ { "cell_type": "markdown", "id": "25e3004d", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "where the superscript $l-1$ indicates that these are the outputs from layer $l-1$.\n", "Our cost function at the final layer $l=L$ is now" @@ -517,9 +441,7 @@ { "cell_type": "markdown", "id": "9440c725", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "\\mathcal{C}(\\boldsymbol{W}) = - \\sum_{i=1}^n \\left(t_i\\log{a_i^L}+(1-t_i)\\log{(1-a_i^L)}\\right),\n", @@ -529,9 +451,7 @@ { "cell_type": "markdown", "id": "782f5282", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "where we have defined the targets $t_i$. The derivatives of the cost function with respect to the output $a_i^L$ are then easily calculated and we get" ] @@ -539,9 +459,7 @@ { "cell_type": "markdown", "id": "0e8498a5", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "\\frac{\\partial \\mathcal{C}(\\boldsymbol{W})}{\\partial a_i^L} = \\frac{a_i^L-t_i}{a_i^L(1-a_i^L)}.\n", @@ -551,9 +469,7 @@ { "cell_type": "markdown", "id": "68398b35", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "In case we use another activation function than the logistic one, we need to evaluate other derivatives." ] @@ -561,9 +477,7 @@ { "cell_type": "markdown", "id": "19887152", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## The Softmax function\n", "In case we employ the more general case given by the Softmax equation, we need to evaluate the derivative of the activation function with respect to the activation $z_i^l$, that is we need" @@ -572,9 +486,7 @@ { "cell_type": "markdown", "id": "80e8dc5d", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "\\frac{\\partial f(z_i^l)}{\\partial w_{jk}^l} =\n", @@ -585,9 +497,7 @@ { "cell_type": "markdown", "id": "68d33776", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "For the Softmax function we have" ] @@ -595,9 +505,7 @@ { "cell_type": "markdown", "id": "3c86943c", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "f(z_i^l) = \\frac{\\exp{(z_i^l)}}{\\sum_{m=1}^K\\exp{(z_m^l)}}.\n", @@ -607,9 +515,7 @@ { "cell_type": "markdown", "id": "efe53876", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "Its derivative with respect to $z_j^l$ gives" ] @@ -617,9 +523,7 @@ { "cell_type": "markdown", "id": "fce5b9b2", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "\\frac{\\partial f(z_i^l)}{\\partial z_j^l}= f(z_i^l)\\left(\\delta_{ij}-f(z_j^l)\\right),\n", @@ -629,9 +533,7 @@ { "cell_type": "markdown", "id": "97210471", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "which in case of the simply binary model reduces to having $i=j$." ] @@ -639,9 +541,7 @@ { "cell_type": "markdown", "id": "4f515591", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Developing a code for doing neural networks with back propagation\n", "\n", @@ -663,9 +563,7 @@ { "cell_type": "markdown", "id": "ec34f212", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Collect and pre-process data\n", "\n", @@ -713,10 +611,7 @@ "cell_type": "code", "execution_count": 1, "id": "e389e60e", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", @@ -768,9 +663,7 @@ { "cell_type": "markdown", "id": "9a264b82", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Train and test datasets\n", "\n", @@ -789,10 +682,7 @@ "cell_type": "code", "execution_count": 2, "id": "8750ea41", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "from sklearn.model_selection import train_test_split\n", @@ -827,9 +717,7 @@ { "cell_type": "markdown", "id": "d3897eca", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Define model and architecture\n", "\n", @@ -871,9 +759,7 @@ { "cell_type": "markdown", "id": "ad593a03", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Layers\n", "\n", @@ -911,9 +797,7 @@ { "cell_type": "markdown", "id": "e37b3844", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Weights and biases\n", "\n", @@ -932,10 +816,7 @@ "cell_type": "code", "execution_count": 3, "id": "3d909fc7", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "# building our neural network\n", @@ -958,9 +839,7 @@ { "cell_type": "markdown", "id": "b89c2d9f", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Feed-forward pass\n", "\n", @@ -986,9 +865,7 @@ { "cell_type": "markdown", "id": "435c0ced", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Matrix multiplications\n", "\n", @@ -1023,10 +900,7 @@ "cell_type": "code", "execution_count": 4, "id": "3037d7ab", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "# setup the feed-forward pass, subscript h = hidden layer\n", @@ -1069,9 +943,7 @@ { "cell_type": "markdown", "id": "61c33a3f", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Choose cost function and optimizer\n", "\n", @@ -1100,9 +972,7 @@ { "cell_type": "markdown", "id": "665f44ff", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Optimizing the cost function\n", "\n", @@ -1138,9 +1008,7 @@ { "cell_type": "markdown", "id": "2d0168d0", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Regularization\n", "\n", @@ -1172,9 +1040,7 @@ { "cell_type": "markdown", "id": "9c0a8db3", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Matrix multiplication\n", "\n", @@ -1213,10 +1079,7 @@ "cell_type": "code", "execution_count": 5, "id": "0bf3739e", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "# to categorical turns our integer vector into a onehot representation\n", @@ -1292,9 +1155,7 @@ { "cell_type": "markdown", "id": "33e198f3", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Improving performance\n", "\n", @@ -1313,9 +1174,7 @@ { "cell_type": "markdown", "id": "932f6c5e", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Full object-oriented implementation\n", "\n", @@ -1327,10 +1186,7 @@ "cell_type": "code", "execution_count": 6, "id": "91e351de", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "class NeuralNetwork:\n", @@ -1437,9 +1293,7 @@ { "cell_type": "markdown", "id": "e8c2feb6", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Evaluate model performance on test data\n", "\n", @@ -1456,10 +1310,7 @@ "cell_type": "code", "execution_count": 7, "id": "1534af1b", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "epochs = 100\n", @@ -1483,9 +1334,7 @@ { "cell_type": "markdown", "id": "85627e28", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Adjust hyperparameters\n", "\n", @@ -1497,10 +1346,7 @@ "cell_type": "code", "execution_count": 8, "id": "19382903", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "eta_vals = np.logspace(-5, 1, 7)\n", @@ -1528,9 +1374,7 @@ { "cell_type": "markdown", "id": "12bc42df", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Visualization" ] @@ -1539,10 +1383,7 @@ "cell_type": "code", "execution_count": 9, "id": "ec0dc239", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "# visual representation of grid search\n", @@ -1583,9 +1424,7 @@ { "cell_type": "markdown", "id": "4dd39506", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## scikit-learn implementation\n", "\n", @@ -1606,10 +1445,7 @@ "cell_type": "code", "execution_count": 10, "id": "d9dbb807", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "from sklearn.neural_network import MLPClassifier\n", @@ -1633,9 +1469,7 @@ { "cell_type": "markdown", "id": "214af3ab", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Visualization" ] @@ -1644,10 +1478,7 @@ "cell_type": "code", "execution_count": 11, "id": "d57415ac", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "# optional\n", @@ -1689,9 +1520,7 @@ { "cell_type": "markdown", "id": "fe7af77c", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Testing our code for the XOR, OR and AND gates\n", "\n", @@ -1716,9 +1545,7 @@ { "cell_type": "markdown", "id": "7e12b1cf", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## The AND and XOR Gates\n", "\n", @@ -1754,9 +1581,7 @@ { "cell_type": "markdown", "id": "4b5002b4", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Representing the Data Sets\n", "\n", @@ -1766,9 +1591,7 @@ { "cell_type": "markdown", "id": "a44df1a3", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "\\boldsymbol{X}=\\begin{bmatrix} 0 & 0 \\\\\n", @@ -1781,9 +1604,7 @@ { "cell_type": "markdown", "id": "acdb4e08", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "while the vector of outputs is $\\boldsymbol{y}^T=[0,1,1,0]$ for the XOR gate, $\\boldsymbol{y}^T=[0,0,0,1]$ for the AND gate and $\\boldsymbol{y}^T=[0,1,1,1]$ for the OR gate." ] @@ -1791,9 +1612,7 @@ { "cell_type": "markdown", "id": "0567fd0f", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Setting up the Neural Network\n", "\n", @@ -1804,10 +1623,7 @@ "cell_type": "code", "execution_count": 12, "id": "412401df", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "\"\"\"\n", @@ -1880,9 +1696,7 @@ { "cell_type": "markdown", "id": "53c52dab", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "Not an impressive result, but this was our first forward pass with randomly assigned weights. Let us now add the full network with the back-propagation algorithm discussed above." ] @@ -1890,9 +1704,7 @@ { "cell_type": "markdown", "id": "7d192a02", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## The Code using Scikit-Learn" ] @@ -1901,10 +1713,7 @@ "cell_type": "code", "execution_count": 13, "id": "766d5af6", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "# import necessary packages\n", @@ -1969,9 +1778,7 @@ { "cell_type": "markdown", "id": "9b182ae1", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Building neural networks in Tensorflow and Keras\n", "\n", @@ -1987,9 +1794,7 @@ { "cell_type": "markdown", "id": "60683ec5", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Tensorflow\n", "\n", @@ -2022,10 +1827,7 @@ "cell_type": "code", "execution_count": 14, "id": "8a0c6901", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "pip3 install tensorflow" @@ -2034,9 +1836,7 @@ { "cell_type": "markdown", "id": "b66e0227", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "and/or if you use **anaconda**, just write (or install from the graphical user interface)\n", "(current release of CPU-only TensorFlow)" @@ -2046,10 +1846,7 @@ "cell_type": "code", "execution_count": 15, "id": "df994f58", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "conda create -n tf tensorflow\n", @@ -2059,9 +1856,7 @@ { "cell_type": "markdown", "id": "b9005559", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "To install the current release of GPU TensorFlow" ] @@ -2070,10 +1865,7 @@ "cell_type": "code", "execution_count": 16, "id": "7287b5eb", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "conda create -n tf-gpu tensorflow-gpu\n", @@ -2083,9 +1875,7 @@ { "cell_type": "markdown", "id": "c066b083", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Using Keras\n", "\n", @@ -2098,10 +1888,7 @@ "cell_type": "code", "execution_count": 17, "id": "6582adea", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "conda install keras" @@ -2110,9 +1897,7 @@ { "cell_type": "markdown", "id": "7d305596", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "You can look up the [instructions here](https://keras.io/) for more information.\n", "\n", @@ -2122,9 +1907,7 @@ { "cell_type": "markdown", "id": "a4508850", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Collect and pre-process data\n", "\n", @@ -2135,10 +1918,7 @@ "cell_type": "code", "execution_count": 18, "id": "5f2256f6", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "# import necessary packages\n", @@ -2190,10 +1970,7 @@ "cell_type": "code", "execution_count": 19, "id": "a5dfa0e9", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "from tensorflow.keras.layers import Input\n", @@ -2219,10 +1996,7 @@ "cell_type": "code", "execution_count": 20, "id": "dd935ce0", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "\n", @@ -2249,10 +2023,7 @@ "cell_type": "code", "execution_count": 21, "id": "67158cb2", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "DNN_keras = np.zeros((len(eta_vals), len(lmbd_vals)), dtype=object)\n", @@ -2276,10 +2047,7 @@ "cell_type": "code", "execution_count": 22, "id": "86d74ee3", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "# optional\n", @@ -2318,9 +2086,7 @@ { "cell_type": "markdown", "id": "563d3f68", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## The Breast Cancer Data, now with Keras" ] @@ -2329,10 +2095,7 @@ "cell_type": "code", "execution_count": 23, "id": "34e6467a", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "\n", @@ -2506,9 +2269,7 @@ { "cell_type": "markdown", "id": "09879108", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Fine-tuning neural network hyperparameters\n", "\n", @@ -2534,9 +2295,7 @@ { "cell_type": "markdown", "id": "f8ec1769", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Hidden layers\n", "\n", @@ -2557,9 +2316,7 @@ { "cell_type": "markdown", "id": "43cc1fe5", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Which activation function should I use?\n", "\n", @@ -2588,9 +2345,7 @@ { "cell_type": "markdown", "id": "a9cbce9f", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Is the Logistic activation function (Sigmoid) our choice?\n", "\n", @@ -2620,9 +2375,7 @@ { "cell_type": "markdown", "id": "2dfb3f9a", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## The derivative of the Logistic funtion\n", "\n", @@ -2658,9 +2411,7 @@ { "cell_type": "markdown", "id": "f806c047", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## The RELU function family\n", "\n", @@ -2683,9 +2434,7 @@ { "cell_type": "markdown", "id": "ef9e2a08", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "ELU(z) = \\left\\{\\begin{array}{cc} \\alpha\\left( \\exp{(z)}-1\\right) & z < 0,\\\\ z & z \\ge 0.\\end{array}\\right.\n", @@ -2695,9 +2444,7 @@ { "cell_type": "markdown", "id": "2e2750d8", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Which activation function should we use?\n", "\n", @@ -2718,9 +2465,7 @@ { "cell_type": "markdown", "id": "4e566f13", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## More on activation functions, output layers\n", "\n", @@ -2738,9 +2483,7 @@ { "cell_type": "markdown", "id": "03205666", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Batch Normalization\n", "\n", @@ -2760,9 +2503,7 @@ { "cell_type": "markdown", "id": "ad7c3e53", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Dropout\n", "\n", @@ -2778,9 +2519,7 @@ { "cell_type": "markdown", "id": "c3b98a7c", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Gradient Clipping\n", "\n", @@ -2797,9 +2536,7 @@ { "cell_type": "markdown", "id": "e7f21477", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## A very nice website on Neural Networks\n", "\n", @@ -2809,9 +2546,7 @@ { "cell_type": "markdown", "id": "54968291", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## A top-down perspective on Neural networks\n", "\n", @@ -2854,9 +2589,7 @@ { "cell_type": "markdown", "id": "4500b85e", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Limitations of supervised learning with deep networks\n", "\n", @@ -2882,7 +2615,25 @@ ] } ], - "metadata": {}, + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.10" + } + }, "nbformat": 4, "nbformat_minor": 5 } diff --git a/doc/LectureNotes/_build/html/chapter1.html b/doc/LectureNotes/_build/html/chapter1.html index 2f2b2ea11..44221cffb 100644 --- a/doc/LectureNotes/_build/html/chapter1.html +++ b/doc/LectureNotes/_build/html/chapter1.html @@ -333,6 +333,16 @@ const thebe_selector_output = ".output, .cell_output" Week 42 Constructing a Neural Network code with introduction to Tensor flow +
  • + + Exercises weeks 43 and 44 + +
    • +
  • + + Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations + +

    • @@ -1066,13 +1076,13 @@ example of the functionality of Scikit-Learn.

    The intercept alpha: 
- [2.04828291]
+ [1.95815651]
 Coefficient beta : 
- [[4.85601654]]
-Mean squared error: 0.27
-Variance score: 0.89
+ [[5.03219974]]
+Mean squared error: 0.26
+Variance score: 0.90
 Mean squared log error: 0.01
-Mean absolute error: 0.40
+Mean absolute error: 0.41
    _images/chapter1_19_1.png @@ -1172,7 +1182,7 @@ a linear \(x\)-dependence we s
    _images/chapter1_33_0.png -
    0.005000000000000001
+
    0.004999999999999996
    diff --git a/doc/LectureNotes/_build/html/chapter10.html b/doc/LectureNotes/_build/html/chapter10.html index 2796f090c..88afb4026 100644 --- a/doc/LectureNotes/_build/html/chapter10.html +++ b/doc/LectureNotes/_build/html/chapter10.html @@ -333,6 +333,16 @@ const thebe_selector_output = ".output, .cell_output" Week 42 Constructing a Neural Network code with introduction to Tensor flow +
  • + + Exercises weeks 43 and 44 + +
    • +
  • + + Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations + +

    • @@ -1383,7 +1393,7 @@ the Hadamard product, meaning element-wise multiplication.

    Old accuracy on training data: 0.1440501043841336
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
    
 
    
@@ -1717,7 +1727,7 @@ Lambda =  10.0
 Accuracy score on test set:  0.19166666666666668
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
    
 
    
@@ -1726,7 +1736,7 @@ Lambda =  1e-05
 Accuracy score on test set:  0.10555555555555556
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
    
 
    
@@ -1735,7 +1745,7 @@ Lambda =  0.0001
 Accuracy score on test set:  0.08611111111111111
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
    
 
    
@@ -1744,7 +1754,7 @@ Lambda =  0.001
 Accuracy score on test set:  0.10555555555555556
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
    
 
    
@@ -1753,7 +1763,7 @@ Lambda =  0.01
 Accuracy score on test set:  0.08888888888888889
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
    
 
    
@@ -1762,7 +1772,7 @@ Lambda =  0.1
 Accuracy score on test set:  0.08611111111111111
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
    
 
    
@@ -1771,7 +1781,7 @@ Lambda =  1.0
 Accuracy score on test set:  0.08888888888888889
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
    
 
    
@@ -1780,11 +1790,11 @@ Lambda =  10.0
 Accuracy score on test set:  0.09166666666666666
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
@@ -1793,11 +1803,11 @@ Lambda =  1e-05
 Accuracy score on test set:  0.07777777777777778
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
@@ -1806,11 +1816,11 @@ Lambda =  0.0001
 Accuracy score on test set:  0.07777777777777778
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
@@ -1819,11 +1829,11 @@ Lambda =  0.001
 Accuracy score on test set:  0.07777777777777778
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
@@ -1832,11 +1842,11 @@ Lambda =  0.01
 Accuracy score on test set:  0.07777777777777778
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
@@ -1845,7 +1855,7 @@ Lambda =  0.1
 Accuracy score on test set:  0.07777777777777778
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
    
 
    
@@ -1854,11 +1864,11 @@ Lambda =  1.0
 Accuracy score on test set:  0.10555555555555556
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
@@ -1867,11 +1877,11 @@ Lambda =  10.0
 Accuracy score on test set:  0.07777777777777778
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
@@ -1880,11 +1890,11 @@ Lambda =  1e-05
 Accuracy score on test set:  0.07777777777777778
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
@@ -1893,11 +1903,11 @@ Lambda =  0.0001
 Accuracy score on test set:  0.07777777777777778
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
@@ -1906,11 +1916,11 @@ Lambda =  0.001
 Accuracy score on test set:  0.07777777777777778
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
@@ -1919,11 +1929,11 @@ Lambda =  0.01
 Accuracy score on test set:  0.07777777777777778
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
@@ -1932,11 +1942,11 @@ Lambda =  0.1
 Accuracy score on test set:  0.07777777777777778
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
@@ -1945,17 +1955,38 @@ Lambda =  1.0
 Accuracy score on test set:  0.07777777777777778
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
-
    Learning rate  =  10.0
-Lambda =  10.0
-Accuracy score on test set:  0.07777777777777778
+
    ---------------------------------------------------------------------------
+KeyboardInterrupt                         Traceback (most recent call last)
+Input In [8], in <cell line: 7>()
+      8 for j, lmbd in enumerate(lmbd_vals):
+      9     dnn = NeuralNetwork(X_train, Y_train_onehot, eta=eta, lmbd=lmbd, epochs=epochs, batch_size=batch_size,
+     10                         n_hidden_neurons=n_hidden_neurons, n_categories=n_categories)
+---> 11     dnn.train()
+     13     DNN_numpy[i][j] = dnn
+     15     test_predict = dnn.predict(X_test)
+
+Input In [6], in NeuralNetwork.train(self)
+     95 self.X_data = self.X_data_full[chosen_datapoints]
+     96 self.Y_data = self.Y_data_full[chosen_datapoints]
+---> 98 self.feed_forward()
+     99 self.backpropagation()
+
+Input In [6], in NeuralNetwork.feed_forward(self)
+     36 def feed_forward(self):
+     37     # feed-forward for training
+---> 38     self.z_h = np.matmul(self.X_data, self.hidden_weights) + self.hidden_bias
+     39     self.a_h = sigmoid(self.z_h)
+     41     self.z_o = np.matmul(self.a_h, self.output_weights) + self.output_bias
+
+KeyboardInterrupt: 
 
    
 
    
 
    
@@ -2001,22 +2032,6 @@ Accuracy score on test set:  0.07777777777777778
    -
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/953065564.py:4: RuntimeWarning: overflow encountered in exp
-  return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/953065564.py:4: RuntimeWarning: overflow encountered in exp
-  return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/953065564.py:4: RuntimeWarning: overflow encountered in exp
-  return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/953065564.py:4: RuntimeWarning: overflow encountered in exp
-  return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/953065564.py:4: RuntimeWarning: overflow encountered in exp
-  return 1/(1 + np.exp(-x))
-
    -
    -_images/chapter10_59_1.png -_images/chapter10_59_2.png -
    @@ -2052,329 +2067,6 @@ performance overall.

    -
    -
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
-  warnings.warn(
-
    -
    -
    Learning rate  =  1e-05
-Lambda =  1e-05
-Accuracy score on test set:  0.18333333333333332
-
    -
    -
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
-  warnings.warn(
-
    -
    -
    Learning rate  =  1e-05
-Lambda =  0.0001
-Accuracy score on test set:  0.18611111111111112
-
    -
    -
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
-  warnings.warn(
-
    -
    -
    Learning rate  =  1e-05
-Lambda =  0.001
-Accuracy score on test set:  0.13055555555555556
-
    -
    -
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
-  warnings.warn(
-
    -
    -
    Learning rate  =  1e-05
-Lambda =  0.01
-Accuracy score on test set:  0.24444444444444444
-
    -
    -
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
-  warnings.warn(
-
    -
    -
    Learning rate  =  1e-05
-Lambda =  0.1
-Accuracy score on test set:  0.23333333333333334
-
    -
    -
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
-  warnings.warn(
-
    -
    -
    Learning rate  =  1e-05
-Lambda =  1.0
-Accuracy score on test set:  0.12777777777777777
-
    -
    -
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
-  warnings.warn(
-
    -
    -
    Learning rate  =  1e-05
-Lambda =  10.0
-Accuracy score on test set:  0.1527777777777778
-
    -
    -
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
-  warnings.warn(
-
    -
    -
    Learning rate  =  0.0001
-Lambda =  1e-05
-Accuracy score on test set:  0.9111111111111111
-
    -
    -
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
-  warnings.warn(
-
    -
    -
    Learning rate  =  0.0001
-Lambda =  0.0001
-Accuracy score on test set:  0.8888888888888888
-
    -
    -
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
-  warnings.warn(
-
    -
    -
    Learning rate  =  0.0001
-Lambda =  0.001
-Accuracy score on test set:  0.8722222222222222
-
    -
    -
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
-  warnings.warn(
-
    -
    -
    Learning rate  =  0.0001
-Lambda =  0.01
-Accuracy score on test set:  0.8305555555555556
-
    -
    -
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
-  warnings.warn(
-
    -
    -
    Learning rate  =  0.0001
-Lambda =  0.1
-Accuracy score on test set:  0.8888888888888888
-
    -
    -
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
-  warnings.warn(
-
    -
    -
    Learning rate  =  0.0001
-Lambda =  1.0
-Accuracy score on test set:  0.8805555555555555
-
    -
    -
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
-  warnings.warn(
-
    -
    -
    Learning rate  =  0.0001
-Lambda =  10.0
-Accuracy score on test set:  0.8944444444444445
-
    -
    -
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
-  warnings.warn(
-
    -
    -
    Learning rate  =  0.001
-Lambda =  1e-05
-Accuracy score on test set:  0.975
-
    -
    -
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
-  warnings.warn(
-
    -
    -
    Learning rate  =  0.001
-Lambda =  0.0001
-Accuracy score on test set:  0.9777777777777777
-
    -
    -
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
-  warnings.warn(
-
    -
    -
    Learning rate  =  0.001
-Lambda =  0.001
-Accuracy score on test set:  0.9805555555555555
-
    -
    -
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
-  warnings.warn(
-
    -
    -
    Learning rate  =  0.001
-Lambda =  0.01
-Accuracy score on test set:  0.9861111111111112
-
    -
    -
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
-  warnings.warn(
-
    -
    -
    Learning rate  =  0.001
-Lambda =  0.1
-Accuracy score on test set:  0.9805555555555555
-
    -
    -
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
-  warnings.warn(
-
    -
    -
    Learning rate  =  0.001
-Lambda =  1.0
-Accuracy score on test set:  0.9777777777777777
-
    -
    -
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
-  warnings.warn(
-
    -
    -
    Learning rate  =  0.001
-Lambda =  10.0
-Accuracy score on test set:  0.9444444444444444
-
    -
    -
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
-  warnings.warn(
-
    -
    -
    Learning rate  =  0.01
-Lambda =  1e-05
-Accuracy score on test set:  0.9861111111111112
-
    -
    -
    Learning rate  =  0.01
-Lambda =  0.0001
-Accuracy score on test set:  0.9888888888888889
-
    -
    -
    Learning rate  =  0.01
-Lambda =  0.001
-Accuracy score on test set:  0.9888888888888889
-
    -
    -
    Learning rate  =  0.01
-Lambda =  0.01
-Accuracy score on test set:  0.9861111111111112
-
    -
    -
    Learning rate  =  0.01
-Lambda =  0.1
-Accuracy score on test set:  0.9888888888888889
-
    -
    -
    Learning rate  =  0.01
-Lambda =  1.0
-Accuracy score on test set:  0.9722222222222222
-
    -
    -
    Learning rate  =  0.01
-Lambda =  10.0
-Accuracy score on test set:  0.9527777777777777
-
    -
    -
    Learning rate  =  0.1
-Lambda =  1e-05
-Accuracy score on test set:  0.9027777777777778
-
-Learning rate  =  0.1
-Lambda =  0.0001
-Accuracy score on test set:  0.8583333333333333
-
    -
    -
    Learning rate  =  0.1
-Lambda =  0.001
-Accuracy score on test set:  0.8722222222222222
-
-Learning rate  =  0.1
-Lambda =  0.01
-Accuracy score on test set:  0.9055555555555556
-
    -
    -
    Learning rate  =  0.1
-Lambda =  0.1
-Accuracy score on test set:  0.8805555555555555
-
-Learning rate  =  0.1
-Lambda =  1.0
-Accuracy score on test set:  0.8722222222222222
-
    -
    -
    Learning rate  =  0.1
-Lambda =  10.0
-Accuracy score on test set:  0.8666666666666667
-
-Learning rate  =  1.0
-Lambda =  1e-05
-Accuracy score on test set:  0.08611111111111111
-
-Learning rate  =  1.0
-Lambda =  0.0001
-Accuracy score on test set:  0.10555555555555556
-
    -
    -
    Learning rate  =  1.0
-Lambda =  0.001
-Accuracy score on test set:  0.10555555555555556
-
-Learning rate  =  1.0
-Lambda =  0.01
-Accuracy score on test set:  0.17777777777777778
-
-Learning rate  =  1.0
-Lambda =  0.1
-Accuracy score on test set:  0.08333333333333333
-
    -
    -
    Learning rate  =  1.0
-Lambda =  1.0
-Accuracy score on test set:  0.08888888888888889
-
-Learning rate  =  1.0
-Lambda =  10.0
-Accuracy score on test set:  0.09444444444444444
-
    -
    -
    Learning rate  =  10.0
-Lambda =  1e-05
-Accuracy score on test set:  0.17222222222222222
-
-Learning rate  =  10.0
-Lambda =  0.0001
-Accuracy score on test set:  0.11666666666666667
-
-Learning rate  =  10.0
-Lambda =  0.001
-Accuracy score on test set:  0.10555555555555556
-
-Learning rate  =  10.0
-Lambda =  0.01
-Accuracy score on test set:  0.1388888888888889
-
    -
    -
    Learning rate  =  10.0
-Lambda =  0.1
-Accuracy score on test set:  0.11388888888888889
-
    -
    -
    Learning rate  =  10.0
-Lambda =  1.0
-Accuracy score on test set:  0.10555555555555556
-
-Learning rate  =  10.0
-Lambda =  10.0
-Accuracy score on test set:  0.09444444444444444
-
    -
    -
    @@ -2418,10 +2110,6 @@ Accuracy score on test set: 0.09444444444444444
    -
    -_images/chapter10_63_0.png -_images/chapter10_63_1.png -
    @@ -2460,14 +2148,6 @@ and/or if you use anaconda, just write (or install from the gra
    -
    -
      Input In [12]
-    conda create -n tf tensorflow
-          ^
-SyntaxError: invalid syntax
-
    -
    -

    To install the current release of GPU TensorFlow

    diff --git a/doc/LectureNotes/_build/html/chapter11.html b/doc/LectureNotes/_build/html/chapter11.html index aecd5c6bf..7fd994b10 100644 --- a/doc/LectureNotes/_build/html/chapter11.html +++ b/doc/LectureNotes/_build/html/chapter11.html @@ -333,6 +333,16 @@ const thebe_selector_output = ".output, .cell_output" Week 42 Constructing a Neural Network code with introduction to Tensor flow +
  • + + Exercises weeks 43 and 44 + +
    • +
  • + + Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations + +

    • @@ -2652,24 +2662,6 @@ Using TensorFlow results in a much better execution time. Try it!

    ---> 23 outgrads[parent] = add_outgrads(outgrads.get(parent), ingrad) 24 return outgrad[0] -File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:165, in add_outgrads(prev_g_flagged, g) - 163 if mutable: - 164 if sparse: ---> 165 return sparse_add(vs, prev_g, g), True - 166 else: - 167 return vs.mut_add(prev_g, g), True - -File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/tracer.py:48, in primitive.<locals>.f_wrapped(*args, **kwargs) - 46 return new_box(ans, trace, node) - 47 else: ----> 48 return f_raw(*args, **kwargs) - -File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:186, in sparse_add(vs, x_prev, x_new) - 183 @primitive - 184 def sparse_add(vs, x_prev, x_new): - 185 x_prev = x_prev if x_prev is not None else vs.zeros() ---> 186 return x_new.mut_add(x_prev) - KeyboardInterrupt:
    diff --git a/doc/LectureNotes/_build/html/chapter12.html b/doc/LectureNotes/_build/html/chapter12.html index 9f5dbc2f8..43ed02538 100644 --- a/doc/LectureNotes/_build/html/chapter12.html +++ b/doc/LectureNotes/_build/html/chapter12.html @@ -333,6 +333,16 @@ const thebe_selector_output = ".output, .cell_output" Week 42 Constructing a Neural Network code with introduction to Tensor flow +
  • + + Exercises weeks 43 and 44 + +
    • +
  • + + Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations + +

    • @@ -1297,7 +1307,7 @@ labels = (n_inputs) = (1797,) 

    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/keras/optimizer_v2/gradient_descent.py:102: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.
   super(SGD, self).__init__(name, **kwargs)
-2023-10-15 21:48:58.909327: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz
+2023-10-25 15:31:33.965944: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz
 
    ---------------------------------------------------------------------------
diff --git a/doc/LectureNotes/_build/html/chapter13.html b/doc/LectureNotes/_build/html/chapter13.html
index e53fbcecc..86ce44183 100644
--- a/doc/LectureNotes/_build/html/chapter13.html
+++ b/doc/LectureNotes/_build/html/chapter13.html
@@ -333,6 +333,16 @@ const thebe_selector_output = ".output, .cell_output"
    Week 42 Constructing a Neural Network code with introduction to Tensor flow
   
  
+ 
  • 
+  
+   Exercises weeks 43 and 44
+  
+ 
    • 
+ 
  • 
+  
+   Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations
+  
+ 
    • 
 
 
    
  
@@ -700,316 +710,316 @@ systems such as automatic translation and speech-to-text.
    
 
    Epoch 1/100
 
    
 
    
-
    2023-10-15 21:49:35.228942: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz
+
    2023-10-25 15:32:11.077734: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz
 
    
 
    
-
    50/50 - 3s - loss: 0.5276 - 3s/epoch - 66ms/step
+
    50/50 - 3s - loss: 1.4222 - 3s/epoch - 66ms/step
 
    
 
    
 
    Epoch 2/100
 
    
 
    
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    Epoch 13/100
 
    
 
    
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    50/50 - 0s - loss: 0.4181 - 457ms/epoch - 9ms/step
 
    
 
    
 
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    Epoch 18/100
 
    
 
    
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    50/50 - 0s - loss: 0.4162 - 458ms/epoch - 9ms/step
 
    
 
    
 
    Epoch 19/100
 
    
 
    
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    50/50 - 0s - loss: 0.4148 - 463ms/epoch - 9ms/step
 
    
 
    
 
    Epoch 20/100
 
    
 
    
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    50/50 - 0s - loss: 0.3871 - 453ms/epoch - 9ms/step
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    50/50 - 0s - loss: 0.4146 - 460ms/epoch - 9ms/step
 
    
 
    
 
    Epoch 21/100
 
    
 
    
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    50/50 - 0s - loss: 0.4137 - 460ms/epoch - 9ms/step
 
    
 
    
 
    Epoch 22/100
 
    
 
    
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    50/50 - 0s - loss: 0.4128 - 460ms/epoch - 9ms/step
 
    
 
    
 
    Epoch 23/100
 
    
 
    
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    50/50 - 0s - loss: 0.4130 - 462ms/epoch - 9ms/step
 
    
 
    
 
    Epoch 24/100
 
    
 
    
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    50/50 - 0s - loss: 0.4106 - 457ms/epoch - 9ms/step
 
    
 
    
 
    Epoch 25/100
 
    
 
    
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    50/50 - 0s - loss: 0.3815 - 452ms/epoch - 9ms/step
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    50/50 - 0s - loss: 0.4099 - 456ms/epoch - 9ms/step
 
    
 
    
 
    Epoch 26/100
 
    
 
    
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    50/50 - 0s - loss: 0.3782 - 454ms/epoch - 9ms/step
+
    50/50 - 0s - loss: 0.4100 - 463ms/epoch - 9ms/step
 
    
 
    
 
    Epoch 27/100
 
    
 
    
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    50/50 - 0s - loss: 0.3798 - 454ms/epoch - 9ms/step
+
    50/50 - 0s - loss: 0.4086 - 456ms/epoch - 9ms/step
 
    
 
    
 
    Epoch 28/100
 
    
 
    
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    50/50 - 0s - loss: 0.3804 - 456ms/epoch - 9ms/step
+
    50/50 - 0s - loss: 0.4088 - 458ms/epoch - 9ms/step
 
    
 
    
 
    Epoch 29/100
 
    
 
    
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    50/50 - 0s - loss: 0.3812 - 452ms/epoch - 9ms/step
+
    50/50 - 0s - loss: 0.4078 - 462ms/epoch - 9ms/step
 
    
 
    
 
    Epoch 30/100
 
    
 
    
-
    50/50 - 0s - loss: 0.3780 - 454ms/epoch - 9ms/step
+
    50/50 - 0s - loss: 0.4063 - 465ms/epoch - 9ms/step
 
    
 
    
 
    Epoch 31/100
 
    
 
    
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    50/50 - 0s - loss: 0.3800 - 453ms/epoch - 9ms/step
+
    50/50 - 0s - loss: 0.4054 - 455ms/epoch - 9ms/step
 
    
 
    
 
    Epoch 32/100
 
    
 
    
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    50/50 - 0s - loss: 0.3767 - 467ms/epoch - 9ms/step
+
    50/50 - 0s - loss: 0.4054 - 464ms/epoch - 9ms/step
 
    
 
    
 
    Epoch 33/100
 
    
 
    
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    50/50 - 0s - loss: 0.3787 - 493ms/epoch - 10ms/step
+
    50/50 - 0s - loss: 0.4048 - 456ms/epoch - 9ms/step
 
    
 
    
 
    Epoch 34/100
 
    
 
    
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    50/50 - 0s - loss: 0.3758 - 464ms/epoch - 9ms/step
+
    50/50 - 0s - loss: 0.4051 - 454ms/epoch - 9ms/step
 
    
 
    
 
    Epoch 35/100
 
    
 
    
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    50/50 - 0s - loss: 0.3784 - 459ms/epoch - 9ms/step
+
    50/50 - 0s - loss: 0.4027 - 458ms/epoch - 9ms/step
 
    
 
    
 
    Epoch 36/100
 
    
 
    
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    50/50 - 0s - loss: 0.3766 - 461ms/epoch - 9ms/step
+
    50/50 - 0s - loss: 0.4008 - 460ms/epoch - 9ms/step
 
    
 
    
 
    Epoch 37/100
 
    
 
    
-
    50/50 - 0s - loss: 0.3733 - 456ms/epoch - 9ms/step
+
    50/50 - 0s - loss: 0.4011 - 463ms/epoch - 9ms/step
 
    
 
    
 
    Epoch 38/100
 
    
 
    
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    50/50 - 0s - loss: 0.3749 - 455ms/epoch - 9ms/step
+
    50/50 - 0s - loss: 0.4024 - 458ms/epoch - 9ms/step
 
    
 
    
 
    Epoch 39/100
 
    
 
    
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    50/50 - 0s - loss: 0.3756 - 459ms/epoch - 9ms/step
+
    50/50 - 0s - loss: 0.4014 - 455ms/epoch - 9ms/step
 
    
 
    
 
    Epoch 40/100
 
    
 
    
-
    50/50 - 0s - loss: 0.3737 - 458ms/epoch - 9ms/step
+
    50/50 - 0s - loss: 0.4001 - 455ms/epoch - 9ms/step
 
    
 
    
 
    Epoch 41/100
 
    
 
    
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    50/50 - 0s - loss: 0.3743 - 459ms/epoch - 9ms/step
+
    50/50 - 0s - loss: 0.3992 - 459ms/epoch - 9ms/step
 
    
 
    
 
    Epoch 42/100
 
    
 
    
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    50/50 - 0s - loss: 0.3730 - 460ms/epoch - 9ms/step
+
    50/50 - 0s - loss: 0.3990 - 459ms/epoch - 9ms/step
 
    
 
    
 
    Epoch 43/100
 
    
 
    
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    50/50 - 0s - loss: 0.3706 - 457ms/epoch - 9ms/step
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    50/50 - 0s - loss: 0.3990 - 459ms/epoch - 9ms/step
 
    
 
    
 
    Epoch 44/100
 
    
 
    
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+
    50/50 - 0s - loss: 0.3975 - 453ms/epoch - 9ms/step
 
    
 
    
 
    Epoch 45/100
 
    
 
    
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    50/50 - 0s - loss: 0.3716 - 456ms/epoch - 9ms/step
+
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    Epoch 46/100
 
    
 
    
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    50/50 - 0s - loss: 0.3713 - 459ms/epoch - 9ms/step
+
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    Epoch 47/100
 
    
 
    
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    50/50 - 0s - loss: 0.3705 - 458ms/epoch - 9ms/step
+
    50/50 - 0s - loss: 0.3954 - 458ms/epoch - 9ms/step
 
    
 
    
 
    Epoch 48/100
 
    
 
    
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    50/50 - 0s - loss: 0.3703 - 460ms/epoch - 9ms/step
+
    50/50 - 0s - loss: 0.3931 - 462ms/epoch - 9ms/step
 
    
 
    
 
    Epoch 49/100
 
    
 
    
-
    50/50 - 0s - loss: 0.3701 - 459ms/epoch - 9ms/step
+
    50/50 - 0s - loss: 0.3946 - 461ms/epoch - 9ms/step
 
    
 
    
 
    Epoch 50/100
 
    
 
    
-
    50/50 - 0s - loss: 0.3676 - 470ms/epoch - 9ms/step
+
    50/50 - 0s - loss: 0.3940 - 458ms/epoch - 9ms/step
 
    
 
    
 
    Epoch 51/100
 
    
 
    
-
    50/50 - 0s - loss: 0.3679 - 464ms/epoch - 9ms/step
+
    50/50 - 0s - loss: 0.3935 - 457ms/epoch - 9ms/step
 
    
 
    
 
    Epoch 52/100
 
    
 
    
-
    50/50 - 0s - loss: 0.3689 - 460ms/epoch - 9ms/step
+
    50/50 - 0s - loss: 0.3932 - 458ms/epoch - 9ms/step
 
    
 
    
 
    Epoch 53/100
diff --git a/doc/LectureNotes/_build/html/chapter2.html b/doc/LectureNotes/_build/html/chapter2.html
index 8baccf5d0..c5833b87a 100644
--- a/doc/LectureNotes/_build/html/chapter2.html
+++ b/doc/LectureNotes/_build/html/chapter2.html
@@ -333,6 +333,16 @@ const thebe_selector_output = ".output, .cell_output"
    Week 42 Constructing a Neural Network code with introduction to Tensor flow
   
  
+ 
  • 
+  
+   Exercises weeks 43 and 44
+  
+ 
    • 
+ 
  • 
+  
+   Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations
+  
+ 
    • 
 
 
    
  
@@ -1315,10 +1325,10 @@ covariance matrix through the np.linalg.eig() function.
    
 
    
 
    
 
    
-
    0.12765651865754318
-4.348676117830458
-[[0.98073929 2.88442538]
- [2.88442538 9.24128917]]
+
    0.023888460698069384
+4.161573669199933
+[[0.708589   2.01323615]
+ [2.01323615 6.75406265]]
 
    
 
    
 
    
@@ -1355,10 +1365,10 @@ a more brute force way. Here we scale the mean values for each column of the des
 
    
 
    
 
    
-
    0.08652153831327969
-1.7893215781870513
-[[1.         0.70344416]
- [0.70344416 1.        ]]
+
    0.08737007811453563
+1.792898603630095
+[[1.         0.65673455]
+ [0.65673455 1.        ]]
 
    
 
    
 
    
@@ -1388,30 +1398,30 @@ this matrix we easily see that it is a positive definite matrix.
    
 
    
 
    
 
    
-
    [[ 1.71727268  5.22388434]
- [ 0.31027702  0.17469167]
- [-0.26149831 -0.93082933]
- [ 0.04107874  1.47244548]
- [-2.10812381 -5.28818554]
- [-1.62910047 -4.07706814]
- [ 0.92136836  2.27309401]
- [-0.3175938  -1.42457498]
- [ 0.68037392  0.16481217]
- [ 0.64594566  2.41173033]]
+
    [[ 0.1036544   0.17432695]
+ [ 0.51758232  1.25700419]
+ [-1.37236385 -4.22805937]
+ [-0.46766277 -1.52501047]
+ [ 0.99175336  3.59187177]
+ [-2.34718587 -5.3512747 ]
+ [ 1.94980712  6.99450177]
+ [ 0.24849282 -0.56424167]
+ [-0.35408251 -2.3930301 ]
+ [ 0.73000497  2.04391163]]
           0         1
-0  1.717273  5.223884
-1  0.310277  0.174692
-2 -0.261498 -0.930829
-3  0.041079  1.472445
-4 -2.108124 -5.288186
-5 -1.629100 -4.077068
-6  0.921368  2.273094
-7 -0.317594 -1.424575
-8  0.680374  0.164812
-9  0.645946  2.411730
+0  0.103654  0.174327
+1  0.517582  1.257004
+2 -1.372364 -4.228059
+3 -0.467663 -1.525010
+4  0.991753  3.591872
+5 -2.347186 -5.351275
+6  1.949807  6.994502
+7  0.248493 -0.564242
+8 -0.354083 -2.393030
+9  0.730005  2.043912
           0         1
-0  1.000000  0.962653
-1  0.962653  1.000000
+0  1.000000  0.966337
+1  0.966337  1.000000
 
    
 
    
 
    
@@ -1468,37 +1478,37 @@ this matrix we easily see that it is a positive definite matrix.
    
 
    
 
         0         1         2         3         4         5         6         7   \
 0   0.0  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   
-1   0.0  0.082246  0.081621  0.082225  0.081617  0.081120  0.073421  0.072973   
-2   0.0  0.081621  0.081679  0.081960  0.081804  0.081742  0.073498  0.073387   
-3   0.0  0.082225  0.081960  0.087271  0.086900  0.086636  0.081057  0.080755   
-4   0.0  0.081617  0.081804  0.086900  0.086868  0.086932  0.080935  0.080903   
-5   0.0  0.081120  0.081742  0.086636  0.086932  0.087311  0.080906  0.081136   
-6   0.0  0.073421  0.073498  0.081057  0.080935  0.080906  0.077455  0.077330   
-7   0.0  0.072973  0.073387  0.080755  0.080903  0.081136  0.077330  0.077429   
-8   0.0  0.072637  0.073376  0.080571  0.080980  0.081466  0.077313  0.077630   
-9   0.0  0.072410  0.073465  0.080502  0.081164  0.081896  0.077403  0.077931   
-10  0.0  0.064640  0.064948  0.073406  0.073471  0.073618  0.071662  0.071685   
-11  0.0  0.064320  0.064896  0.073187  0.073476  0.073840  0.071579  0.071792   
-12  0.0  0.064101  0.064938  0.073080  0.073586  0.074161  0.071601  0.072000   
-13  0.0  0.063980  0.065069  0.073079  0.073797  0.074577  0.071726  0.072305   
-14  0.0  0.063953  0.065289  0.073184  0.074108  0.075089  0.071951  0.072707   
+1   0.0  0.084489  0.079202  0.083269  0.079700  0.076331  0.074355  0.071456   
+2   0.0  0.079202  0.075352  0.079849  0.077042  0.074328  0.072527  0.070086   
+3   0.0  0.083269  0.079849  0.087761  0.084946  0.082203  0.081779  0.079170   
+4   0.0  0.079700  0.077042  0.084946  0.082590  0.080248  0.079820  0.077517   
+5   0.0  0.076331  0.074328  0.082203  0.080248  0.078258  0.077833  0.075804   
+6   0.0  0.074355  0.072527  0.081779  0.079820  0.077833  0.078423  0.076337   
+7   0.0  0.071456  0.070086  0.079170  0.077517  0.075804  0.076337  0.074477   
+8   0.0  0.068743  0.067765  0.076678  0.075294  0.073824  0.074307  0.072650   
+9   0.0  0.066200  0.065559  0.074301  0.073154  0.071901  0.072338  0.070865   
+10  0.0  0.065631  0.064814  0.074306  0.072976  0.071564  0.072718  0.071080   
+11  0.0  0.063225  0.062696  0.071960  0.070845  0.069629  0.070705  0.069239   
+12  0.0  0.060971  0.060691  0.069733  0.068809  0.067769  0.068771  0.067462   
+13  0.0  0.058856  0.058793  0.067619  0.066865  0.065984  0.066919  0.065753   
+14  0.0  0.056870  0.056996  0.065613  0.065012  0.064275  0.065147  0.064113   
 
           8         9         10        11        12        13        14  
 0   0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  
-1   0.072637  0.072410  0.064640  0.064320  0.064101  0.063980  0.063953  
-2   0.073376  0.073465  0.064948  0.064896  0.064938  0.065069  0.065289  
-3   0.080571  0.080502  0.073406  0.073187  0.073080  0.073079  0.073184  
-4   0.080980  0.081164  0.073471  0.073476  0.073586  0.073797  0.074108  
-5   0.081466  0.081896  0.073618  0.073840  0.074161  0.074577  0.075089  
-6   0.077313  0.077403  0.071662  0.071579  0.071601  0.071726  0.071951  
-7   0.077630  0.077931  0.071685  0.071792  0.072000  0.072305  0.072707  
-8   0.078041  0.078548  0.071805  0.072098  0.072486  0.072967  0.073541  
-9   0.078548  0.079255  0.072022  0.072495  0.073059  0.073712  0.074455  
-10  0.071805  0.072022  0.067420  0.067457  0.067591  0.067820  0.068141  
-11  0.072098  0.072495  0.067457  0.067660  0.067955  0.068340  0.068815  
-12  0.072486  0.073059  0.067591  0.067955  0.068407  0.068945  0.069570  
-13  0.072967  0.073712  0.067820  0.068340  0.068945  0.069634  0.070406  
-14  0.073541  0.074455  0.068141  0.068815  0.069570  0.070406  0.071323  
+1   0.068743  0.066200  0.065631  0.063225  0.060971  0.058856  0.056870  
+2   0.067765  0.065559  0.064814  0.062696  0.060691  0.058793  0.056996  
+3   0.076678  0.074301  0.074306  0.071960  0.069733  0.067619  0.065613  
+4   0.075294  0.073154  0.072976  0.070845  0.068809  0.066865  0.065012  
+5   0.073824  0.071901  0.071564  0.069629  0.067769  0.065984  0.064275  
+6   0.074307  0.072338  0.072718  0.070705  0.068771  0.066919  0.065147  
+7   0.072650  0.070865  0.071080  0.069239  0.067462  0.065753  0.064113  
+8   0.071008  0.069391  0.069456  0.067774  0.066143  0.064568  0.063051  
+9   0.069391  0.067929  0.067859  0.066323  0.064827  0.063378  0.061977  
+10  0.069456  0.067859  0.068437  0.066752  0.065119  0.063542  0.062023  
+11  0.067774  0.066323  0.066752  0.065207  0.063705  0.062250  0.060845  
+12  0.066143  0.064827  0.065119  0.063705  0.062325  0.060983  0.059685  
+13  0.064568  0.063378  0.063542  0.062250  0.060983  0.059749  0.058550  
+14  0.063051  0.061977  0.062023  0.060845  0.059685  0.058550  0.057446  
 
    
 
    
 
    
diff --git a/doc/LectureNotes/_build/html/chapter3.html b/doc/LectureNotes/_build/html/chapter3.html
index 7253cd0f1..33b58b349 100644
--- a/doc/LectureNotes/_build/html/chapter3.html
+++ b/doc/LectureNotes/_build/html/chapter3.html
@@ -333,6 +333,16 @@ const thebe_selector_output = ".output, .cell_output"
    Week 42 Constructing a Neural Network code with introduction to Tensor flow
   
  
+ 
  • 
+  
+   Exercises weeks 43 and 44
+  
+ 
    • 
+ 
  • 
+  
+   Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations
+  
+ 
    • 
 
 
    
  
@@ -869,10 +879,10 @@ number \(i\) is left out. Usin
 
    
 
    
 
    
-
    Runtime: 0.136236 sec
+
    Runtime: 0.134565 sec
 Jackknife Statistics :
 original           bias      std. error
-  99.979         99.969         0.14845
+   100.2         100.19        0.146591
 
    
 
    
 
    
@@ -1091,7 +1101,7 @@ theorem.
    
 
    
 
    Bootstrap Statistics :
 original           bias      std. error
- 99.8342  14.8306        99.8351         0.14857
+ 99.9919  15.0954        99.9924        0.150989
 
    
 
    
 
    
@@ -1313,14 +1323,14 @@ Error: 0.06844519414009445
 Bias^2: 0.06453579006728322
 Var: 0.003909404072811221
 0.06844519414009445 >= 0.06453579006728322 + 0.003909404072811221 = 0.06844519414009444
-Polynomial degree: 5
+
    
+
    
+
    Polynomial degree: 5
 Error: 0.05227921801205679
 Bias^2: 0.04818727730430286
 Var: 0.004091940707753925
 0.05227921801205679 >= 0.04818727730430286 + 0.004091940707753925 = 0.05227921801205679
-
    
-
    
-
    Polynomial degree: 6
+Polynomial degree: 6
 Error: 0.03781367141738902
 Bias^2: 0.03365768507152769
 Var: 0.0041559863458613296
@@ -1640,12 +1650,12 @@ Mean squared error on test data: 1371.99051150
 Degree of polynomial:  20
 Mean squared error on training data: 0.00137818
 Mean squared error on test data: 1887.86252988
-Degree of polynomial:  21
-Mean squared error on training data: 0.00118508
-Mean squared error on test data: 14859.69908626
 
    
 
    
-
    Degree of polynomial:  22
+
    Degree of polynomial:  21
+Mean squared error on training data: 0.00118508
+Mean squared error on test data: 14859.69908626
+Degree of polynomial:  22
 Mean squared error on training data: 0.00092647
 Mean squared error on test data: 876.51191552
 Degree of polynomial:  23
@@ -1660,12 +1670,12 @@ Mean squared error on test data: 128664.31650694
 Degree of polynomial:  26
 Mean squared error on training data: 0.00076905
 Mean squared error on test data: 19003.94822514
-Degree of polynomial:  27
-Mean squared error on training data: 0.00068946
-Mean squared error on test data: 2379.66219404
 
    
 
    
-
    Degree of polynomial:  28
+
    Degree of polynomial:  27
+Mean squared error on training data: 0.00068946
+Mean squared error on test data: 2379.66219404
+Degree of polynomial:  28
 Mean squared error on training data: 0.00062595
 Mean squared error on test data: 4082.19983530
 Degree of polynomial:  29
@@ -1673,9 +1683,9 @@ Mean squared error on training data: 0.00060705
 Mean squared error on test data: 3250.17647619
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31624/626635268.py:73: RuntimeWarning: divide by zero encountered in log10
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10962/626635268.py:73: RuntimeWarning: divide by zero encountered in log10
   plt.plot(polynomial, np.log10(trainingerror), label='Training Error')
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31624/626635268.py:74: RuntimeWarning: divide by zero encountered in log10
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10962/626635268.py:74: RuntimeWarning: divide by zero encountered in log10
   plt.plot(polynomial, np.log10(testerror), label='Test Error')
 
    
 
    
@@ -1909,7 +1919,7 @@ cross-validation (LOOCV).
    
 
    
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31624/3817475779.py:63: RuntimeWarning: divide by zero encountered in log10
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10962/3817475779.py:63: RuntimeWarning: divide by zero encountered in log10
   plt.plot(polynomial, np.log10(estimated_mse_sklearn), label='Test Error')
 
    
 
    
@@ -2798,9 +2808,9 @@ linear system as an equation would reduce this down to
 
    
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31624/4162706317.py:6: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10962/4162706317.py:6: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.
   cb = fig.colorbar(im)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31624/4162706317.py:7: UserWarning: FixedFormatter should only be used together with FixedLocator
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10962/4162706317.py:7: UserWarning: FixedFormatter should only be used together with FixedLocator
   cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)
 
    
 
    
@@ -2944,9 +2954,9 @@ with the form utilized in linear regression, viz.
    
 
    
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31624/3777801602.py:6: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10962/3777801602.py:6: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.
   cb = fig.colorbar(im)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31624/3777801602.py:7: UserWarning: FixedFormatter should only be used together with FixedLocator
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10962/3777801602.py:7: UserWarning: FixedFormatter should only be used together with FixedLocator
   cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)
 
    
 
    
@@ -2986,9 +2996,9 @@ cost function is given by
    
 
    
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31624/438060758.py:9: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10962/438060758.py:9: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.
   cb = fig.colorbar(im)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31624/438060758.py:10: UserWarning: FixedFormatter should only be used together with FixedLocator
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10962/438060758.py:10: UserWarning: FixedFormatter should only be used together with FixedLocator
   cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)
 
    
 
    
@@ -3023,9 +3033,9 @@ cost function is given by
    
 
    
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31624/3544313922.py:8: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10962/3544313922.py:8: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.
   cb = fig.colorbar(im)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31624/3544313922.py:9: UserWarning: FixedFormatter should only be used together with FixedLocator
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10962/3544313922.py:9: UserWarning: FixedFormatter should only be used together with FixedLocator
   cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)
 
    
 
    
@@ -3078,43 +3088,43 @@ constant as opposed to ridge and OLS. We get a sparse solution with
 
    
 
    
 
    
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    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:647: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.924e+00, tolerance: 1.797e+00
   model = cd_fast.enet_coordinate_descent(
 
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@@ -3261,9 +3271,9 @@ which polynomial fits the data best.
    
 
    
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31624/3980313467.py:9: MatplotlibDeprecationWarning: Calling gca() with keyword arguments was deprecated in Matplotlib 3.4. Starting two minor releases later, gca() will take no keyword arguments. The gca() function should only be used to get the current axes, or if no axes exist, create new axes with default keyword arguments. To create a new axes with non-default arguments, use plt.axes() or plt.subplot().
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10962/3980313467.py:9: MatplotlibDeprecationWarning: Calling gca() with keyword arguments was deprecated in Matplotlib 3.4. Starting two minor releases later, gca() will take no keyword arguments. The gca() function should only be used to get the current axes, or if no axes exist, create new axes with default keyword arguments. To create a new axes with non-default arguments, use plt.axes() or plt.subplot().
   ax = fig.gca(projection='3d')
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31624/3980313467.py:37: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10962/3980313467.py:37: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.
   fig.colorbar(surf, shrink=0.5, aspect=5)
 
    
 
    
diff --git a/doc/LectureNotes/_build/html/chapter4.html b/doc/LectureNotes/_build/html/chapter4.html
index dbd68ef30..140df44e6 100644
--- a/doc/LectureNotes/_build/html/chapter4.html
+++ b/doc/LectureNotes/_build/html/chapter4.html
@@ -333,6 +333,16 @@ const thebe_selector_output = ".output, .cell_output"
    Week 42 Constructing a Neural Network code with introduction to Tensor flow
   
  
+ 
  • 
+  
+   Exercises weeks 43 and 44
+  
+ 
    • 
+ 
  • 
+  
+   Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations
+  
+ 
    • 
 
 
    
  
diff --git a/doc/LectureNotes/_build/html/chapter5.html b/doc/LectureNotes/_build/html/chapter5.html
index 7ba11d262..222df26c1 100644
--- a/doc/LectureNotes/_build/html/chapter5.html
+++ b/doc/LectureNotes/_build/html/chapter5.html
@@ -333,6 +333,16 @@ const thebe_selector_output = ".output, .cell_output"
    Week 42 Constructing a Neural Network code with introduction to Tensor flow
   
  
+ 
  • 
+  
+   Exercises weeks 43 and 44
+  
+ 
    • 
+ 
  • 
+  
+   Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations
+  
+ 
    • 
 
 
    
  
diff --git a/doc/LectureNotes/_build/html/chapter6.html b/doc/LectureNotes/_build/html/chapter6.html
index a928e6651..ca0987d0a 100644
--- a/doc/LectureNotes/_build/html/chapter6.html
+++ b/doc/LectureNotes/_build/html/chapter6.html
@@ -333,6 +333,16 @@ const thebe_selector_output = ".output, .cell_output"
    Week 42 Constructing a Neural Network code with introduction to Tensor flow
   
  
+ 
  • 
+  
+   Exercises weeks 43 and 44
+  
+ 
    • 
+ 
  • 
+  
+   Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations
+  
+ 
    • 
 
 
    
  
@@ -797,9 +807,9 @@ predicting the target features of query instances is as follows:
    
 
    
 
    
 
    2nd degree coefficients:
-zero power:  -0.2774877574815404
-first power:  0.11112589053037751
-second power:  -0.00033136014047192484
+zero power:  2.731441119315968
+first power:  -0.07208896238192342
+second power:  0.0005051756404139333
 
    
 
    
 _images/chapter6_1_1.png
diff --git a/doc/LectureNotes/_build/html/chapter7.html b/doc/LectureNotes/_build/html/chapter7.html
index 19c6f4426..3f0c4ee90 100644
--- a/doc/LectureNotes/_build/html/chapter7.html
+++ b/doc/LectureNotes/_build/html/chapter7.html
@@ -333,6 +333,16 @@ const thebe_selector_output = ".output, .cell_output"
    Week 42 Constructing a Neural Network code with introduction to Tensor flow
   
  
+ 
  • 
+  
+   Exercises weeks 43 and 44
+  
+ 
    • 
+ 
  • 
+  
+   Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations
+  
+ 
    • 
 
 
    
  
diff --git a/doc/LectureNotes/_build/html/chapter8.html b/doc/LectureNotes/_build/html/chapter8.html
index 88ecb4d12..20466c93c 100644
--- a/doc/LectureNotes/_build/html/chapter8.html
+++ b/doc/LectureNotes/_build/html/chapter8.html
@@ -333,6 +333,16 @@ const thebe_selector_output = ".output, .cell_output"
    Week 42 Constructing a Neural Network code with introduction to Tensor flow
   
  
+ 
  • 
+  
+   Exercises weeks 43 and 44
+  
+ 
    • 
+ 
  • 
+  
+   Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations
+  
+ 
    • 
 
 
    
  
@@ -751,10 +761,10 @@ covariance matrix through the np.linalg.eig() function.
    
 
    
 
    
 
    
-
    -0.10776220958055382
-3.743189104728408
-[[0.82379443 2.29894362]
- [2.29894362 7.75174305]]
+
    0.04570437990371566
+4.420442688206847
+[[ 1.01597952  3.06059304]
+ [ 3.06059304 10.1387933 ]]
 
    
 
    
 
    
@@ -794,10 +804,10 @@ a more brute force way. Here we scale the mean values for each column of the des
 
    
 
    
 
    
-
    0.0704374681593734
-1.3273472571412799
-[[1.         0.58076367]
- [0.58076367 1.        ]]
+
    0.07663067400487368
+1.9423652864980914
+[[1.         0.72782592]
+ [0.72782592 1.        ]]
 
    
 
    
 
    
@@ -826,30 +836,30 @@ this matrix we easily see that it is a positive definite matrix.
    
 
    
 
    
 
    
-
    [[-0.92200223 -1.78838813]
- [-0.90854751 -2.66047048]
- [ 0.83618601  2.91748202]
- [-0.88821402 -4.10035098]
- [ 0.44781662  2.48685204]
- [ 1.20493234  2.32729105]
- [ 1.02509184  2.42265837]
- [-0.84210141 -3.82012236]
- [-0.01031541  1.40111899]
- [ 0.05715377  0.81392948]]
+
    [[-0.51761523 -1.42486342]
+ [ 1.91816586  6.87585634]
+ [-0.34694145 -1.09920915]
+ [ 0.31244861  1.08282867]
+ [ 1.12441319  3.24411906]
+ [-0.51892347 -1.08417181]
+ [-0.54509921 -2.1148557 ]
+ [ 0.26084008  0.60846694]
+ [ 0.01029574  0.21049575]
+ [-1.69758412 -6.29866668]]
           0         1
-0 -0.922002 -1.788388
-1 -0.908548 -2.660470
-2  0.836186  2.917482
-3 -0.888214 -4.100351
-4  0.447817  2.486852
-5  1.204932  2.327291
-6  1.025092  2.422658
-7 -0.842101 -3.820122
-8 -0.010315  1.401119
-9  0.057154  0.813929
+0 -0.517615 -1.424863
+1  1.918166  6.875856
+2 -0.346941 -1.099209
+3  0.312449  1.082829
+4  1.124413  3.244119
+5 -0.518923 -1.084172
+6 -0.545099 -2.114856
+7  0.260840  0.608467
+8  0.010296  0.210496
+9 -1.697584 -6.298667
           0         1
-0  1.000000  0.920619
-1  0.920619  1.000000
+0  1.000000  0.993148
+1  0.993148  1.000000
 
    
 
    
 
    
@@ -906,37 +916,37 @@ this matrix we easily see that it is a positive definite matrix.
    
 
    
 
         0         1         2         3         4         5         6         7   \
 0   0.0  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   
-1   0.0  0.076527  0.079731  0.075684  0.075421  0.075030  0.066467  0.065808   
-2   0.0  0.079731  0.084207  0.080233  0.080607  0.080750  0.071252  0.070964   
-3   0.0  0.075684  0.080233  0.079381  0.079948  0.080284  0.072483  0.072285   
-4   0.0  0.075421  0.080607  0.079948  0.080953  0.081677  0.073541  0.073640   
-5   0.0  0.075030  0.080750  0.080284  0.081677  0.082746  0.074340  0.074708   
-6   0.0  0.066467  0.071252  0.072483  0.073541  0.074340  0.068082  0.068257   
-7   0.0  0.065808  0.070964  0.072285  0.073640  0.074708  0.068257  0.068650   
-8   0.0  0.065215  0.070694  0.072098  0.073720  0.075030  0.068406  0.068997   
-9   0.0  0.064696  0.070461  0.071942  0.073802  0.075331  0.068551  0.069320   
-10  0.0  0.057462  0.062071  0.064444  0.065735  0.066768  0.061869  0.062273   
-11  0.0  0.056898  0.061747  0.064145  0.065645  0.066870  0.061826  0.062390   
-12  0.0  0.056418  0.061484  0.063905  0.065593  0.066992  0.061813  0.062523   
-13  0.0  0.056019  0.061281  0.063722  0.065582  0.067139  0.061833  0.062675   
-14  0.0  0.055697  0.061138  0.063597  0.065614  0.067315  0.061888  0.062852   
+1   0.0  0.079243  0.085251  0.080541  0.083416  0.086394  0.074010  0.075867   
+2   0.0  0.085251  0.093570  0.084995  0.089008  0.093218  0.076658  0.079150   
+3   0.0  0.080541  0.084995  0.088240  0.090361  0.092452  0.084878  0.086337   
+4   0.0  0.083416  0.089008  0.090361  0.093080  0.095821  0.086090  0.087899   
+5   0.0  0.086394  0.093218  0.092452  0.095821  0.099275  0.087175  0.089365   
+6   0.0  0.074010  0.076658  0.084878  0.086090  0.087175  0.084042  0.084968   
+7   0.0  0.075867  0.079150  0.086337  0.087899  0.089365  0.084968  0.086108   
+8   0.0  0.077847  0.081832  0.087845  0.089793  0.091685  0.085877  0.087254   
+9   0.0  0.079975  0.084740  0.089414  0.091796  0.094163  0.086774  0.088416   
+10  0.0  0.067272  0.068624  0.079437  0.079971  0.080322  0.080193  0.080702   
+11  0.0  0.068609  0.070338  0.080572  0.081322  0.081908  0.081000  0.081647   
+12  0.0  0.070043  0.072194  0.081762  0.082754  0.083604  0.081821  0.082621   
+13  0.0  0.071587  0.074211  0.083015  0.084278  0.085427  0.082657  0.083630   
+14  0.0  0.073256  0.076413  0.084340  0.085908  0.087393  0.083511  0.084678   
 
           8         9         10        11        12        13        14  
 0   0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  
-1   0.065215  0.064696  0.057462  0.056898  0.056418  0.056019  0.055697  
-2   0.070694  0.070461  0.062071  0.061747  0.061484  0.061281  0.061138  
-3   0.072098  0.071942  0.064444  0.064145  0.063905  0.063722  0.063597  
-4   0.073720  0.073802  0.065735  0.065645  0.065593  0.065582  0.065614  
-5   0.075030  0.075331  0.066768  0.066870  0.066992  0.067139  0.067315  
-6   0.068406  0.068551  0.061869  0.061826  0.061813  0.061833  0.061888  
-7   0.068997  0.069320  0.062273  0.062390  0.062523  0.062675  0.062852  
-8   0.069522  0.070009  0.062631  0.062894  0.063159  0.063434  0.063723  
-9   0.070009  0.070645  0.062963  0.063359  0.063747  0.064134  0.064527  
-10  0.062631  0.062963  0.057231  0.057361  0.057502  0.057657  0.057831  
-11  0.062894  0.063359  0.057361  0.057613  0.057864  0.058121  0.058388  
-12  0.063159  0.063747  0.057502  0.057864  0.058216  0.058567  0.058921  
-13  0.063434  0.064134  0.057657  0.058121  0.058567  0.059004  0.059439  
-14  0.063723  0.064527  0.057831  0.058388  0.058921  0.059439  0.059949  
+1   0.077847  0.079975  0.067272  0.068609  0.070043  0.071587  0.073256  
+2   0.081832  0.084740  0.068624  0.070338  0.072194  0.074211  0.076413  
+3   0.087845  0.089414  0.079437  0.080572  0.081762  0.083015  0.084340  
+4   0.089793  0.091796  0.079971  0.081322  0.082754  0.084278  0.085908  
+5   0.091685  0.094163  0.080322  0.081908  0.083604  0.085427  0.087393  
+6   0.085877  0.086774  0.080193  0.081000  0.081821  0.082657  0.083511  
+7   0.087254  0.088416  0.080702  0.081647  0.082621  0.083630  0.084678  
+8   0.088665  0.090123  0.081150  0.082248  0.083393  0.084594  0.085858  
+9   0.090123  0.091913  0.081538  0.082805  0.084141  0.085557  0.087063  
+10  0.081150  0.081538  0.077571  0.078106  0.078624  0.079125  0.079606  
+11  0.082248  0.082805  0.078106  0.078732  0.079353  0.079969  0.080577  
+12  0.083393  0.084141  0.078624  0.079353  0.080089  0.080832  0.081584  
+13  0.084594  0.085557  0.079125  0.079969  0.080832  0.081718  0.082632  
+14  0.085858  0.087063  0.079606  0.080577  0.081584  0.082632  0.083726  
 
    
 
    
 
    
@@ -1125,10 +1135,10 @@ We can write our own code or simply use either the functionaly of numpy<
 
    
 
    
 
              0         1
-0  3.949162  1.987722
-1  1.987722  2.004480
-[[3.94916237 1.98772232]
- [1.98772232 2.00447992]]
+0  3.987648  2.034723
+1  2.034723  2.038727
+[[3.98764765 2.03472297]
+ [2.03472297 2.03872663]]
 
    
 
    
 
    
@@ -1155,8 +1165,8 @@ Our own code here is not very elegant and asks for obvious improvements. It is t
 
    
 
    
 
    Centered covariance using own code
-[[3.94916237 1.98772232]
- [1.98772232 2.00447992]]
+[[3.98764765 2.03472297]
+ [2.03472297 2.03872663]]
 
    
 
    
 _images/chapter8_65_1.png
@@ -1216,16 +1226,16 @@ questions.
    
 
    
 
    
 
    Eigenvalues of Covariance matrix
-5.189621963782685
-0.7640203256838339
+5.269217029290255
+0.7571572558830478
 First eigenvector
-[0.84835621 0.52942586]
+[0.84614892 0.53294653]
 Second eigenvector
-[-0.52942586  0.84835621]
+[-0.53294653  0.84614892]
 
    
 
    
 
    Eigenvector of largest eigenvalue
-[0.84835621 0.52942586]
+[-0.84614892 -0.53294653]
 
    
 
    
 
    
diff --git a/doc/LectureNotes/_build/html/chapter9.html b/doc/LectureNotes/_build/html/chapter9.html
index 06ab0b886..09852dd6e 100644
--- a/doc/LectureNotes/_build/html/chapter9.html
+++ b/doc/LectureNotes/_build/html/chapter9.html
@@ -333,6 +333,16 @@ const thebe_selector_output = ".output, .cell_output"
    Week 42 Constructing a Neural Network code with introduction to Tensor flow
   
  
+ 
  • 
+  
+   Exercises weeks 43 and 44
+  
+ 
    • 
+ 
  • 
+  
+   Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations
+  
+ 
    • 
 
 
    
  
diff --git a/doc/LectureNotes/_build/html/chapteroptimization.html b/doc/LectureNotes/_build/html/chapteroptimization.html
index 4dca7d07f..7a957deb6 100644
--- a/doc/LectureNotes/_build/html/chapteroptimization.html
+++ b/doc/LectureNotes/_build/html/chapteroptimization.html
@@ -333,6 +333,16 @@ const thebe_selector_output = ".output, .cell_output"
    Week 42 Constructing a Neural Network code with introduction to Tensor flow
   
  
+ 
  • 
+  
+   Exercises weeks 43 and 44
+  
+ 
    • 
+ 
  • 
+  
+   Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations
+  
+ 
    • 
 
 
    
  
@@ -1047,11 +1057,11 @@ which equals
    
 
    
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31672/483257001.py:18: MatplotlibDeprecationWarning: Calling gca() with keyword arguments was deprecated in Matplotlib 3.4. Starting two minor releases later, gca() will take no keyword arguments. The gca() function should only be used to get the current axes, or if no axes exist, create new axes with default keyword arguments. To create a new axes with non-default arguments, use plt.axes() or plt.subplot().
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11016/483257001.py:18: MatplotlibDeprecationWarning: Calling gca() with keyword arguments was deprecated in Matplotlib 3.4. Starting two minor releases later, gca() will take no keyword arguments. The gca() function should only be used to get the current axes, or if no axes exist, create new axes with default keyword arguments. To create a new axes with non-default arguments, use plt.axes() or plt.subplot().
   ax = fig.gca(projection="3d")
 
    
 
    
-
    <mpl_toolkits.mplot3d.art3d.Poly3DCollection at 0x1183f2640>
+
    <mpl_toolkits.mplot3d.art3d.Poly3DCollection at 0x122d2e790>
 
    
 
    
 _images/chapteroptimization_61_2.png
@@ -1109,7 +1119,7 @@ which equals
    
 
    
 
    
 
    
-
    [<matplotlib.lines.Line2D at 0x118c9b1c0>]
+
    [<matplotlib.lines.Line2D at 0x12334c310>]
 
    
 
    
 _images/chapteroptimization_69_1.png
@@ -1366,11 +1376,11 @@ when \(||\nabla_\beta C(\beta_k) || \
 
    
 
    
 
    
-
    [0.29633889 4.15119514]
-[[3.90793019]
- [3.18761375]]
-[[3.90793019]
- [3.18761375]]
+
    [0.27637358 4.69167569]
+[[4.08692465]
+ [2.84462849]]
+[[4.08692465]
+ [2.84462849]]
 
    
 
    
 _images/chapteroptimization_123_1.png
@@ -1399,9 +1409,9 @@ when \(||\nabla_\beta C(\beta_k) || \
 
    
 
    
 
    
-
    [[3.96783837]
- [3.23305112]]
-[3.95982273] [3.21682143]
+
    [[4.17086577]
+ [2.92317667]]
+[4.14538257] [2.90670236]
 
    
 
    
 
    
@@ -1472,10 +1482,10 @@ C_{\text{ridge}}(\beta) = \frac{1}{n}||X\beta -\mathbf{y}||^2 + \lambda ||\beta|
 
    
 
    
 
    
-
    [[3.91619855]
- [3.20101684]]
-[[3.85813693]
- [3.24679418]]
+
    [[4.11027723]
+ [2.92805329]]
+[[4.04931542]
+ [2.97504526]]
 
    
 
    
 _images/chapteroptimization_132_1.png
@@ -1725,15 +1735,15 @@ function.
    
 
    
 
    
 
    Own inversion
-[[3.70224083]
- [3.16389131]]
-Eigenvalues of Hessian Matrix:[0.29022057 4.66510547]
+[[3.22532324]
+ [3.44210664]]
+Eigenvalues of Hessian Matrix:[0.30012384 4.62464344]
 theta from own gd
-[[3.70224083]
- [3.16389131]]
+[[3.22532324]
+ [3.44210664]]
 theta from own sdg
-[[3.67541155]
- [3.1532465 ]]
+[[3.17736035]
+ [3.48289037]]
 
    
 
    
 _images/chapteroptimization_148_1.png
diff --git a/doc/LectureNotes/_build/html/clustering.html b/doc/LectureNotes/_build/html/clustering.html
index d3392881a..508961051 100644
--- a/doc/LectureNotes/_build/html/clustering.html
+++ b/doc/LectureNotes/_build/html/clustering.html
@@ -333,6 +333,16 @@ const thebe_selector_output = ".output, .cell_output"
    Week 42 Constructing a Neural Network code with introduction to Tensor flow
   
  
+ 
  • 
+  
+   Exercises weeks 43 and 44
+  
+ 
    • 
+ 
  • 
+  
+   Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations
+  
+ 
    • 
 
 
    
  
diff --git a/doc/LectureNotes/_build/html/exercisesweek34.html b/doc/LectureNotes/_build/html/exercisesweek34.html
index 51bd0a4e7..0da71c964 100644
--- a/doc/LectureNotes/_build/html/exercisesweek34.html
+++ b/doc/LectureNotes/_build/html/exercisesweek34.html
@@ -333,6 +333,16 @@ const thebe_selector_output = ".output, .cell_output"
    Week 42 Constructing a Neural Network code with introduction to Tensor flow
   
  
+ 
  • 
+  
+   Exercises weeks 43 and 44
+  
+ 
    • 
+ 
  • 
+  
+   Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations
+  
+ 
    • 
 
 
    
  
diff --git a/doc/LectureNotes/_build/html/exercisesweek35.html b/doc/LectureNotes/_build/html/exercisesweek35.html
index ae0dcb919..f3eed704e 100644
--- a/doc/LectureNotes/_build/html/exercisesweek35.html
+++ b/doc/LectureNotes/_build/html/exercisesweek35.html
@@ -333,6 +333,16 @@ const thebe_selector_output = ".output, .cell_output"
    Week 42 Constructing a Neural Network code with introduction to Tensor flow
   
  
+ 
  • 
+  
+   Exercises weeks 43 and 44
+  
+ 
    • 
+ 
  • 
+  
+   Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations
+  
+ 
    • 
 
 
    
  
diff --git a/doc/LectureNotes/_build/html/exercisesweek36.html b/doc/LectureNotes/_build/html/exercisesweek36.html
index 14a1a71a1..cb5be9132 100644
--- a/doc/LectureNotes/_build/html/exercisesweek36.html
+++ b/doc/LectureNotes/_build/html/exercisesweek36.html
@@ -333,6 +333,16 @@ const thebe_selector_output = ".output, .cell_output"
    Week 42 Constructing a Neural Network code with introduction to Tensor flow
   
  
+ 
  • 
+  
+   Exercises weeks 43 and 44
+  
+ 
    • 
+ 
  • 
+  
+   Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations
+  
+ 
    • 
 
 
    
  
diff --git a/doc/LectureNotes/_build/html/exercisesweek37.html b/doc/LectureNotes/_build/html/exercisesweek37.html
index cf05619c1..dc866b9fd 100644
--- a/doc/LectureNotes/_build/html/exercisesweek37.html
+++ b/doc/LectureNotes/_build/html/exercisesweek37.html
@@ -333,6 +333,16 @@ const thebe_selector_output = ".output, .cell_output"
    Week 42 Constructing a Neural Network code with introduction to Tensor flow
   
  
+ 
  • 
+  
+   Exercises weeks 43 and 44
+  
+ 
    • 
+ 
  • 
+  
+   Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations
+  
+ 
    • 
 
 
    
  
diff --git a/doc/LectureNotes/_build/html/exercisesweek38.html b/doc/LectureNotes/_build/html/exercisesweek38.html
index 9786b5333..8aecc16b5 100644
--- a/doc/LectureNotes/_build/html/exercisesweek38.html
+++ b/doc/LectureNotes/_build/html/exercisesweek38.html
@@ -333,6 +333,16 @@ const thebe_selector_output = ".output, .cell_output"
    Week 42 Constructing a Neural Network code with introduction to Tensor flow
   
  
+ 
  • 
+  
+   Exercises weeks 43 and 44
+  
+ 
    • 
+ 
  • 
+  
+   Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations
+  
+ 
    • 
 
 
    
  
diff --git a/doc/LectureNotes/_build/html/exercisesweek39.html b/doc/LectureNotes/_build/html/exercisesweek39.html
index c9e6c99bd..417b35c92 100644
--- a/doc/LectureNotes/_build/html/exercisesweek39.html
+++ b/doc/LectureNotes/_build/html/exercisesweek39.html
@@ -331,6 +331,16 @@ const thebe_selector_output = ".output, .cell_output"
    Week 42 Constructing a Neural Network code with introduction to Tensor flow
   
  
+ 
  • 
+  
+   Exercises weeks 43 and 44
+  
+ 
    • 
+ 
  • 
+  
+   Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations
+  
+ 
    • 
 
 
    
  
diff --git a/doc/LectureNotes/_build/html/exercisesweek41.html b/doc/LectureNotes/_build/html/exercisesweek41.html
index d53e81fd9..34c086bfd 100644
--- a/doc/LectureNotes/_build/html/exercisesweek41.html
+++ b/doc/LectureNotes/_build/html/exercisesweek41.html
@@ -333,6 +333,16 @@ const thebe_selector_output = ".output, .cell_output"
    Week 42 Constructing a Neural Network code with introduction to Tensor flow
   
  
+ 
  • 
+  
+   Exercises weeks 43 and 44
+  
+ 
    • 
+ 
  • 
+  
+   Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations
+  
+ 
    • 
 
 
    
  
@@ -804,15 +814,15 @@ regression.
    
 
    
 
    
 
    Own inversion
-[[3.89481038]
- [3.13155259]]
-Eigenvalues of Hessian Matrix:[0.28194659 4.81122914]
+[[3.97739698]
+ [2.9189726 ]]
+Eigenvalues of Hessian Matrix:[0.27987128 4.51549827]
 theta from own gd
-[[3.89481038]
- [3.13155259]]
+[[3.97739698]
+ [2.9189726 ]]
 theta from own sdg
-[[3.88291866]
- [3.19123037]]
+[[3.9577228 ]
+ [2.91473433]]
 
    
 
    
 _images/exercisesweek41_5_1.png
@@ -934,14 +944,14 @@ first example shows results with ordinary leats squares.
    
 
    
 
    
 
    Own inversion
-[[4.23636536]
- [2.77184871]]
-Eigenvalues of Hessian Matrix:[0.30125775 4.66878535]
+[[4.32133765]
+ [2.59905073]]
+Eigenvalues of Hessian Matrix:[0.29426584 4.28858038]
 
    
 
    
 
    theta from own gd
-[[4.23636536]
- [2.77184871]]
+[[4.32133765]
+ [2.59905073]]
 
    
 
    
 _images/exercisesweek41_16_2.png
@@ -1012,73 +1022,73 @@ Eigenvalues of Hessian Matrix:[0.30125775 4.66878535]
 
    Own inversion
 [[4.]
  [3.]]
-Eigenvalues of Hessian Matrix:[0.35058127 4.29679459]
-0 [-8.31895514] [-8.15055258]
-1 [-0.75472506] [0.63957747]
-2 [-0.69314603] [0.58739348]
-3 [-0.63659131] [0.53946725]
-4 [-0.58465096] [0.49545139]
-5 [-0.5369485] [0.45502684]
-6 [-0.49313815] [0.41790059]
-7 [-0.45290234] [0.38380352]
-8 [-0.41594943] [0.35248847]
-9 [-0.38201155] [0.32372846]
-10 [-0.35084272] [0.29731502]
-11 [-0.32221699] [0.27305669]
-12 [-0.29592687] [0.25077762]
-13 [-0.2717818] [0.23031634]
-14 [-0.24960675] [0.21152452]
-15 [-0.229241] [0.19426595]
-16 [-0.21053692] [0.17841553]
-17 [-0.19335893] [0.16385836]
-18 [-0.17758251] [0.15048894]
-19 [-0.16309331] [0.13821034]
-20 [-0.14978631] [0.12693357]
-21 [-0.13756504] [0.11657689]
-22 [-0.12634093] [0.10706523]
-23 [-0.1160326] [0.09832963]
-24 [-0.10656534] [0.09030678]
-25 [-0.09787053] [0.08293853]
-26 [-0.08988514] [0.07617146]
-27 [-0.08255129] [0.06995653]
-28 [-0.07581582] [0.06424868]
-29 [-0.06962991] [0.05900655]
+Eigenvalues of Hessian Matrix:[0.30311767 4.03556032]
+0 [-14.28624958] [-15.54071847]
+1 [-0.04900086] [0.04473913]
+2 [-0.04532032] [0.0413787]
+3 [-0.04191624] [0.03827068]
+4 [-0.03876784] [0.0353961]
+5 [-0.03585592] [0.03273744]
+6 [-0.03316272] [0.03027848]
+7 [-0.03067182] [0.02800421]
+8 [-0.02836801] [0.02590077]
+9 [-0.02623724] [0.02395532]
+10 [-0.02426651] [0.022156]
+11 [-0.02244382] [0.02049182]
+12 [-0.02075802] [0.01895265]
+13 [-0.01919885] [0.01752908]
+14 [-0.0177568] [0.01621244]
+15 [-0.01642305] [0.0149947]
+16 [-0.01518949] [0.01386842]
+17 [-0.01404858] [0.01282674]
+18 [-0.01299337] [0.0118633]
+19 [-0.01201742] [0.01097223]
+20 [-0.01111477] [0.01014809]
+21 [-0.01027992] [0.00938585]
+22 [-0.00950778] [0.00868086]
+23 [-0.00879363] [0.00802883]
+24 [-0.00813313] [0.00742577]
+25 [-0.00752224] [0.00686801]
+26 [-0.00695723] [0.00635214]
+27 [-0.00643466] [0.00587502]
+28 [-0.00595134] [0.00543374]
+29 [-0.00550433] [0.0050256]
 theta from own gd
-[[3.81759234]
- [3.15457792]]
-0 [-0.06394871] [0.05419212]
-1 [-0.05873105] [0.04977051]
-2 [-0.0523738] [0.04438319]
-3 [-0.04619338] [0.03914571]
-4 [-0.04057027] [0.03438051]
-5 [-0.03557316] [0.0301458]
-6 [-0.03117156] [0.02641575]
-7 [-0.02730775] [0.02314144]
-8 [-0.02392053] [0.020271]
-9 [-0.02095266] [0.01775594]
-10 [-0.01835274] [0.01555268]
-11 [-0.01607534] [0.01362274]
-12 [-0.01408051] [0.01193226]
-13 [-0.01233322] [0.01045155]
-14 [-0.01080274] [0.00915458]
-15 [-0.00946219] [0.00801855]
-16 [-0.00828799] [0.0070235]
-17 [-0.0072595] [0.00615193]
-18 [-0.00635865] [0.00538851]
-19 [-0.00556958] [0.00471983]
-20 [-0.00487843] [0.00413413]
-21 [-0.00427304] [0.00362111]
-22 [-0.00374279] [0.00317175]
-23 [-0.00327833] [0.00277816]
-24 [-0.00287151] [0.00243341]
-25 [-0.00251517] [0.00213144]
-26 [-0.00220306] [0.00186694]
-27 [-0.00192967] [0.00163526]
-28 [-0.00169021] [0.00143234]
-29 [-0.00148047] [0.00125459]
+[[3.98320492]
+ [3.01533437]]
+0 [-0.00509089] [0.00464812]
+1 [-0.0047085] [0.00429899]
+2 [-0.00424012] [0.00387135]
+3 [-0.00378113] [0.00345227]
+4 [-0.00335942] [0.00306724]
+5 [-0.00298058] [0.00272135]
+6 [-0.00264305] [0.00241318]
+7 [-0.00234327] [0.00213947]
+8 [-0.00207732] [0.00189665]
+9 [-0.00184151] [0.00168135]
+10 [-0.00163245] [0.00149047]
+11 [-0.00144711] [0.00132125]
+12 [-0.00128282] [0.00117125]
+13 [-0.00113717] [0.00103827]
+14 [-0.00100807] [0.00092039]
+15 [-0.00089362] [0.0008159]
+16 [-0.00079216] [0.00072326]
+17 [-0.00070222] [0.00064115]
+18 [-0.0006225] [0.00056836]
+19 [-0.00055182] [0.00050383]
+20 [-0.00048917] [0.00044663]
+21 [-0.00043363] [0.00039592]
+22 [-0.0003844] [0.00035097]
+23 [-0.00034076] [0.00031112]
+24 [-0.00030207] [0.0002758]
+25 [-0.00026778] [0.00024449]
+26 [-0.00023737] [0.00021673]
+27 [-0.00021042] [0.00019212]
+28 [-0.00018653] [0.00017031]
+29 [-0.00016536] [0.00015097]
 theta from own gd wth momentum
-[[3.99630114]
- [3.00313452]]
+[[3.99951642]
+ [3.00044152]]
 
    
 
    
 
    
@@ -1131,17 +1141,17 @@ theta from own gd wth momentum
 
    
 
    
 
    Own inversion
-[[4.08185019]
- [2.82781715]]
-Eigenvalues of Hessian Matrix:[0.32244056 3.90871918]
-0 [-14.19393543] [-14.29174301]
-1 [-6.35255737e-15] [-1.10851799e-14]
-2 [-5.89805982e-17] [-2.66490332e-16]
-3 [9.81853487e-16] [1.10741066e-15]
-4 [-5.89805982e-17] [-2.66490332e-16]
+[[3.66959644]
+ [3.26513904]]
+Eigenvalues of Hessian Matrix:[0.33285444 4.11450263]
+0 [-12.48534921] [-14.7906583]
+1 [-1.09712586e-14] [-5.19623863e-15]
+2 [-1.27068495e-16] [-2.9424428e-16]
+3 [5.91540705e-16] [7.2837521e-16]
+4 [-1.27068495e-16] [-2.9424428e-16]
 beta from own Newton code
-[[4.08185019]
- [2.82781715]]
+[[3.66959644]
+ [3.26513904]]
 
    
 
    
 
    
@@ -1230,20 +1240,18 @@ beta from own Newton code
 
    
 
    
 
    Own inversion
-[[3.41716708]
- [3.50046106]]
-Eigenvalues of Hessian Matrix:[0.24252405 4.30774404]
+[[3.68184997]
+ [3.32507975]]
+Eigenvalues of Hessian Matrix:[0.26370919 4.62518501]
+theta from own gd
+[[3.68184997]
+ [3.32507975]]
 
    
 
    
-
    theta from own gd
-[[3.41716708]
- [3.50046106]]
-
    
-
    
-_images/exercisesweek41_22_2.png
+_images/exercisesweek41_22_1.png
 
    theta from own sdg
-[[3.38135654]
- [3.49216685]]
+[[3.68809785]
+ [3.32032017]]
 
    
 
    
 
    
@@ -1325,15 +1333,15 @@ Eigenvalues of Hessian Matrix:[0.24252405 4.30774404]
 
    
 
    
 
    Own inversion
-[[4.12427537]
- [2.85355539]]
-Eigenvalues of Hessian Matrix:[0.33486875 3.91080327]
+[[3.55555773]
+ [3.41891092]]
+Eigenvalues of Hessian Matrix:[0.30326262 4.34133193]
 theta from own gd
-[[4.12417157]
- [2.85365229]]
+[[3.55511609]
+ [3.41928689]]
 theta from own sdg with momentum
-[[4.25050227]
- [2.8249367 ]]
+[[3.58207928]
+ [3.37895549]]
 
    
 
    
 
    
@@ -1408,9 +1416,9 @@ theta from own sdg with momentum
 
    
 
    
 
    theta from own AdaGrad
-[[1.99999773]
- [3.0000164 ]
- [3.99998703]]
+[[2.00039962]
+ [2.99772199]
+ [4.00233436]]
 
    
 
    
 
    
@@ -1492,9 +1500,9 @@ theta from own sdg with momentum
 
    
 
    
 
    theta from own RMSprop
-[[2.01023308]
- [2.95235306]
- [4.04597076]]
+[[1.99858411]
+ [2.9981377 ]
+ [3.99861427]]
 
    
 
    
 
    
@@ -1580,9 +1588,9 @@ theta from own sdg with momentum
 
    
 
    
 
    theta from own ADAM
-[[1.99998193]
- [3.0000747 ]
- [3.99992263]]
+[[2.00002678]
+ [2.99985103]
+ [4.00014662]]
 
    
 
    
 
    
@@ -1655,7 +1663,7 @@ It provides composable transformations of Python+NumPy programs: differentiate,
   return asarray(x, dtype=self.dtype)
 
    
 
    
-
    [<matplotlib.lines.Line2D at 0x10febc640>]
+
    [<matplotlib.lines.Line2D at 0x1262b9a90>]
 
    
 
    
 _images/exercisesweek41_39_2.png
@@ -1694,7 +1702,7 @@ It provides composable transformations of Python+NumPy programs: differentiate,
   return asarray(x, dtype=self.dtype)
 
    
 
    
-
    <matplotlib.collections.PathCollection at 0x10febcf10>
+
    <matplotlib.collections.PathCollection at 0x126323b50>
 
    
 
    
 _images/exercisesweek41_41_2.png
diff --git a/doc/LectureNotes/_build/html/exercisesweek42.html b/doc/LectureNotes/_build/html/exercisesweek42.html
index f8ba3c845..cbcf79ed3 100644
--- a/doc/LectureNotes/_build/html/exercisesweek42.html
+++ b/doc/LectureNotes/_build/html/exercisesweek42.html
@@ -333,6 +333,16 @@ const thebe_selector_output = ".output, .cell_output"
    Week 42 Constructing a Neural Network code with introduction to Tensor flow
   
  
+ 
  • 
+  
+   Exercises weeks 43 and 44
+  
+ 
    • 
+ 
  • 
+  
+   Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations
+  
+ 
    • 
 
 
    
  
diff --git a/doc/LectureNotes/_build/html/exercisesweek43.html b/doc/LectureNotes/_build/html/exercisesweek43.html
index 5c1994bb7..0bd96ec58 100644
--- a/doc/LectureNotes/_build/html/exercisesweek43.html
+++ b/doc/LectureNotes/_build/html/exercisesweek43.html
@@ -933,8 +933,9 @@ Accuracy score on data set:  0.5
 Learning rate  =  0.0001
 Lambda =  0.0001
 Accuracy score on data set:  0.5
-
-Learning rate  =  0.0001
+
    
+
    
+
    Learning rate  =  0.0001
 Lambda =  0.001
 Accuracy score on data set:  0.5
 
@@ -1117,7 +1118,7 @@ Accuracy score on data set:  0.5
   warnings.warn(
 
    
 
    
-_images/exercisesweek43_26_2.png
+_images/exercisesweek43_26_3.png
 
    
 
    
 
    
diff --git a/doc/LectureNotes/_build/html/linalg.html b/doc/LectureNotes/_build/html/linalg.html
index 764e896f7..01c2390fa 100644
--- a/doc/LectureNotes/_build/html/linalg.html
+++ b/doc/LectureNotes/_build/html/linalg.html
@@ -333,6 +333,16 @@ const thebe_selector_output = ".output, .cell_output"
    Week 42 Constructing a Neural Network code with introduction to Tensor flow
   
  
+ 
  • 
+  
+   Exercises weeks 43 and 44
+  
+ 
    • 
+ 
  • 
+  
+   Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations
+  
+ 
    • 
 
 
    
  
@@ -653,8 +663,9 @@ matrices and vectors.
    
 
    
 
    
 
    
-
    [-1.0526992  -0.18065292  0.78521833  0.80074264  0.59327016 -1.16492688
-  0.85276246 -0.04581197 -0.65825344 -0.87931006]
+
    [-8.18243276e-01 -1.33525471e+00 -8.98409646e-01 -7.24434901e-01
+  2.27832584e+00 -8.78192446e-01 -1.35539164e-03 -7.36097055e-01
+ -1.06125720e+00  2.86376300e+00]
 
    
 
    
 
    
@@ -875,26 +886,36 @@ as (recall that we user lowercase letters for vectors and uppercase letters for
 
    
 
    
 
    
-
    [[0.80207897 0.22885848 0.14526269 0.91022359 0.76135601 0.52687741
-  0.68711054 0.01455922 0.70967214 0.47297104]
- [0.87431418 0.15724663 0.06301519 0.41894238 0.47364408 0.06406913
-  0.31588043 0.87953769 0.74731872 0.10490195]
- [0.87533326 0.71038664 0.29726695 0.34011629 0.51741855 0.32185967
-  0.58793527 0.0510594  0.81948868 0.5914397 ]
- [0.96850702 0.53558374 0.40512793 0.83443463 0.96618584 0.54644868
-  0.1871257  0.28585116 0.79035184 0.2171263 ]
- [0.46873567 0.91358019 0.28294305 0.03061555 0.86850963 0.19910208
-  0.16650509 0.07417526 0.42535003 0.98765625]
- [0.29588674 0.70249832 0.5364857  0.1036131  0.56249706 0.15827078
-  0.53515878 0.40182469 0.24828523 0.44402322]
- [0.95746721 0.33159476 0.86811569 0.89098129 0.67109613 0.96599594
-  0.17078905 0.22297358 0.2193546  0.4993133 ]
- [0.60675691 0.33800793 0.23780865 0.30914432 0.64695862 0.19717411
-  0.94400087 0.07404236 0.20967833 0.74391438]
- [0.72651548 0.13310008 0.78220032 0.90556496 0.45014    0.19314584
-  0.71977472 0.97866042 0.53700083 0.10479359]
- [0.17829104 0.30129931 0.77203046 0.2707158  0.09391542 0.82988221
-  0.61480907 0.29282684 0.37667238 0.91086026]]
+
    [[3.88960586e-01 2.54050804e-02 9.61246573e-01 4.42642980e-01
+  4.99344165e-01 8.87477172e-01 2.94785447e-01 4.80006130e-01
+  8.05206787e-02 9.27743488e-01]
+ [9.00892604e-01 5.95597666e-01 5.92327822e-01 4.71979573e-01
+  2.92123586e-01 1.36128659e-01 7.83917436e-01 1.19154013e-01
+  7.92576742e-01 6.87338811e-01]
+ [5.40660618e-01 1.11628185e-01 9.46904874e-01 7.61050884e-01
+  5.94782102e-01 6.61949985e-03 3.01329488e-01 4.26660718e-01
+  2.07824586e-02 5.75821358e-01]
+ [7.16539406e-01 8.20261698e-01 5.70328587e-01 5.03331552e-01
+  9.26331821e-01 6.78989003e-01 9.53683592e-01 1.41471556e-01
+  3.17934657e-01 2.93828592e-01]
+ [1.22354860e-01 4.77805840e-01 9.56878976e-01 2.21706540e-01
+  8.57223110e-01 2.55043852e-01 7.60863613e-01 4.61669885e-01
+  5.31290061e-01 7.60381656e-01]
+ [1.68685905e-01 1.26113838e-01 8.15301931e-01 4.38511237e-01
+  4.42633236e-01 7.28595266e-01 3.30571694e-01 3.15593134e-01
+  8.22979294e-01 7.32441343e-01]
+ [5.43608740e-01 2.76181397e-01 6.66722647e-01 7.35434042e-01
+  2.44020145e-01 2.15352815e-01 1.91487049e-01 6.11137061e-01
+  2.79167312e-01 2.27230624e-01]
+ [8.97033646e-02 1.88667234e-01 1.80076024e-01 8.65728698e-01
+  2.79254918e-01 3.33457718e-01 9.52626194e-01 8.75240336e-01
+  7.76059447e-01 3.52565509e-01]
+ [8.49556373e-03 1.50925722e-04 8.98413020e-01 7.49186362e-01
+  1.90602444e-01 4.79877535e-01 6.10615323e-01 7.78031111e-02
+  2.39856058e-01 3.20554718e-01]
+ [2.63843494e-01 1.46306318e-01 7.29933720e-01 5.27826845e-01
+  1.33857909e-01 3.50359169e-01 4.20020918e-01 8.58581665e-03
+  5.54378734e-02 8.68351374e-01]]
 
    
 
    
 
    
@@ -954,13 +975,13 @@ covariance matrix through the np.linalg.eig() function.
    
 
    
 
    
 
    
-
    0.016972818397989375
-3.9050595316983907
-0.11007935789924998
-[[ 1.21860973  3.69519297  6.31082439]
- [ 3.69519297 12.18993003 19.00431775]
- [ 6.31082439 19.00431775 65.3283771 ]]
-[72.14421971  0.08339896  6.5092982 ]
+
    0.07291818479810824
+4.313183599076104
+-0.05687021620384533
+[[ 0.87810129  2.59703606  2.52470105]
+ [ 2.59703606  8.59883217  6.83603568]
+ [ 2.52470105  6.83603568 13.87354403]]
+[19.25091007  0.06413187  4.03543554]
 
    
 
    
 
    
diff --git a/doc/LectureNotes/_build/html/project1.html b/doc/LectureNotes/_build/html/project1.html
index e736e8ce4..8a063cbf5 100644
--- a/doc/LectureNotes/_build/html/project1.html
+++ b/doc/LectureNotes/_build/html/project1.html
@@ -56,7 +56,7 @@ const thebe_selector_output = ".output, .cell_output"
     
     
     
-    
+    
     
     
     
@@ -333,6 +333,16 @@ const thebe_selector_output = ".output, .cell_output"
    Week 42 Constructing a Neural Network code with introduction to Tensor flow
   
  
+ 
  • 
+  
+   Exercises weeks 43 and 44
+  
+ 
    • 
+ 
  • 
+  
+   Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations
+  
+ 
    • 
 
 
    
  
@@ -700,7 +710,7 @@ which polynomial fits the data best.
    
 
    
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31707/39730396.py:11: MatplotlibDeprecationWarning: Calling gca() with keyword arguments was deprecated in Matplotlib 3.4. Starting two minor releases later, gca() will take no keyword arguments. The gca() function should only be used to get the current axes, or if no axes exist, create new axes with default keyword arguments. To create a new axes with non-default arguments, use plt.axes() or plt.subplot().
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11057/39730396.py:11: MatplotlibDeprecationWarning: Calling gca() with keyword arguments was deprecated in Matplotlib 3.4. Starting two minor releases later, gca() will take no keyword arguments. The gca() function should only be used to get the current axes, or if no axes exist, create new axes with default keyword arguments. To create a new axes with non-default arguments, use plt.axes() or plt.subplot().
   ax = fig.gca(projection='3d')
 
    
 
    
@@ -1059,11 +1069,11 @@ of code developers and contributors keeps increasing.
    
             
                 
 
     
-    
+    
         
         
    
             
    previous
    
-            
    Week 42 Constructing a Neural Network code with introduction to Tensor flow
    
+            
    Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations
    
         
    
         
     
diff --git a/doc/LectureNotes/_build/html/project2.html b/doc/LectureNotes/_build/html/project2.html
index 5800974b8..29936c86c 100644
--- a/doc/LectureNotes/_build/html/project2.html
+++ b/doc/LectureNotes/_build/html/project2.html
@@ -332,6 +332,16 @@ const thebe_selector_output = ".output, .cell_output"
    Week 42 Constructing a Neural Network code with introduction to Tensor flow
   
  
+ 
  • 
+  
+   Exercises weeks 43 and 44
+  
+ 
    • 
+ 
  • 
+  
+   Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations
+  
+ 
    • 
 
 
    
  
diff --git a/doc/LectureNotes/_build/html/schedule.html b/doc/LectureNotes/_build/html/schedule.html
index 817725adb..d8806a39c 100644
--- a/doc/LectureNotes/_build/html/schedule.html
+++ b/doc/LectureNotes/_build/html/schedule.html
@@ -331,6 +331,16 @@ const thebe_selector_output = ".output, .cell_output"
    Week 42 Constructing a Neural Network code with introduction to Tensor flow
   
  
+ 
  • 
+  
+   Exercises weeks 43 and 44
+  
+ 
    • 
+ 
  • 
+  
+   Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations
+  
+ 
    • 
 
 
    
  
diff --git a/doc/LectureNotes/_build/html/searchindex.js b/doc/LectureNotes/_build/html/searchindex.js
index 3f4072a76..dd395913c 100644
--- a/doc/LectureNotes/_build/html/searchindex.js
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:[9,10],agorithm:10,agre:[5,6,29,34,35],agreement:[13,21,37,38],ahead:9,ai:[0,27,31],aid:[11,20],aim:[0,1,4,6,7,11,14,15,16,24,25,26,27,33,35,36,40,41],ainv:5,airplan:3,aka:[5,34,35],al:[0,2,4,15,16,17,27,31,32,33,34,35,36,37,38,39,40,41],alarm:[5,7,34,35],algebra:[0,3,5,13,21,24,33,34,35,37,38],algorithm:[0,1,2,4,5,6,7,8,13,14,22,23,24,25,26,29,31,32,34,35,36,41],align:[0,2,5,6,7,8,13,26,29,32,33,34,35,36,37,41],all:[0,1,2,3,4,5,6,7,9,10,11,12,13,14,15,21,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41],allevi:[1,13,36,37,40,41],alloc:[3,25],allow:[0,1,2,3,5,6,8,10,13,15,16,23,24,25,26,32,33,34,35,36,37,38,39,40,41],almost:[0,1,6,8,11,13,21,29,32,35,36,37,38,40,41],alon:[2,9,41],along:[2,3,4,5,6,9,10,11,23,24,25,32,33,34,35,36,41],alpha:[0,1,2,3,4,6,7,8,9,10,13,14,23,29,32,33,35,36,37,39,40,41],alpha_0:3,alpha_1:3,alpha_2:3,alpha_:10,alpha_i:[3,13,37],alpha_k:[13,37],alpha_m:10,alpha_n:3,alpha_opt:[13,37],alreadi:[2,3,4,5,6,10,12,24,25,29,32,33,34,35,38,39,41],also:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,21,23,24,25,26,27,28,29,32,33,34,35,36,37,38,39,40,41],alter:[1,39,40,41],altern:[0,1,4,5,6,7,8,9,11,13,25,26,32,33,34,35,36,37,38,39,40,41],although:[0,1,5,6,8,10,13,16,21,23,32,34,35,37,38,40,41],alwai:[0,3,5,6,12,13,16,21,29,32,33,34,35,36,37,38,39],am:[4,33],ame2016:[0,32],american:[0,33],among:[0,3,5,9,10,12,25,32,33,34,38,39],amongst:[5,34,35],amount:[0,1,3,4,6,8,10,14,23,24,35,40,41],an:[1,2,3,5,6,7,8,9,11,12,13,14,15,16,17,19,20,21,23,24,25,26,27,29,30,31,33,34,35,36,37,38,39,40,41],an_:29,anaconda:[0,1,15,24,26,32,40,41],analog:[13,37,38],analys:[6,34,35],analysi:[1,3,4,7,14,15,16,17,19,21,22,25,31,36,39,40,41],analyt:[2,3,5,6,7,12,13,15,22,24,26,27,32,33,34,35,36,37,38,39],analytical_gradi:21,analyz:[0,1,3,4,5,6,16,17,26,27,29,32,33,34,39,40,41],andrew:[1,39,40,41],angl:[0,3,9,33],anharmon:3,ani:[0,1,2,3,4,5,6,7,8,9,10,12,14,18,23,29,32,33,34,35,38,39,40,41],anim:[4,12,38,39],ann:[12,38,39],annot:[0,1,3,7,8,23,32,33,36,39,40,41],announc:32,anoth:[0,1,3,4,5,6,7,8,10,11,12,13,25,26,27,29,32,33,36,37,38,39,40,41],ans_vspac:[],ansatz:[0,32],answer:[0,1,3,5,6,25,26,27,30,32,34,35,39,40,41],antialias:[2,6,26,41],anticip:4,anymor:[1,8,40,41],anyon:[4,8],anyth:[1,29,40,41],anytim:[30,32],apach:[1,40,41],apart:[11,13,36,37],api:[1,24,32,40,41],appar:[2,41],appear:[0,1,3,13,16,23,25,29,32,37,38,39,40,41],append:[1,3,4,8,9,13,21,23,32,37,40,41],appendix:26,appl:[3,4],appli:[0,1,2,3,4,6,7,8,9,10,11,12,13,15,16,26,27,29,31,32,33,34,35,36,37,38,39,40,41],applic:[0,1,3,4,5,6,7,9,12,13,21,25,26,29,31,32,33,35,36,37,38,39,40,41],apply_gradi:4,approach:[1,2,4,5,6,9,10,11,12,13,17,21,23,24,29,31,33,36,39,40,41],appropri:[2,6,9,12,13,24,29,35,37,38,39,41],approv:32,approx:[0,2,3,6,10,11,13,29,32,35,36,37,38,41],approxim:[0,1,2,3,4,5,6,7,10,11,13,18,19,26,29,32,33,34,35,36,37,38,40,41],apt:[0,15,24,26,32],aq:29,ar:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,19,21,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41],aragorn:32,arang:[1,3,4,6,7,9,10,12,13,23,26,32,36,37,38,39,40,41],arbitrari:[1,4,6,8,12,13,29,35,36,37,38,39,40,41],arbitrarili:[0,1,11,32,39,40,41],arc:[6,26],architectur:[3,4,12,23],archiv:27,area:[0,3,6,9,26,31,32],arg:[0,2,3,4,13,32,38],argmax:[1,11,39,40,41],argmin:[4,10,14],argnum:[2,13,38],argnum_0:[],argnum_1:[],args_with_tang:[3,4],argsort:11,argu:[1,13,37,38,40,41],argument:[0,2,3,5,6,11,12,13,21,23,26,32,33,34,35,39,41],argval:[],aris:[0,6,12,13,29,32,35,36,37,39],arithmet:[0,13,25,32,37,38],arm:[6,34],armadillo:25,around:[0,1,4,5,6,11,29,32,34,35,39,40,41],arr:[],arrai:[0,1,2,3,4,5,6,7,8,9,11,12,13,14,21,23,24,26,29,33,34,35,36,37,38,39,40,41],arrang:[3,32],arraybox:[13,37,38],arriv:[0,6,9,11,25,29,32,35],arrow:[12,23,38,39,41],arrowprop:8,art3d:[13,37],art:[0,1,15,24,32,40,41],articl:[0,3,4,6,10,18,27,32,33,34,35],artifici:[0,2,7,12,31,32,36,41],artificialneuron:[12,38,39],arug:[13,37,38],arxiv:[3,4,21,27,37,38],as_fram:33,asap:32,asarrai:[0,2,6,9,21,33,34,37],ashort:20,asid:33,ask:[5,6,11,12,34,35,39],aspect:[0,6,24,26,32,33,34],assembl:[0,3,32],assert:[4,23,41],assess:[0,6,26,32,33,34,35],assici:4,assign:[0,7,8,9,12,13,14,23,28,30,31,32,33,36,37,38,39,40,41],associ:[0,6,9,12,14,29,32,35,38,39],assum:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,19,23,25,26,29,32,33,34,35,36,37,38,39,40,41],assumpt:[0,3,5,6,9,11,18,26,29,32,33],ast:[0,5,6,18,26,32,34,35,36],astronomi:[32,33,34,35,36,37,38,39,40,41],astyp:[4,9,10],asymmetri:[0,32],asymptot:[4,6,35],async_wait:[3,4],atla:32,atom:[0,32],attempt:[0,4,6,7,8,10,32,34,36],attend:28,attent:[0,23,25,32,41],attr:[3,4,34],attract:[0,10,32],attribut:[0,9,13,23,27,32,34,35,41],attributeerror:[13,34],audi:[0,32],audio:[3,4],august:[15,16,32],aurelien:[0,15,28,31,32,38,40,41],austfjel:[6,26],author:[0,1,10,29,32,40,41],authour:32,auto:[6,9,10,23,29,41],autocor:29,autocorrelation_tim:29,autocorrelform:29,autocovari:29,autoencod:[4,24,32],autoencond:24,autograd:[22,23,24,27,32],autom:[0,24,31,32],automac:25,automag:32,automat:[0,1,2,3,4,11,16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Linear Regression","14. Building a Feed Forward Neural Network","15. Solving Differential Equations  with Deep Learning","16. Convolutional Neural Networks","17. Recurrent neural networks: Overarching view","4. Ridge and Lasso Regression","5. Resampling Methods","6. Logistic Regression","8. Support Vector Machines, overarching aims","9. Decision trees, overarching aims","10. Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods","11. Basic ideas of the Principal Component Analysis (PCA)","13. Neural networks","7. Optimization, the central part of any Machine Learning algortithm","12. Clustering and Unsupervised Learning","Exercises week 34","Exercises week 35","Exercises week 36","Exercises week 37","Exercises week 38","Exercises week 39","Exercises week 41","Exercises week 42","Exercises weeks 43 and 44","Applied Data Analysis and Machine Learning","2. Linear Algebra, Handling of Arrays and more Python Features","Project 1 on Machine Learning, deadline October 9 (midnight), 2023","Project 2 on Machine Learning, deadline November 13 (Midnight)","Teaching schedule with links to material","1. Elements of Probability Theory and Statistical Data Analysis","Teachers and Grading","Textbooks","Week 34: Introduction to the course, Logistics and Practicalities","Week 35: From Ordinary Linear Regression to Ridge and Lasso Regression","Week 36: Statistical interpretation of Linear Regression and Resampling techniques","Week 37: Statistical interpretations and Resampling Methods","Week 38: Logistic Regression and Optimization","Week 39: Optimization and  Gradient Methods","Week 40: Gradient descent methods (continued) and start Neural networks","Week 41 Neural networks and constructing a neural network code","Week 42 Constructing a Neural Network code with introduction to Tensor flow","Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations"],titleterms:{"1":[0,15,16,17,18,26,32,33],"12":39,"13":27,"14":35,"19":40,"2":[0,15,16,17,18,27,32,33],"2023":[26,30],"21":36,"23":36,"26":41,"3":[0,15,16,32,33],"31":33,"34":[15,32],"35":[16,33],"36":[17,34],"37":[18,35],"38":[19,36],"39":[20,21,37],"4":[0,33],"40":[21,38],"41":[21,39],"42":[22,40],"43":[23,41],"44":[23,41],"5":0,"7":34,"9":26,"case":[8,10,29,33,34,36,37],"class":36,"do":[1,32,34,35,37,38,39,40,41],"final":[12,21,27,33,34,37,38,39,41],"function":[0,1,6,7,8,10,11,12,13,21,23,26,27,29,32,33,34,35,36,37,38,39,40,41],"import":[5,21,25,32,33,34],"new":[4,34,35],A:[0,1,4,8,9,21,32,34,35,36,40,41],AND:[23,38,39,40,41],And:[21,32,33,34,36,37,38],But:[21,37,38],In:30,Is:[40,41],Ising:6,OR:[38,39,40,41],The:[0,1,2,3,5,6,7,8,9,11,12,17,23,24,32,33,34,35,36,37,38,39,40,41],To:[32,33],With:[4,34],about:[32,33],abov:[23,34,41],activ:[1,12,23,27,34,38,39,40,41],ad:[0,6,17,26,32,33,38,39],adaboost:10,adagrad:[13,21,37,38],adam:[13,21,37,38],adapt:[10,21,37,38],adjust:[1,39,40,41],advanc:21,adversari:4,again:[3,9,36],ai:32,aim:[8,9,17,18,19,20,21,22,23,32],aka:[32,33],al:21,algebra:[25,32],algorithm:[9,10,11,12,21,27,33,37,38,39,40],algortithm:[13,36,37],all:8,an:[0,4,10,32],analys:[5,33],analysi:[0,5,6,11,24,26,27,29,32,33,34,35],analyt:[0,16,17,21,41],ani:[13,36,37],anoth:[9,34,35],appli:24,approach:[0,8,14,32,35,37,38],approxim:[12,39],architectur:[1,39,40,41],argument:[37,38],arrai:[25,32],artifici:[38,39],assist:30,assumpt:[34,35],august:33,autocorrel:29,autograd:[2,13,21,37,38,41],automat:[13,21,37,38,41],avoid:[37,38],b:[17,26,27,37,38],back:[1,11,12,39,40,41],background:[24,26,27,35],bag:10,base:[13,21,35,37,38],basic:[0,5,7,9,10,11,25,33,34,35,36],batch:[1,37,38,40,41],bay:[5,34,35],befor:11,beta:[34,35],better:[8,38,39],bia:[6,26,35],bias:[39,40,41],binari:[1,39,40],bind:32,bird:10,boldsymbol:[33,34,35],boost:10,bootstrap:[6,10,35],boston:[0,33],breast:[1,40,41],brief:[32,35,36,37],bring:[12,39],build:[1,3,9,23,40,41],c:[26,27,32],calcul:33,can:[21,32,35,37,38],cancer:[1,7,9,11,36,40,41],cart:9,center:33,central:[13,24,29,35,36,37],chain:[12,39],challeng:36,chang:10,channel:32,chi:[0,32],choic:[40,41],choos:[1,39,40,41],cifar01:3,classic:11,classif:[1,9,10,23,27,36,39,40,41],classifi:[8,36],clip:[1,40,41],cluster:14,cnn:3,code:[0,1,2,5,9,11,12,13,14,21,23,27,32,33,34,35,36,37,38,39,40,41],collect:[1,3,39,40,41],come:36,commun:32,compact:36,compar:[2,10,41],comparison:34,compet:[21,37,38],complet:33,complex:[0,6,26,33],complic:[6,37,38],compon:11,comput:[9,37,38],computation:35,computerlab:32,con:9,concept:29,condit:[34,35,36,37],confid:35,conjug:[13,37],construct:[39,40,41],continu:38,contn:32,convex:[8,13,36,37],convolut:[3,12,38,39],correctli:[34,35],correl:[11,33,36],correspond:[36,37],cost:[1,10,23,33,34,35,36,37,39,40,41],cours:[24,31,32],covari:[5,11,29,33],cover:32,critic:27,cross:[6,26,35,36],cython:32,d:[26,27],data:[0,1,3,6,7,9,11,15,16,23,24,26,29,32,33,34,36,39,40,41],dataset:[1,3,39,40,41],deadlin:[26,27,32],decai:[2,37,38,41],decis:[9,10],decomposit:[5,11,17,25,33],deep:[1,2,36,40,41],defin:[1,32,39,40,41],definit:39,degre:[0,33],deliveri:[26,27],delta:35,dens:[0,32],deriv:[5,12,33,34,35,36,37,39,40,41],descent:[2,10,13,21,27,36,37,38,41],descript:26,design:33,detail:[3,32,41],develop:[1,39,40,41],diagon:11,differ:[8,27,37,38],different:21,differenti:[2,13,37,38,41],diffus:[2,41],dimension:[2,3,8,23,26,33,41],directli:[37,38],disadvantag:9,discret:29,discuss:36,distribut:[5,29,34,35],distrubut:[34,35],doe:[33,34,38,39],domain:29,dot:[37,38],down:[1,40,41],dropout:[1,40,41],e:[26,27],each:36,economi:33,electron:[26,27],element:[0,29,32,37,38],elimin:25,energi:32,ensembl:10,entropi:[9,36],environ:[0,15,32],equat:[0,2,12,32,33,34,36,37,39,41],error:[0,10,32,33,35],essenti:32,estim:[34,35],et:21,etc:32,euler:[2,41],evalu:[1,27,39,40,41],exampl:[0,1,2,3,4,6,7,8,9,10,21,32,33,34,35,36,37,38,39,40,41],exercis:[0,6,15,16,17,18,19,20,21,22,23,32,33,35,41],expect:[18,29,34,35],expens:35,experi:29,explor:[0,15,16,32],exponenti:[2,41],express:[17,18,32,33,36,37],extend:[36,37],extrapol:4,extrem:[10,32],ey:10,f:[26,27],fall:30,famili:[1,32,33,40,41],famou:25,fantast:33,featur:[9,25,33],feed:[1,12,38,39,40,41],find:[35,37,38],fine:[1,40,41],first:[4,12,27,32,33,34,36,37,39,41],fit:[0,10,32,34],fix:33,flow:40,fold:[35,36],forc:3,forest:10,format:[26,27,32],forward:[1,2,12,38,39,40,41],fourier:3,frank:[6,26,33],freedom:[0,33],frequent:33,frequentist:[0,32],fridai:36,from:[5,10,12,21,27,33,34,35,36,37,38,39],full:[2,39,40,41],funtion:[40,41],further:[3,5,33],g:26,gan:4,gate:[23,38,39,40,41],gaussian:25,gd:[13,21,37,38],gener:[4,9,32],geometr:[11,36,37],get:21,gini:9,good:[0,32],goodfellow:21,grade:[30,32],gradient:[1,2,10,13,21,27,36,37,38,40,41],grid:36,group:36,growth:[2,41],ha:24,hand:[23,41],handl:[25,32,33],happen:[34,35],have:32,heard:32,hessian:[33,36,37],hidden:[2,40,41],histogram:35,homework:[36,37],hous:[0,33],how:36,hyperbol:[38,39],hyperparamet:[1,39,40,41],hyperplan:8,i:[1,40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Linear Regression","14. Building a Feed Forward Neural Network","15. Solving Differential Equations  with Deep Learning","16. Convolutional Neural Networks","17. Recurrent neural networks: Overarching view","4. Ridge and Lasso Regression","5. Resampling Methods","6. Logistic Regression","8. Support Vector Machines, overarching aims","9. Decision trees, overarching aims","10. Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods","11. Basic ideas of the Principal Component Analysis (PCA)","13. Neural networks","7. Optimization, the central part of any Machine Learning algortithm","12. Clustering and Unsupervised Learning","Exercises week 34","Exercises week 35","Exercises week 36","Exercises week 37","Exercises week 38","Exercises week 39","Exercises week 41","Exercises week 42","Exercises weeks 43 and 44","Applied Data Analysis and Machine Learning","2. Linear Algebra, Handling of Arrays and more Python Features","Project 1 on Machine Learning, deadline October 9 (midnight), 2023","Project 2 on Machine Learning, deadline November 13 (Midnight)","Teaching schedule with links to material","1. Elements of Probability Theory and Statistical Data Analysis","Teachers and Grading","Textbooks","Week 34: Introduction to the course, Logistics and Practicalities","Week 35: From Ordinary Linear Regression to Ridge and Lasso Regression","Week 36: Statistical interpretation of Linear Regression and Resampling techniques","Week 37: Statistical interpretations and Resampling Methods","Week 38: Logistic Regression and Optimization","Week 39: Optimization and  Gradient Methods","Week 40: Gradient descent methods (continued) and start Neural networks","Week 41 Neural networks and constructing a neural network code","Week 42 Constructing a Neural Network code with introduction to Tensor flow","Week 43: Deep Learning: Constructing a Neural Network code and solving differential 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\ No newline at end of file
diff --git a/doc/LectureNotes/_build/html/statistics.html b/doc/LectureNotes/_build/html/statistics.html
index 4534a0d41..8fdb80c31 100644
--- a/doc/LectureNotes/_build/html/statistics.html
+++ b/doc/LectureNotes/_build/html/statistics.html
@@ -333,6 +333,16 @@ const thebe_selector_output = ".output, .cell_output"
    Week 42 Constructing a Neural Network code with introduction to Tensor flow
   
  
+ 
  • 
+  
+   Exercises weeks 43 and 44
+  
+ 
    • 
+ 
  • 
+  
+   Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations
+  
+ 
    • 
 
 
    
  
@@ -1025,27 +1035,27 @@ uncorrelated.
    
 
    
 
    
 
    
-
    1.3329671101137754
-[[ 5.35146218  5.08271388  8.11911824  8.05880359  2.9797317   2.96911909
-   7.59642735  1.24785221  3.14389839  8.36403046]
- [ 5.08271388  4.827462    7.71137935  7.65409368  2.83009076  2.82001111
-   7.21493779  1.18518557  2.98601306  7.94399217]
- [ 8.11911824  7.71137935 12.31814386 12.22663583  4.52078202  4.5046808
-  11.52512898  1.89321335  4.76985203 12.68971917]
- [ 8.05880359  7.65409368 12.22663583 12.13580759  4.4871984   4.4712168
-  11.43951204  1.8791492   4.73441814 12.59545081]
- [ 2.9797317   2.83009076  4.52078202  4.4871984   1.65913552  1.65322635
-   4.22974406  0.69481287  1.75054469  4.65715086]
- [ 2.96911909  2.82001111  4.5046808   4.4712168   1.65322635  1.64733822
-   4.21467941  0.69233822  1.74430995  4.64056395]
- [ 7.59642735  7.21493779 11.52512898 11.43951204  4.22974406  4.21467941
-  10.78316665  1.77133246  4.4627795  11.8727831 ]
- [ 1.24785221  1.18518557  1.89321335  1.8791492   0.69481287  0.69233822
-   1.77133246  0.29097377  0.7330932   1.9503219 ]
- [ 3.14389839  2.98601306  4.76985203  4.73441814  1.75054469  1.74430995
-   4.4627795   0.7330932   1.84698999  4.91373404]
- [ 8.36403046  7.94399217 12.68971917 12.59545081  4.65715086  4.64056395
-  11.8727831   1.9503219   4.91373404 13.07250301]]
+
    2.0994119523801045
+[[12.84061976  8.96489054 20.01094579 11.7317759  10.16168848  9.47128712
+   5.28962017 13.19870992 12.47831084 19.16089488]
+ [ 8.96489054  6.25898624 13.97097196  8.19073291  7.09455047  6.61253537
+   3.69303559  9.21489709  8.71193859 13.37749489]
+ [20.01094579 13.97097196 31.18525109 18.28291282 15.83607342 14.76014528
+   8.24339513 20.56899695 19.44632008 29.86052354]
+ [11.7317759   8.19073291 18.28291282 10.71868557  9.28418209  8.65339992
+   4.83283826 12.0589434  11.40075395 17.50626752]
+ [10.16168848  7.09455047 15.83607342  9.28418209  8.04166112  7.49529781
+   4.18604968 10.44507049  9.87496787 15.16336815]
+ [ 9.47128712  6.61253537 14.76014528  8.65339992  7.49529781  6.9860553
+   3.90164278  9.7354157   9.20404676 14.13314468]
+ [ 5.28962017  3.69303559  8.24339513  4.83283826  4.18604968  3.90164278
+   2.1790289   5.43713337  5.14036907  7.8932215 ]
+ [13.19870992  9.21489709 20.56899695 12.0589434  10.44507049  9.7354157
+   5.43713337 13.56678624 12.82629718 19.69524041]
+ [12.47831084  8.71193859 19.44632008 11.40075395  9.87496787  9.20404676
+   5.14036907 12.82629718 12.12622478 18.62025406]
+ [19.16089488 13.37749489 29.86052354 17.50626752 15.16336815 14.13314468
+   7.8932215  19.69524041 18.62025406 28.59206948]]
 
    
 
    
 
    
@@ -1313,15 +1323,15 @@ more practically oriented methods like the blocking technique.
    
 
    
 
    
 
    
-
    -0.02582613386840159
-3.8311393813043355
--0.30339081517583943
-1.1608179281668718 12.374777250972322 24.97606135399951
-3.6353716266230895 4.038211969489939 12.83140314044099
-[[ 1.16081793  3.63537163  4.03821197]
- [ 3.63537163 12.37477725 12.83140314]
- [ 4.03821197 12.83140314 24.97606135]]
-[33.84671508  0.08066381  4.58427764]
+
    0.035513525941656535
+4.0516821246279795
+-0.00822879725131466
+1.0340060287164625 10.447659635275407 10.449001126081919
+3.1285896350792584 2.5721905504656455 7.8028954343792245
+[[ 1.03400603  3.12858964  2.57219055]
+ [ 3.12858964 10.44765964  7.80289543]
+ [ 2.57219055  7.80289543 10.44900113]]
+[19.14871402  0.07942491  2.70252786]
 
    
 
    
 
    
@@ -1651,7 +1661,7 @@ assumption for approximating \(\sigma
 
    
 
    
 
    
-
    0.01229732982000352 0.93003138502386
+
    -0.024318244280276506 1.0399587275832265
 
    
 
    
 _images/statistics_188_1.png
diff --git a/doc/LectureNotes/_build/html/teachers.html b/doc/LectureNotes/_build/html/teachers.html
index 79d903759..e6f1a125d 100644
--- a/doc/LectureNotes/_build/html/teachers.html
+++ b/doc/LectureNotes/_build/html/teachers.html
@@ -331,6 +331,16 @@ const thebe_selector_output = ".output, .cell_output"
    Week 42 Constructing a Neural Network code with introduction to Tensor flow
   
  
+ 
  • 
+  
+   Exercises weeks 43 and 44
+  
+ 
    • 
+ 
  • 
+  
+   Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations
+  
+ 
    • 
 
 
    
  
diff --git a/doc/LectureNotes/_build/html/textbooks.html b/doc/LectureNotes/_build/html/textbooks.html
index 1baf3794e..9503ec8cc 100644
--- a/doc/LectureNotes/_build/html/textbooks.html
+++ b/doc/LectureNotes/_build/html/textbooks.html
@@ -331,6 +331,16 @@ const thebe_selector_output = ".output, .cell_output"
    Week 42 Constructing a Neural Network code with introduction to Tensor flow
   
  
+ 
  • 
+  
+   Exercises weeks 43 and 44
+  
+ 
    • 
+ 
  • 
+  
+   Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations
+  
+ 
    • 
 
 
    
  
diff --git a/doc/LectureNotes/_build/html/week34.html b/doc/LectureNotes/_build/html/week34.html
index 4f07987a3..c722c1557 100644
--- a/doc/LectureNotes/_build/html/week34.html
+++ b/doc/LectureNotes/_build/html/week34.html
@@ -333,6 +333,16 @@ const thebe_selector_output = ".output, .cell_output"
    Week 42 Constructing a Neural Network code with introduction to Tensor flow
   
  
+ 
  • 
+  
+   Exercises weeks 43 and 44
+  
+ 
    • 
+ 
  • 
+  
+   Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations
+  
+ 
    • 
 
 
    
  
@@ -1815,8 +1825,8 @@ developed in the 1970s, namely EISPACK and LINPACK.  We describe them shortly he
 
    
 
    
 
    
-
    [ 0.27919014 -0.36550376 -0.86282204  1.31714002 -1.99484719 -0.63837812
-  1.45062284 -0.33079132 -0.58182803 -0.7207467 ]
+
    [ 0.98502634 -1.56822376 -0.45668633  1.26063304 -1.43801947 -0.72264336
+  0.12428533 -0.64472123 -0.99439119  0.84257054]
 
    
 
    
 
    
@@ -2041,26 +2051,26 @@ lowercase letters for vectors and uppercase letters for matrices)
    
 
    
 
    
 
    
-
    [[0.23516186 0.40009482 0.40666305 0.26318493 0.37335014 0.7865355
-  0.11587186 0.81840893 0.38046294 0.17138811]
- [0.70506522 0.56475572 0.02507163 0.57935482 0.33537181 0.32382849
-  0.37266855 0.00348543 0.11400145 0.84991754]
- [0.27152452 0.88901776 0.95166414 0.82292185 0.09524714 0.92630576
-  0.29374695 0.55867377 0.10959669 0.71647328]
- [0.15913825 0.89604286 0.80447153 0.59412285 0.30990916 0.94642209
-  0.20494446 0.7215423  0.03049638 0.55649207]
- [0.70899024 0.80389541 0.60883945 0.7803213  0.43466245 0.33078483
-  0.86852099 0.19772911 0.93022647 0.45253585]
- [0.5810785  0.94823368 0.73379189 0.09408163 0.14549142 0.8353591
-  0.46754435 0.29732036 0.7698352  0.39200159]
- [0.77265782 0.37376184 0.43647835 0.38782352 0.97449977 0.02276062
-  0.36802977 0.43941514 0.99006712 0.98316168]
- [0.89897156 0.04956816 0.52067151 0.52180619 0.21275991 0.86420934
-  0.50653545 0.49057373 0.77067609 0.16043757]
- [0.22616902 0.70408916 0.43902948 0.68992377 0.5608253  0.84132082
-  0.95661705 0.7082333  0.33600213 0.44116407]
- [0.96459246 0.32141575 0.95679388 0.44595818 0.1875353  0.47700752
-  0.03321947 0.55865092 0.96543101 0.63227278]]
+
    [[0.36681298 0.62199022 0.32023229 0.08231145 0.09917246 0.29025302
+  0.85115237 0.79516409 0.833774   0.85910255]
+ [0.08990571 0.56425249 0.34440086 0.37540613 0.30085673 0.93901621
+  0.00790262 0.92604308 0.7742213  0.58486384]
+ [0.57811941 0.84268865 0.11339075 0.57329374 0.78094722 0.46156624
+  0.2545724  0.1095957  0.6559956  0.22291364]
+ [0.12049203 0.50172141 0.38493367 0.62633359 0.13880371 0.37092452
+  0.21913628 0.78478186 0.12625715 0.89142357]
+ [0.69018454 0.02200532 0.63281889 0.28622606 0.84900747 0.44440345
+  0.5302517  0.15957051 0.10154612 0.7025846 ]
+ [0.26518597 0.48577692 0.68736603 0.10401756 0.88473534 0.31949465
+  0.00434364 0.20240089 0.46285399 0.64432571]
+ [0.39229856 0.66289428 0.2591811  0.68871199 0.37021881 0.32041353
+  0.93049763 0.30690504 0.63212587 0.56397327]
+ [0.31212802 0.82402448 0.94610136 0.19407473 0.28535441 0.99933329
+  0.55331574 0.96439516 0.4809676  0.21947455]
+ [0.66489687 0.7613959  0.33285905 0.20726939 0.74587018 0.97375628
+  0.62642303 0.95762994 0.22169909 0.82717721]
+ [0.8470287  0.16761991 0.74042291 0.42143986 0.70262543 0.8964059
+  0.69629255 0.05916189 0.89940861 0.19010909]]
 
    
 
    
 
    
@@ -2115,13 +2125,13 @@ covariance matrix through the np.linalg.eig() function.
    
 
    
 
    
 
    
-
    -0.022210866177877393
-3.7588118737641243
-0.5615739502773949
-[[ 1.30150056  3.81953844  7.0377961 ]
- [ 3.81953844 12.2361161  19.72546953]
- [ 7.0377961  19.72546953 73.31248389]]
-[79.90960269  0.08394792  6.85654993]
+
    -0.1804736801658276
+3.577421319924605
+-0.13894606338836166
+[[ 0.93900613  3.09173024  2.71615146]
+ [ 3.09173024 11.22689573  9.13261905]
+ [ 2.71615146  9.13261905 12.06931309]]
+[21.60398689  0.07438088  2.55684718]
 
    
 
    
 
    
@@ -2344,7 +2354,7 @@ Name: Aragorn, dtype: object
 
    
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31718/1326197715.py:6: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11068/1326197715.py:6: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
   data_pandas=data_pandas.append(pd.DataFrame(new_hobbit, index=['Pippin']))
 
    
 
    
diff --git a/doc/LectureNotes/_build/html/week35.html b/doc/LectureNotes/_build/html/week35.html
index 3dcc25cbf..cb75d3083 100644
--- a/doc/LectureNotes/_build/html/week35.html
+++ b/doc/LectureNotes/_build/html/week35.html
@@ -333,6 +333,16 @@ const thebe_selector_output = ".output, .cell_output"
    Week 42 Constructing a Neural Network code with introduction to Tensor flow
   
  
+ 
  • 
+  
+   Exercises weeks 43 and 44
+  
+ 
    • 
+ 
  • 
+  
+   Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations
+  
+ 
    • 
 
 
    
  
@@ -1696,7 +1706,7 @@ Since we are not using Scikit-Learn here we can define our own
 
    
 
    
 
    
-
    0.995840825550726
+
    0.9955273625597437
 
    
 
    
 
    
@@ -1713,7 +1723,7 @@ Since we are not using Scikit-Learn here we can define our own
 
    
 
    
 
    
-
    0.007607459165915922
+
    0.008900933315885705
 
    
 
    
 
    
@@ -1728,23 +1738,23 @@ Since we are not using Scikit-Learn here we can define our own
 
    
 
    
 
    
-
    [0.00552246 0.00115669 0.02017377 0.01896127 0.01389847 0.01591021
- 0.02816083 0.0100949  0.02757522 0.00182747 0.00584432 0.02081274
- 0.01365363 0.0191717  0.01068907 0.02763182 0.02950229 0.02485679
- 0.01456159 0.00644939 0.0297291  0.08272096 0.01335857 0.00825399
- 0.04478101 0.05834444 0.04198166 0.034985   0.00562524 0.01524072
- 0.01529708 0.02083512 0.01161357 0.01708691 0.02279888 0.02912421
- 0.00076617 0.02329285 0.00626773 0.01054509 0.00460405 0.01476097
- 0.0036718  0.00569405 0.07804489 0.03894873 0.02103178 0.00726135
- 0.00353575 0.00857028 0.00923278 0.01616709 0.02881357 0.00550379
- 0.02942218 0.00946636 0.03982972 0.0149713  0.04103307 0.05526765
- 0.00463639 0.00254359 0.00915433 0.02588522 0.00090992 0.00739382
- 0.02075115 0.024632   0.00115506 0.01963203 0.00086063 0.01580414
- 0.01059601 0.03376827 0.02745507 0.02109939 0.05977068 0.04662395
- 0.00283853 0.03903968 0.0001225  0.02385515 0.02089297 0.04214702
- 0.01289962 0.00798188 0.04746791 0.04822955 0.02066371 0.01045774
- 0.01164198 0.03633213 0.00183398 0.0105301  0.00880924 0.015244
- 0.01596986 0.01176096 0.01448147 0.00610607]
+
    [0.00643899 0.04246989 0.0607062  0.02997344 0.0011878  0.00123457
+ 0.00324986 0.03285652 0.01028728 0.01571866 0.03940381 0.0814985
+ 0.038844   0.01828593 0.03967758 0.00303693 0.02227466 0.02702328
+ 0.00026861 0.00883798 0.02876697 0.00472251 0.03141454 0.02911162
+ 0.02387339 0.00585113 0.00110716 0.00587564 0.00693821 0.00604105
+ 0.00804985 0.02058094 0.01151984 0.01782721 0.0286851  0.08874631
+ 0.01678538 0.0065912  0.03611471 0.02706508 0.00313125 0.04977093
+ 0.00415289 0.02760079 0.00518122 0.00628874 0.00739489 0.01619456
+ 0.00996972 0.02210753 0.02030107 0.02100763 0.04699527 0.01512934
+ 0.00717079 0.01784714 0.01095703 0.01281486 0.0231703  0.04482932
+ 0.00287871 0.0565419  0.04028659 0.03102525 0.01617722 0.0271761
+ 0.01736502 0.03394827 0.00328494 0.05750876 0.0059888  0.01915888
+ 0.01423609 0.01024227 0.03660869 0.01012951 0.00534938 0.03375068
+ 0.02699539 0.04083439 0.04965227 0.00565625 0.02250553 0.00893027
+ 0.02244755 0.00741987 0.00189101 0.02042476 0.02036545 0.06362348
+ 0.03145163 0.02987833 0.07393685 0.0033575  0.02791218 0.00214832
+ 0.0111154  0.01344581 0.00368581 0.01436601]
 
    
 
    
 
    
@@ -1813,15 +1823,15 @@ but now splitting the data into a training set and a test set.
    
 
    
 
    
 
    
-
    [ 2.04899609 -0.34193915  5.64527549 -0.69997503  0.31290684]
+
    [ 2.09851217 -1.48209629 10.27096183 -6.79998516  2.87206824]
 Training R2
-0.9952222065466447
+0.9957273060382023
 Training MSE
-0.008897354602673473
+0.010053880703541525
 Test R2
-0.9915165982451293
+0.9888005551376943
 Test MSE
-0.009442796383765939
+0.008043926731954223
 
    
 
    
 
    
diff --git a/doc/LectureNotes/_build/html/week36.html b/doc/LectureNotes/_build/html/week36.html
index 4ff957ced..681ce6e4a 100644
--- a/doc/LectureNotes/_build/html/week36.html
+++ b/doc/LectureNotes/_build/html/week36.html
@@ -333,6 +333,16 @@ const thebe_selector_output = ".output, .cell_output"
    Week 42 Constructing a Neural Network code with introduction to Tensor flow
   
  
+ 
  • 
+  
+   Exercises weeks 43 and 44
+  
+ 
    • 
+ 
  • 
+  
+   Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations
+  
+ 
    • 
 
 
    
  
diff --git a/doc/LectureNotes/_build/html/week37.html b/doc/LectureNotes/_build/html/week37.html
index a322bccac..e822ee4a0 100644
--- a/doc/LectureNotes/_build/html/week37.html
+++ b/doc/LectureNotes/_build/html/week37.html
@@ -333,6 +333,16 @@ const thebe_selector_output = ".output, .cell_output"
    Week 42 Constructing a Neural Network code with introduction to Tensor flow
   
  
+ 
  • 
+  
+   Exercises weeks 43 and 44
+  
+ 
    • 
+ 
  • 
+  
+   Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations
+  
+ 
    • 
 
 
    
  
@@ -1817,7 +1827,7 @@ theorem.
    
 
    
 
    Bootstrap Statistics :
 original           bias      std. error
- 100.115  15.1213        100.117        0.151517
+ 99.9889  15.1336        99.9892        0.150749
 
    
 
    
 
    
@@ -2079,7 +2089,9 @@ Error: 0.026605727637184558
 Bias^2: 0.010018312644139219
 Var: 0.016587414993045335
 0.026605727637184558 >= 0.010018312644139219 + 0.016587414993045335 = 0.026605727637184554
-Polynomial degree: 10
+
    
+
    
+
    Polynomial degree: 10
 Error: 0.021592704588021178
 Bias^2: 0.010516485576646504
 Var: 0.01107621901137467
@@ -2089,23 +2101,19 @@ Error: 0.07160048164232538
 Bias^2: 0.014436800088896381
 Var: 0.05716368155342902
 0.07160048164232538 >= 0.014436800088896381 + 0.05716368155342902 = 0.0716004816423254
-
    
-
    
-
    Polynomial degree: 12
+Polynomial degree: 12
 Error: 0.11547777218876518
 Bias^2: 0.016285782696017142
 Var: 0.09919198949274803
 0.11547777218876518 >= 0.016285782696017142 + 0.09919198949274803 = 0.11547777218876518
-
    
-
    
-
    Polynomial degree: 13
+Polynomial degree: 13
 Error: 0.2284246870217162
 Bias^2: 0.01975416527168255
 Var: 0.20867052175003364
 0.2284246870217162 >= 0.01975416527168255 + 0.20867052175003364 = 0.2284246870217162
 
    
 
    
-_images/week37_162_4.png
+_images/week37_162_3.png
 
    
 
    
 
    
@@ -2558,9 +2566,9 @@ Mean squared error on training data: 0.00063862
 Mean squared error on test data: 3073.63180447
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31736/626635268.py:73: RuntimeWarning: divide by zero encountered in log10
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11090/626635268.py:73: RuntimeWarning: divide by zero encountered in log10
   plt.plot(polynomial, np.log10(trainingerror), label='Training Error')
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31736/626635268.py:74: RuntimeWarning: divide by zero encountered in log10
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11090/626635268.py:74: RuntimeWarning: divide by zero encountered in log10
   plt.plot(polynomial, np.log10(testerror), label='Test Error')
 
    
 
    
@@ -2645,7 +2653,7 @@ Mean squared error on test data: 3073.63180447
 
    
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31736/3817475779.py:63: RuntimeWarning: divide by zero encountered in log10
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11090/3817475779.py:63: RuntimeWarning: divide by zero encountered in log10
   plt.plot(polynomial, np.log10(estimated_mse_sklearn), label='Test Error')
 
    
 
    
diff --git a/doc/LectureNotes/_build/html/week38.html b/doc/LectureNotes/_build/html/week38.html
index 234a63f80..0fc241504 100644
--- a/doc/LectureNotes/_build/html/week38.html
+++ b/doc/LectureNotes/_build/html/week38.html
@@ -333,6 +333,16 @@ const thebe_selector_output = ".output, .cell_output"
    Week 42 Constructing a Neural Network code with introduction to Tensor flow
   
  
+ 
  • 
+  
+   Exercises weeks 43 and 44
+  
+ 
    • 
+ 
  • 
+  
+   Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations
+  
+ 
    • 
 
 
    
  
@@ -2034,7 +2044,7 @@ case under study.
    
 
    
 
    
 
    RandomizedSearchCV(estimator=Ridge(), n_iter=100,
-                   param_distributions={'alpha': <scipy.stats._distn_infrastructure.rv_frozen object at 0x156346610>})
+                   param_distributions={'alpha': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1045a7eb0>})
 Best estimated lambda-value: 0.9849967686928113
 MSE score: 1.0853136633465326
 R2 score: -0.0002382102844775691
diff --git a/doc/LectureNotes/_build/html/week39.html b/doc/LectureNotes/_build/html/week39.html
index bfba01344..456abb5de 100644
--- a/doc/LectureNotes/_build/html/week39.html
+++ b/doc/LectureNotes/_build/html/week39.html
@@ -333,6 +333,16 @@ const thebe_selector_output = ".output, .cell_output"
    Week 42 Constructing a Neural Network code with introduction to Tensor flow
   
  
+ 
  • 
+  
+   Exercises weeks 43 and 44
+  
+ 
    • 
+ 
  • 
+  
+   Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations
+  
+ 
    • 
 
 
    
  
@@ -1798,11 +1808,11 @@ which equals
    
 
    
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31749/3838917029.py:18: MatplotlibDeprecationWarning: Calling gca() with keyword arguments was deprecated in Matplotlib 3.4. Starting two minor releases later, gca() will take no keyword arguments. The gca() function should only be used to get the current axes, or if no axes exist, create new axes with default keyword arguments. To create a new axes with non-default arguments, use plt.axes() or plt.subplot().
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11106/3838917029.py:18: MatplotlibDeprecationWarning: Calling gca() with keyword arguments was deprecated in Matplotlib 3.4. Starting two minor releases later, gca() will take no keyword arguments. The gca() function should only be used to get the current axes, or if no axes exist, create new axes with default keyword arguments. To create a new axes with non-default arguments, use plt.axes() or plt.subplot().
   ax = fig.gca(projection="3d")
 
    
 
    
-
    <mpl_toolkits.mplot3d.art3d.Poly3DCollection at 0x11ada9670>
+
    <mpl_toolkits.mplot3d.art3d.Poly3DCollection at 0x136af27c0>
 
    
 
    
 _images/week39_80_2.png
@@ -1860,7 +1870,7 @@ which equals
    
 
    
 
    
 
    
-
    [<matplotlib.lines.Line2D at 0x11f5f6520>]
+
    [<matplotlib.lines.Line2D at 0x13792dfa0>]
 
    
 
    
 _images/week39_88_1.png
diff --git a/doc/LectureNotes/_build/html/week40.html b/doc/LectureNotes/_build/html/week40.html
index f78b3b9ba..76f5d4541 100644
--- a/doc/LectureNotes/_build/html/week40.html
+++ b/doc/LectureNotes/_build/html/week40.html
@@ -333,6 +333,16 @@ const thebe_selector_output = ".output, .cell_output"
    Week 42 Constructing a Neural Network code with introduction to Tensor flow
   
  
+ 
  • 
+  
+   Exercises weeks 43 and 44
+  
+ 
    • 
+ 
  • 
+  
+   Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations
+  
+ 
    • 
 
 
    
  
@@ -1446,15 +1456,15 @@ function.
    
 
    
 
    
 
    Own inversion
-[[4.42484459]
- [2.65626992]]
-Eigenvalues of Hessian Matrix:[0.30361418 4.06484621]
+[[3.88391015]
+ [3.15024162]]
+Eigenvalues of Hessian Matrix:[0.29734306 4.63081005]
 theta from own gd
-[[4.42484459]
- [2.65626992]]
+[[3.88391015]
+ [3.15024162]]
 theta from own sdg
-[[4.53049637]
- [2.68581655]]
+[[3.92822216]
+ [3.17648722]]
 
    
 
    
 _images/week40_25_1.png
diff --git a/doc/LectureNotes/_build/html/week41.html b/doc/LectureNotes/_build/html/week41.html
index 108ca6ee0..ccc684f57 100644
--- a/doc/LectureNotes/_build/html/week41.html
+++ b/doc/LectureNotes/_build/html/week41.html
@@ -333,6 +333,16 @@ const thebe_selector_output = ".output, .cell_output"
    Week 42 Constructing a Neural Network code with introduction to Tensor flow
   
  
+ 
  • 
+  
+   Exercises weeks 43 and 44
+  
+ 
    • 
+ 
  • 
+  
+   Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations
+  
+ 
    • 
 
 
    
  
@@ -2795,7 +2805,7 @@ the Hadamard product, meaning element-wise multiplication.
    
 
    Old accuracy on training data: 0.1440501043841336
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
    
 
    
@@ -3131,7 +3141,7 @@ Lambda =  10.0
 Accuracy score on test set:  0.19166666666666668
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
    
 
    
@@ -3140,7 +3150,7 @@ Lambda =  1e-05
 Accuracy score on test set:  0.10555555555555556
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
    
 
    
@@ -3149,7 +3159,7 @@ Lambda =  0.0001
 Accuracy score on test set:  0.08611111111111111
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
    
 
    
@@ -3158,7 +3168,7 @@ Lambda =  0.001
 Accuracy score on test set:  0.10555555555555556
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
    
 
    
@@ -3167,7 +3177,7 @@ Lambda =  0.01
 Accuracy score on test set:  0.08888888888888889
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
    
 
    
@@ -3176,7 +3186,7 @@ Lambda =  0.1
 Accuracy score on test set:  0.08611111111111111
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
    
 
    
@@ -3185,7 +3195,7 @@ Lambda =  1.0
 Accuracy score on test set:  0.08888888888888889
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
    
 
    
@@ -3194,11 +3204,11 @@ Lambda =  10.0
 Accuracy score on test set:  0.09166666666666666
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
@@ -3207,11 +3217,11 @@ Lambda =  1e-05
 Accuracy score on test set:  0.07777777777777778
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
@@ -3220,11 +3230,11 @@ Lambda =  0.0001
 Accuracy score on test set:  0.07777777777777778
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
@@ -3233,11 +3243,11 @@ Lambda =  0.001
 Accuracy score on test set:  0.07777777777777778
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
@@ -3246,11 +3256,11 @@ Lambda =  0.01
 Accuracy score on test set:  0.07777777777777778
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
@@ -3259,7 +3269,7 @@ Lambda =  0.1
 Accuracy score on test set:  0.07777777777777778
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
    
 
    
@@ -3268,11 +3278,11 @@ Lambda =  1.0
 Accuracy score on test set:  0.10555555555555556
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
@@ -3281,11 +3291,11 @@ Lambda =  10.0
 Accuracy score on test set:  0.07777777777777778
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
@@ -3294,11 +3304,11 @@ Lambda =  1e-05
 Accuracy score on test set:  0.07777777777777778
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
@@ -3307,11 +3317,11 @@ Lambda =  0.0001
 Accuracy score on test set:  0.07777777777777778
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
@@ -3320,11 +3330,11 @@ Lambda =  0.001
 Accuracy score on test set:  0.07777777777777778
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
@@ -3333,11 +3343,11 @@ Lambda =  0.01
 Accuracy score on test set:  0.07777777777777778
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
@@ -3346,11 +3356,11 @@ Lambda =  0.1
 Accuracy score on test set:  0.07777777777777778
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
@@ -3359,11 +3369,11 @@ Lambda =  1.0
 Accuracy score on test set:  0.07777777777777778
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
@@ -3416,15 +3426,15 @@ Accuracy score on test set:  0.07777777777777778
 
    
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/953065564.py:4: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/953065564.py:4: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/953065564.py:4: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/953065564.py:4: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
    
 
    
@@ -3688,8 +3698,9 @@ Accuracy score on test set:  0.9888888888888889
 
    Learning rate  =  0.01
 Lambda =  1.0
 Accuracy score on test set:  0.9722222222222222
-
-Learning rate  =  0.01
+
    
+
    
+
    Learning rate  =  0.01
 Lambda =  10.0
 Accuracy score on test set:  0.9527777777777777
 
    
@@ -3750,8 +3761,9 @@ Accuracy score on test set:  0.08333333333333333
 
    Learning rate  =  1.0
 Lambda =  1.0
 Accuracy score on test set:  0.08888888888888889
-
-Learning rate  =  1.0
+
    
+
    
+
    Learning rate  =  1.0
 Lambda =  10.0
 Accuracy score on test set:  0.09444444444444444
 
@@ -3771,8 +3783,9 @@ Accuracy score on test set:  0.10555555555555556
 Learning rate  =  10.0
 Lambda =  0.01
 Accuracy score on test set:  0.1388888888888889
-
-Learning rate  =  10.0
+
    
+
    
+
    Learning rate  =  10.0
 Lambda =  0.1
 Accuracy score on test set:  0.11388888888888889
 
    
@@ -4208,8 +4221,9 @@ Accuracy score on data set:  0.5
 Learning rate  =  1.0
 Lambda =  10.0
 Accuracy score on data set:  0.5
-
-Learning rate  =  10.0
+
    
+
    
+
    Learning rate  =  10.0
 Lambda =  1e-05
 Accuracy score on data set:  0.5
 
@@ -4260,7 +4274,7 @@ Accuracy score on data set:  0.5
   warnings.warn(
 
    
 
    
-_images/week41_211_2.png
+_images/week41_211_3.png
 
    
 
    
 
    
diff --git a/doc/LectureNotes/_build/html/week42.html b/doc/LectureNotes/_build/html/week42.html
index 206cfa026..5ce3c33c4 100644
--- a/doc/LectureNotes/_build/html/week42.html
+++ b/doc/LectureNotes/_build/html/week42.html
@@ -55,7 +55,7 @@ const thebe_selector_output = ".output, .cell_output"
     
     
     
-    
+    
     
     
     
@@ -333,6 +333,16 @@ const thebe_selector_output = ".output, .cell_output"
    Week 42 Constructing a Neural Network code with introduction to Tensor flow
   
  
+ 
  • 
+  
+   Exercises weeks 43 and 44
+  
+ 
    • 
+ 
  • 
+  
+   Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations
+  
+ 
    • 
 
 
    
  
@@ -1675,7 +1685,7 @@ the Hadamard product, meaning element-wise multiplication.
    
 
    Old accuracy on training data: 0.1440501043841336
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
    
 
    
@@ -2011,7 +2021,7 @@ Lambda =  10.0
 Accuracy score on test set:  0.19166666666666668
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
    
 
    
@@ -2020,7 +2030,7 @@ Lambda =  1e-05
 Accuracy score on test set:  0.10555555555555556
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
    
 
    
@@ -2029,7 +2039,7 @@ Lambda =  0.0001
 Accuracy score on test set:  0.08611111111111111
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
    
 
    
@@ -2038,7 +2048,7 @@ Lambda =  0.001
 Accuracy score on test set:  0.10555555555555556
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
    
 
    
@@ -2047,7 +2057,7 @@ Lambda =  0.01
 Accuracy score on test set:  0.08888888888888889
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
    
 
    
@@ -2056,7 +2066,7 @@ Lambda =  0.1
 Accuracy score on test set:  0.08611111111111111
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
    
 
    
@@ -2065,7 +2075,7 @@ Lambda =  1.0
 Accuracy score on test set:  0.08888888888888889
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
    
 
    
@@ -2074,11 +2084,11 @@ Lambda =  10.0
 Accuracy score on test set:  0.09166666666666666
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
@@ -2087,11 +2097,11 @@ Lambda =  1e-05
 Accuracy score on test set:  0.07777777777777778
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
@@ -2100,11 +2110,11 @@ Lambda =  0.0001
 Accuracy score on test set:  0.07777777777777778
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
@@ -2113,11 +2123,11 @@ Lambda =  0.001
 Accuracy score on test set:  0.07777777777777778
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
@@ -2126,11 +2136,11 @@ Lambda =  0.01
 Accuracy score on test set:  0.07777777777777778
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
@@ -2139,7 +2149,7 @@ Lambda =  0.1
 Accuracy score on test set:  0.07777777777777778
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
    
 
    
@@ -2148,11 +2158,11 @@ Lambda =  1.0
 Accuracy score on test set:  0.10555555555555556
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
@@ -2161,11 +2171,11 @@ Lambda =  10.0
 Accuracy score on test set:  0.07777777777777778
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
@@ -2174,11 +2184,11 @@ Lambda =  1e-05
 Accuracy score on test set:  0.07777777777777778
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
@@ -2187,11 +2197,11 @@ Lambda =  0.0001
 Accuracy score on test set:  0.07777777777777778
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
@@ -2200,11 +2210,11 @@ Lambda =  0.001
 Accuracy score on test set:  0.07777777777777778
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
@@ -2213,11 +2223,11 @@ Lambda =  0.01
 Accuracy score on test set:  0.07777777777777778
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
@@ -2226,11 +2236,11 @@ Lambda =  0.1
 Accuracy score on test set:  0.07777777777777778
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
@@ -2239,11 +2249,11 @@ Lambda =  1.0
 Accuracy score on test set:  0.07777777777777778
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
@@ -2296,15 +2306,15 @@ Accuracy score on test set:  0.07777777777777778
 
    
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/953065564.py:4: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/953065564.py:4: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/953065564.py:4: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/953065564.py:4: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
    
 
    
@@ -2608,39 +2618,40 @@ Accuracy score on test set:  0.8666666666666667
 Learning rate  =  1.0
 Lambda =  1e-05
 Accuracy score on test set:  0.08611111111111111
+
    
+
    
+
    Learning rate  =  1.0
+Lambda =  0.0001
+Accuracy score on test set:  0.10555555555555556
 
 Learning rate  =  1.0
-Lambda =  0.0001
+Lambda =  0.001
 Accuracy score on test set:  0.10555555555555556
 
    
 
    
 
    Learning rate  =  1.0
-Lambda =  0.001
-Accuracy score on test set:  0.10555555555555556
-
-Learning rate  =  1.0
 Lambda =  0.01
 Accuracy score on test set:  0.17777777777777778
 
 Learning rate  =  1.0
 Lambda =  0.1
 Accuracy score on test set:  0.08333333333333333
+
+Learning rate  =  1.0
+Lambda =  1.0
+Accuracy score on test set:  0.08888888888888889
 
    
 
    
 
    Learning rate  =  1.0
-Lambda =  1.0
-Accuracy score on test set:  0.08888888888888889
-
-Learning rate  =  1.0
 Lambda =  10.0
 Accuracy score on test set:  0.09444444444444444
+
+Learning rate  =  10.0
+Lambda =  1e-05
+Accuracy score on test set:  0.17222222222222222
 
    
 
    
 
    Learning rate  =  10.0
-Lambda =  1e-05
-Accuracy score on test set:  0.17222222222222222
-
-Learning rate  =  10.0
 Lambda =  0.0001
 Accuracy score on test set:  0.11666666666666667
 
@@ -3105,9 +3116,18 @@ Accuracy score on data set:  0.5
 Learning rate  =  10.0
 Lambda =  0.01
 Accuracy score on data set:  0.5
-
    
-
    
-
    Learning rate  = 
+
+Learning rate  =  10.0
+Lambda =  0.1
+Accuracy score on data set:  0.5
+
+Learning rate  =  10.0
+Lambda =  1.0
+Accuracy score on data set:  0.5
+
+Learning rate  =  10.0
+Lambda =  10.0
+Accuracy score on data set:  0.5
 
    
 
    
 
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
@@ -3132,20 +3152,7 @@ Accuracy score on data set:  0.5
   warnings.warn(
 
    
 
    
-
     10.0
-Lambda =  0.1
-Accuracy score on data set:  0.5
-
-Learning rate  =  10.0
-Lambda =  1.0
-Accuracy score on data set:  0.5
-
-Learning rate  =  10.0
-Lambda =  10.0
-Accuracy score on data set:  0.5
-
    
-
    
-_images/week42_88_4.png
+_images/week42_88_2.png
 
    
 
    
 
    
@@ -3829,10 +3836,10 @@ features).
    
             
    Exercises week 42
    
         
    
     
-    
+    
     
    
         
    next
    
-        
    Project 1 on Machine Learning, deadline October 9 (midnight), 2023
    
+        
    Exercises weeks 43 and 44
    
     
    
     
         
diff --git a/doc/LectureNotes/_build/html/week43.html b/doc/LectureNotes/_build/html/week43.html
index a51bd6a44..913e7b465 100644
--- a/doc/LectureNotes/_build/html/week43.html
+++ b/doc/LectureNotes/_build/html/week43.html
@@ -1888,8 +1888,9 @@ Accuracy score on data set:  0.5
 Learning rate  =  0.0001
 Lambda =  1.0
 Accuracy score on data set:  0.5
-
-Learning rate  =  0.0001
+
    
+
    
+
    Learning rate  =  0.0001
 Lambda =  10.0
 Accuracy score on data set:  0.5
 
@@ -2056,7 +2057,7 @@ Accuracy score on data set:  0.5
   warnings.warn(
 
    
 
    
-_images/week43_30_2.png
+_images/week43_30_3.png
 
    
 
    
 
    
@@ -6361,12 +6362,8 @@ case.
    
 
    
 
    
 
    Adam: Eta=0.001, Lambda=0
-
    
-
    
-
    
-
    
-
    
-
      [----------------------------------------] 0.000% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392 
+
+  [----------------------------------------] 0.000% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392 
 
    
 
    
 
      [----------------------------------------] 0.1000% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392 
@@ -18835,7 +18832,9 @@ probabilities sum up to: 1.0
 
    
 
    
 
    predictions = (n_inputs) = (1437,)
-prediction for image 0: 8
+
    
+
    
+
    prediction for image 0: 8
 correct label for image 0: 6
 
    
 
    
diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter1.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapter1.ipynb
index 8b1257c93..007e9499d 100644
--- a/doc/LectureNotes/_build/jupyter_execute/chapter1.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/chapter1.ipynb
@@ -343,7 +343,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "
    "
       ]
@@ -515,7 +515,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "
    "
       ]
@@ -583,18 +583,18 @@
      "output_type": "stream",
      "text": [
       "The intercept alpha: \n",
-      " [2.04828291]\n",
+      " [1.95815651]\n",
       "Coefficient beta : \n",
-      " [[4.85601654]]\n",
-      "Mean squared error: 0.27\n",
-      "Variance score: 0.89\n",
+      " [[5.03219974]]\n",
+      "Mean squared error: 0.26\n",
+      "Variance score: 0.90\n",
       "Mean squared log error: 0.01\n",
-      "Mean absolute error: 0.40\n"
+      "Mean absolute error: 0.41\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "
    "
       ]
@@ -822,7 +822,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "
    "
       ]
@@ -838,7 +838,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.005000000000000001\n"
+      "0.004999999999999996\n"
      ]
     }
    ],
diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter10.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapter10.ipynb
index 2ae901d83..7fefc49e8 100644
--- a/doc/LectureNotes/_build/jupyter_execute/chapter10.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/chapter10.ipynb
@@ -1077,7 +1077,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -1655,7 +1655,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -1673,7 +1673,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -1691,7 +1691,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -1709,7 +1709,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -1727,7 +1727,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -1745,7 +1745,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -1763,7 +1763,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -1781,11 +1781,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -1803,11 +1803,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -1825,11 +1825,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -1847,11 +1847,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -1869,11 +1869,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -1891,7 +1891,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -1909,11 +1909,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -1931,11 +1931,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -1953,11 +1953,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -1975,11 +1975,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -1997,11 +1997,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -2019,11 +2019,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -2041,11 +2041,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -2063,22 +2063,25 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10904/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  10.0\n",
-      "Lambda =  10.0\n",
-      "Accuracy score on test set:  0.07777777777777778\n",
-      "\n"
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "Input \u001b[0;32mIn [8]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m j, lmbd \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(lmbd_vals):\n\u001b[1;32m      9\u001b[0m     dnn \u001b[38;5;241m=\u001b[39m NeuralNetwork(X_train, Y_train_onehot, eta\u001b[38;5;241m=\u001b[39meta, lmbd\u001b[38;5;241m=\u001b[39mlmbd, epochs\u001b[38;5;241m=\u001b[39mepochs, batch_size\u001b[38;5;241m=\u001b[39mbatch_size,\n\u001b[1;32m     10\u001b[0m                         n_hidden_neurons\u001b[38;5;241m=\u001b[39mn_hidden_neurons, n_categories\u001b[38;5;241m=\u001b[39mn_categories)\n\u001b[0;32m---> 11\u001b[0m     \u001b[43mdnn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     13\u001b[0m     DNN_numpy[i][j] \u001b[38;5;241m=\u001b[39m dnn\n\u001b[1;32m     15\u001b[0m     test_predict \u001b[38;5;241m=\u001b[39m dnn\u001b[38;5;241m.\u001b[39mpredict(X_test)\n",
+      "Input \u001b[0;32mIn [6]\u001b[0m, in \u001b[0;36mNeuralNetwork.train\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     95\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mX_data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mX_data_full[chosen_datapoints]\n\u001b[1;32m     96\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mY_data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mY_data_full[chosen_datapoints]\n\u001b[0;32m---> 98\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfeed_forward\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     99\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbackpropagation()\n",
+      "Input \u001b[0;32mIn [6]\u001b[0m, in \u001b[0;36mNeuralNetwork.feed_forward\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     36\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfeed_forward\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m     37\u001b[0m     \u001b[38;5;66;03m# feed-forward for training\u001b[39;00m\n\u001b[0;32m---> 38\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mz_h \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmatmul\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mX_data\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhidden_weights\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhidden_bias\n\u001b[1;32m     39\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39ma_h \u001b[38;5;241m=\u001b[39m sigmoid(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mz_h)\n\u001b[1;32m     41\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mz_o \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mmatmul(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39ma_h, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moutput_weights) \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moutput_bias\n",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
      ]
     }
    ],
@@ -2123,52 +2126,7 @@
     "collapsed": false,
     "editable": true
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
-      "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
-      "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
-      "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
-      "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31563/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
-      "  return 1/(1 + np.exp(-x))\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "
    "
-      ]
-     },
-     "metadata": {
-      "filenames": {
-       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter10_59_2.png"
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# visual representation of grid search\n",
     "# uses seaborn heatmap, you can also do this with matplotlib imshow\n",
@@ -2235,602 +2193,7 @@
     "collapsed": false,
     "editable": true
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  1e-05\n",
-      "Lambda =  1e-05\n",
-      "Accuracy score on test set:  0.18333333333333332\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  1e-05\n",
-      "Lambda =  0.0001\n",
-      "Accuracy score on test set:  0.18611111111111112\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  1e-05\n",
-      "Lambda =  0.001\n",
-      "Accuracy score on test set:  0.13055555555555556\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  1e-05\n",
-      "Lambda =  0.01\n",
-      "Accuracy score on test set:  0.24444444444444444\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  1e-05\n",
-      "Lambda =  0.1\n",
-      "Accuracy score on test set:  0.23333333333333334\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  1e-05\n",
-      "Lambda =  1.0\n",
-      "Accuracy score on test set:  0.12777777777777777\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  1e-05\n",
-      "Lambda =  10.0\n",
-      "Accuracy score on test set:  0.1527777777777778\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  0.0001\n",
-      "Lambda =  1e-05\n",
-      "Accuracy score on test set:  0.9111111111111111\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  0.0001\n",
-      "Lambda =  0.0001\n",
-      "Accuracy score on test set:  0.8888888888888888\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  0.0001\n",
-      "Lambda =  0.001\n",
-      "Accuracy score on test set:  0.8722222222222222\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  0.0001\n",
-      "Lambda =  0.01\n",
-      "Accuracy score on test set:  0.8305555555555556\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  0.0001\n",
-      "Lambda =  0.1\n",
-      "Accuracy score on test set:  0.8888888888888888\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  0.0001\n",
-      "Lambda =  1.0\n",
-      "Accuracy score on test set:  0.8805555555555555\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  0.0001\n",
-      "Lambda =  10.0\n",
-      "Accuracy score on test set:  0.8944444444444445\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  0.001\n",
-      "Lambda =  1e-05\n",
-      "Accuracy score on test set:  0.975\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  0.001\n",
-      "Lambda =  0.0001\n",
-      "Accuracy score on test set:  0.9777777777777777\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  0.001\n",
-      "Lambda =  0.001\n",
-      "Accuracy score on test set:  0.9805555555555555\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  0.001\n",
-      "Lambda =  0.01\n",
-      "Accuracy score on test set:  0.9861111111111112\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  0.001\n",
-      "Lambda =  0.1\n",
-      "Accuracy score on test set:  0.9805555555555555\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  0.001\n",
-      "Lambda =  1.0\n",
-      "Accuracy score on test set:  0.9777777777777777\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  0.001\n",
-      "Lambda =  10.0\n",
-      "Accuracy score on test set:  0.9444444444444444\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  0.01\n",
-      "Lambda =  1e-05\n",
-      "Accuracy score on test set:  0.9861111111111112\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  0.01\n",
-      "Lambda =  0.0001\n",
-      "Accuracy score on test set:  0.9888888888888889\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  0.01\n",
-      "Lambda =  0.001\n",
-      "Accuracy score on test set:  0.9888888888888889\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  0.01\n",
-      "Lambda =  0.01\n",
-      "Accuracy score on test set:  0.9861111111111112\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  0.01\n",
-      "Lambda =  0.1\n",
-      "Accuracy score on test set:  0.9888888888888889\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  0.01\n",
-      "Lambda =  1.0\n",
-      "Accuracy score on test set:  0.9722222222222222\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  0.01\n",
-      "Lambda =  10.0\n",
-      "Accuracy score on test set:  0.9527777777777777\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  0.1\n",
-      "Lambda =  1e-05\n",
-      "Accuracy score on test set:  0.9027777777777778\n",
-      "\n",
-      "Learning rate  =  0.1\n",
-      "Lambda =  0.0001\n",
-      "Accuracy score on test set:  0.8583333333333333\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  0.1\n",
-      "Lambda =  0.001\n",
-      "Accuracy score on test set:  0.8722222222222222\n",
-      "\n",
-      "Learning rate  =  0.1\n",
-      "Lambda =  0.01\n",
-      "Accuracy score on test set:  0.9055555555555556\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  0.1\n",
-      "Lambda =  0.1\n",
-      "Accuracy score on test set:  0.8805555555555555\n",
-      "\n",
-      "Learning rate  =  0.1\n",
-      "Lambda =  1.0\n",
-      "Accuracy score on test set:  0.8722222222222222\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  0.1\n",
-      "Lambda =  10.0\n",
-      "Accuracy score on test set:  0.8666666666666667\n",
-      "\n",
-      "Learning rate  =  1.0\n",
-      "Lambda =  1e-05\n",
-      "Accuracy score on test set:  0.08611111111111111\n",
-      "\n",
-      "Learning rate  =  1.0\n",
-      "Lambda =  0.0001\n",
-      "Accuracy score on test set:  0.10555555555555556\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  1.0\n",
-      "Lambda =  0.001\n",
-      "Accuracy score on test set:  0.10555555555555556\n",
-      "\n",
-      "Learning rate  =  1.0\n",
-      "Lambda =  0.01\n",
-      "Accuracy score on test set:  0.17777777777777778\n",
-      "\n",
-      "Learning rate  =  1.0\n",
-      "Lambda =  0.1\n",
-      "Accuracy score on test set:  0.08333333333333333\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  1.0\n",
-      "Lambda =  1.0\n",
-      "Accuracy score on test set:  0.08888888888888889\n",
-      "\n",
-      "Learning rate  =  1.0\n",
-      "Lambda =  10.0\n",
-      "Accuracy score on test set:  0.09444444444444444\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  10.0\n",
-      "Lambda =  1e-05\n",
-      "Accuracy score on test set:  0.17222222222222222\n",
-      "\n",
-      "Learning rate  =  10.0\n",
-      "Lambda =  0.0001\n",
-      "Accuracy score on test set:  0.11666666666666667\n",
-      "\n",
-      "Learning rate  =  10.0\n",
-      "Lambda =  0.001\n",
-      "Accuracy score on test set:  0.10555555555555556\n",
-      "\n",
-      "Learning rate  =  10.0\n",
-      "Lambda =  0.01\n",
-      "Accuracy score on test set:  0.1388888888888889\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  10.0\n",
-      "Lambda =  0.1\n",
-      "Accuracy score on test set:  0.11388888888888889\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  10.0\n",
-      "Lambda =  1.0\n",
-      "Accuracy score on test set:  0.10555555555555556\n",
-      "\n",
-      "Learning rate  =  10.0\n",
-      "Lambda =  10.0\n",
-      "Accuracy score on test set:  0.09444444444444444\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from sklearn.neural_network import MLPClassifier\n",
     "# store models for later use\n",
@@ -2868,36 +2231,7 @@
     "collapsed": false,
     "editable": true
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "
    "
-      ]
-     },
-     "metadata": {
-      "filenames": {
-       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter10_63_1.png"
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# optional\n",
     "# visual representation of grid search\n",
@@ -2987,16 +2321,7 @@
     "collapsed": false,
     "editable": true
    },
-   "outputs": [
-    {
-     "ename": "SyntaxError",
-     "evalue": "invalid syntax (2259440937.py, line 1)",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;36m  Input \u001b[0;32mIn [12]\u001b[0;36m\u001b[0m\n\u001b[0;31m    conda create -n tf tensorflow\u001b[0m\n\u001b[0m          ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "conda create -n tf tensorflow\n",
     "conda activate tf"
diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter11.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapter11.ipynb
index d7106d882..1fd483e8f 100644
--- a/doc/LectureNotes/_build/jupyter_execute/chapter11.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/chapter11.ipynb
@@ -3023,10 +3023,7 @@
       "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/wrap_util.py:20\u001b[0m, in \u001b[0;36munary_to_nary..nary_operator..nary_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m     19\u001b[0m     x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtuple\u001b[39m(args[i] \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m argnum)\n\u001b[0;32m---> 20\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43munary_operator\u001b[49m\u001b[43m(\u001b[49m\u001b[43munary_f\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mnary_op_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mnary_op_kwargs\u001b[49m\u001b[43m)\u001b[49m\n",
       "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/differential_operators.py:29\u001b[0m, in \u001b[0;36mgrad\u001b[0;34m(fun, x)\u001b[0m\n\u001b[1;32m     26\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m vspace(ans)\u001b[38;5;241m.\u001b[39msize \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m     27\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mGrad only applies to real scalar-output functions. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     28\u001b[0m                     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTry jacobian, elementwise_grad or holomorphic_grad.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m---> 29\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mvjp\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvspace\u001b[49m\u001b[43m(\u001b[49m\u001b[43mans\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mones\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n",
       "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:14\u001b[0m, in \u001b[0;36mmake_vjp..vjp\u001b[0;34m(g)\u001b[0m\n\u001b[0;32m---> 14\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mvjp\u001b[39m(g): \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mbackward_pass\u001b[49m\u001b[43m(\u001b[49m\u001b[43mg\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mend_node\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:23\u001b[0m, in \u001b[0;36mbackward_pass\u001b[0;34m(g, end_node)\u001b[0m\n\u001b[1;32m     21\u001b[0m     ingrads \u001b[38;5;241m=\u001b[39m node\u001b[38;5;241m.\u001b[39mvjp(outgrad[\u001b[38;5;241m0\u001b[39m])\n\u001b[1;32m     22\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m parent, ingrad \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(node\u001b[38;5;241m.\u001b[39mparents, ingrads):\n\u001b[0;32m---> 23\u001b[0m         outgrads[parent] \u001b[38;5;241m=\u001b[39m \u001b[43madd_outgrads\u001b[49m\u001b[43m(\u001b[49m\u001b[43moutgrads\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[43mparent\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mingrad\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     24\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m outgrad[\u001b[38;5;241m0\u001b[39m]\n",
-      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:165\u001b[0m, in \u001b[0;36madd_outgrads\u001b[0;34m(prev_g_flagged, g)\u001b[0m\n\u001b[1;32m    163\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m mutable:\n\u001b[1;32m    164\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m sparse:\n\u001b[0;32m--> 165\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43msparse_add\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprev_g\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mg\u001b[49m\u001b[43m)\u001b[49m, \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m    166\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    167\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m vs\u001b[38;5;241m.\u001b[39mmut_add(prev_g, g), \u001b[38;5;28;01mTrue\u001b[39;00m\n",
-      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/tracer.py:48\u001b[0m, in \u001b[0;36mprimitive..f_wrapped\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     46\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m new_box(ans, trace, node)\n\u001b[1;32m     47\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 48\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mf_raw\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:186\u001b[0m, in \u001b[0;36msparse_add\u001b[0;34m(vs, x_prev, x_new)\u001b[0m\n\u001b[1;32m    183\u001b[0m \u001b[38;5;129m@primitive\u001b[39m\n\u001b[1;32m    184\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21msparse_add\u001b[39m(vs, x_prev, x_new):\n\u001b[1;32m    185\u001b[0m     x_prev \u001b[38;5;241m=\u001b[39m x_prev \u001b[38;5;28;01mif\u001b[39;00m x_prev \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m vs\u001b[38;5;241m.\u001b[39mzeros()\n\u001b[0;32m--> 186\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mx_new\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmut_add\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx_prev\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:23\u001b[0m, in \u001b[0;36mbackward_pass\u001b[0;34m(g, end_node)\u001b[0m\n\u001b[1;32m     21\u001b[0m     ingrads \u001b[38;5;241m=\u001b[39m node\u001b[38;5;241m.\u001b[39mvjp(outgrad[\u001b[38;5;241m0\u001b[39m])\n\u001b[1;32m     22\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m parent, ingrad \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(node\u001b[38;5;241m.\u001b[39mparents, ingrads):\n\u001b[0;32m---> 23\u001b[0m         outgrads[parent] \u001b[38;5;241m=\u001b[39m add_outgrads(\u001b[43moutgrads\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[43mparent\u001b[49m\u001b[43m)\u001b[49m, ingrad)\n\u001b[1;32m     24\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m outgrad[\u001b[38;5;241m0\u001b[39m]\n",
       "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
      ]
     }
diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter12.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapter12.ipynb
index 9a859e0d1..8e8b20081 100644
--- a/doc/LectureNotes/_build/jupyter_execute/chapter12.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/chapter12.ipynb
@@ -1382,7 +1382,7 @@
      "text": [
       "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/keras/optimizer_v2/gradient_descent.py:102: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
       "  super(SGD, self).__init__(name, **kwargs)\n",
-      "2023-10-15 21:48:58.909327: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n"
+      "2023-10-25 15:31:33.965944: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n"
      ]
     },
     {
diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter13.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapter13.ipynb
index 7d8805c35..ca55534bf 100644
--- a/doc/LectureNotes/_build/jupyter_execute/chapter13.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/chapter13.ipynb
@@ -61,7 +61,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "
    "
       ]
@@ -196,14 +196,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023-10-15 21:49:35.228942: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n"
+      "2023-10-25 15:32:11.077734: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 3s - loss: 0.5276 - 3s/epoch - 66ms/step\n"
+      "50/50 - 3s - loss: 1.4222 - 3s/epoch - 66ms/step\n"
      ]
     },
     {
@@ -217,7 +217,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.4234 - 459ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.5274 - 458ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -231,7 +231,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.4043 - 459ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.4426 - 460ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -245,7 +245,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.4010 - 460ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.4375 - 459ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -259,7 +259,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3979 - 456ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.4336 - 457ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -273,7 +273,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3967 - 459ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.4310 - 461ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -287,7 +287,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3962 - 456ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.4287 - 454ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -301,7 +301,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3957 - 455ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.4277 - 460ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -315,7 +315,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3929 - 456ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.4266 - 458ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -329,7 +329,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3920 - 456ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.4253 - 457ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -343,7 +343,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3918 - 454ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.4236 - 461ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -357,7 +357,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3898 - 456ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.4221 - 459ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -371,7 +371,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3921 - 456ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.4208 - 461ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -385,7 +385,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3911 - 455ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.4199 - 462ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -399,7 +399,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3888 - 456ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.4203 - 468ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -413,7 +413,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3871 - 456ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.4181 - 457ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -427,7 +427,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3894 - 458ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.4177 - 460ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -441,7 +441,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3873 - 455ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.4162 - 458ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -455,7 +455,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3855 - 456ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.4148 - 463ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -469,7 +469,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3871 - 453ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.4146 - 460ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -483,7 +483,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3802 - 455ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.4137 - 460ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -497,7 +497,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3856 - 455ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.4128 - 460ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -511,7 +511,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3804 - 453ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.4130 - 462ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -525,7 +525,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3842 - 453ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.4106 - 457ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -539,7 +539,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3815 - 452ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.4099 - 456ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -553,7 +553,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3782 - 454ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.4100 - 463ms/epoch - 9ms/step\n"
      ]
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     {
@@ -567,7 +567,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3798 - 454ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.4086 - 456ms/epoch - 9ms/step\n"
      ]
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     {
@@ -581,7 +581,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3804 - 456ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.4088 - 458ms/epoch - 9ms/step\n"
      ]
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     {
@@ -595,7 +595,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3812 - 452ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.4078 - 462ms/epoch - 9ms/step\n"
      ]
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@@ -609,7 +609,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3780 - 454ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.4063 - 465ms/epoch - 9ms/step\n"
      ]
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     {
@@ -623,7 +623,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3800 - 453ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.4054 - 455ms/epoch - 9ms/step\n"
      ]
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     {
@@ -637,7 +637,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3767 - 467ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.4054 - 464ms/epoch - 9ms/step\n"
      ]
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     {
@@ -651,7 +651,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3787 - 493ms/epoch - 10ms/step\n"
+      "50/50 - 0s - loss: 0.4048 - 456ms/epoch - 9ms/step\n"
      ]
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@@ -665,7 +665,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3758 - 464ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.4051 - 454ms/epoch - 9ms/step\n"
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@@ -679,7 +679,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3784 - 459ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.4027 - 458ms/epoch - 9ms/step\n"
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@@ -693,7 +693,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3766 - 461ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.4008 - 460ms/epoch - 9ms/step\n"
      ]
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     {
@@ -707,7 +707,7 @@
      "name": "stdout",
      "output_type": "stream",
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-      "50/50 - 0s - loss: 0.3733 - 456ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.4011 - 463ms/epoch - 9ms/step\n"
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@@ -721,7 +721,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3749 - 455ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.4024 - 458ms/epoch - 9ms/step\n"
      ]
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@@ -735,7 +735,7 @@
      "name": "stdout",
      "output_type": "stream",
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+      "50/50 - 0s - loss: 0.4014 - 455ms/epoch - 9ms/step\n"
      ]
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     {
@@ -749,7 +749,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3737 - 458ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.4001 - 455ms/epoch - 9ms/step\n"
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     {
@@ -763,7 +763,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3743 - 459ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3992 - 459ms/epoch - 9ms/step\n"
      ]
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     {
@@ -777,7 +777,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3730 - 460ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3990 - 459ms/epoch - 9ms/step\n"
      ]
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     {
@@ -791,7 +791,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3706 - 457ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3990 - 459ms/epoch - 9ms/step\n"
      ]
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     {
@@ -805,7 +805,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3724 - 459ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3975 - 453ms/epoch - 9ms/step\n"
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     {
@@ -819,7 +819,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3716 - 456ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3972 - 457ms/epoch - 9ms/step\n"
      ]
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     {
@@ -833,7 +833,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3713 - 459ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3948 - 460ms/epoch - 9ms/step\n"
      ]
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     {
@@ -847,7 +847,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3705 - 458ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3954 - 458ms/epoch - 9ms/step\n"
      ]
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     {
@@ -861,7 +861,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3703 - 460ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3931 - 462ms/epoch - 9ms/step\n"
      ]
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     {
@@ -875,7 +875,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3701 - 459ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3946 - 461ms/epoch - 9ms/step\n"
      ]
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     {
@@ -889,7 +889,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3676 - 470ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3940 - 458ms/epoch - 9ms/step\n"
      ]
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     {
@@ -903,7 +903,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3679 - 464ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3935 - 457ms/epoch - 9ms/step\n"
      ]
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     {
@@ -917,7 +917,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3689 - 460ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3932 - 458ms/epoch - 9ms/step\n"
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diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter13_3_0.png b/doc/LectureNotes/_build/jupyter_execute/chapter13_3_0.png
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diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter2.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapter2.ipynb
index 83fefb15f..198ca1479 100644
--- a/doc/LectureNotes/_build/jupyter_execute/chapter2.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/chapter2.ipynb
@@ -1798,10 +1798,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.12765651865754318\n",
-      "4.348676117830458\n",
-      "[[0.98073929 2.88442538]\n",
-      " [2.88442538 9.24128917]]\n"
+      "0.023888460698069384\n",
+      "4.161573669199933\n",
+      "[[0.708589   2.01323615]\n",
+      " [2.01323615 6.75406265]]\n"
      ]
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@@ -1845,10 +1845,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.08652153831327969\n",
-      "1.7893215781870513\n",
-      "[[1.         0.70344416]\n",
-      " [0.70344416 1.        ]]\n"
+      "0.08737007811453563\n",
+      "1.792898603630095\n",
+      "[[1.         0.65673455]\n",
+      " [0.65673455 1.        ]]\n"
      ]
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@@ -1905,30 +1905,30 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[ 1.71727268  5.22388434]\n",
-      " [ 0.31027702  0.17469167]\n",
-      " [-0.26149831 -0.93082933]\n",
-      " [ 0.04107874  1.47244548]\n",
-      " [-2.10812381 -5.28818554]\n",
-      " [-1.62910047 -4.07706814]\n",
-      " [ 0.92136836  2.27309401]\n",
-      " [-0.3175938  -1.42457498]\n",
-      " [ 0.68037392  0.16481217]\n",
-      " [ 0.64594566  2.41173033]]\n",
+      "[[ 0.1036544   0.17432695]\n",
+      " [ 0.51758232  1.25700419]\n",
+      " [-1.37236385 -4.22805937]\n",
+      " [-0.46766277 -1.52501047]\n",
+      " [ 0.99175336  3.59187177]\n",
+      " [-2.34718587 -5.3512747 ]\n",
+      " [ 1.94980712  6.99450177]\n",
+      " [ 0.24849282 -0.56424167]\n",
+      " [-0.35408251 -2.3930301 ]\n",
+      " [ 0.73000497  2.04391163]]\n",
       "          0         1\n",
-      "0  1.717273  5.223884\n",
-      "1  0.310277  0.174692\n",
-      "2 -0.261498 -0.930829\n",
-      "3  0.041079  1.472445\n",
-      "4 -2.108124 -5.288186\n",
-      "5 -1.629100 -4.077068\n",
-      "6  0.921368  2.273094\n",
-      "7 -0.317594 -1.424575\n",
-      "8  0.680374  0.164812\n",
-      "9  0.645946  2.411730\n",
+      "0  0.103654  0.174327\n",
+      "1  0.517582  1.257004\n",
+      "2 -1.372364 -4.228059\n",
+      "3 -0.467663 -1.525010\n",
+      "4  0.991753  3.591872\n",
+      "5 -2.347186 -5.351275\n",
+      "6  1.949807  6.994502\n",
+      "7  0.248493 -0.564242\n",
+      "8 -0.354083 -2.393030\n",
+      "9  0.730005  2.043912\n",
       "          0         1\n",
-      "0  1.000000  0.962653\n",
-      "1  0.962653  1.000000\n"
+      "0  1.000000  0.966337\n",
+      "1  0.966337  1.000000\n"
      ]
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    ],
@@ -1974,37 +1974,37 @@
      "text": [
       "     0         1         2         3         4         5         6         7   \\\n",
       "0   0.0  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
-      "1   0.0  0.082246  0.081621  0.082225  0.081617  0.081120  0.073421  0.072973   \n",
-      "2   0.0  0.081621  0.081679  0.081960  0.081804  0.081742  0.073498  0.073387   \n",
-      "3   0.0  0.082225  0.081960  0.087271  0.086900  0.086636  0.081057  0.080755   \n",
-      "4   0.0  0.081617  0.081804  0.086900  0.086868  0.086932  0.080935  0.080903   \n",
-      "5   0.0  0.081120  0.081742  0.086636  0.086932  0.087311  0.080906  0.081136   \n",
-      "6   0.0  0.073421  0.073498  0.081057  0.080935  0.080906  0.077455  0.077330   \n",
-      "7   0.0  0.072973  0.073387  0.080755  0.080903  0.081136  0.077330  0.077429   \n",
-      "8   0.0  0.072637  0.073376  0.080571  0.080980  0.081466  0.077313  0.077630   \n",
-      "9   0.0  0.072410  0.073465  0.080502  0.081164  0.081896  0.077403  0.077931   \n",
-      "10  0.0  0.064640  0.064948  0.073406  0.073471  0.073618  0.071662  0.071685   \n",
-      "11  0.0  0.064320  0.064896  0.073187  0.073476  0.073840  0.071579  0.071792   \n",
-      "12  0.0  0.064101  0.064938  0.073080  0.073586  0.074161  0.071601  0.072000   \n",
-      "13  0.0  0.063980  0.065069  0.073079  0.073797  0.074577  0.071726  0.072305   \n",
-      "14  0.0  0.063953  0.065289  0.073184  0.074108  0.075089  0.071951  0.072707   \n",
+      "1   0.0  0.084489  0.079202  0.083269  0.079700  0.076331  0.074355  0.071456   \n",
+      "2   0.0  0.079202  0.075352  0.079849  0.077042  0.074328  0.072527  0.070086   \n",
+      "3   0.0  0.083269  0.079849  0.087761  0.084946  0.082203  0.081779  0.079170   \n",
+      "4   0.0  0.079700  0.077042  0.084946  0.082590  0.080248  0.079820  0.077517   \n",
+      "5   0.0  0.076331  0.074328  0.082203  0.080248  0.078258  0.077833  0.075804   \n",
+      "6   0.0  0.074355  0.072527  0.081779  0.079820  0.077833  0.078423  0.076337   \n",
+      "7   0.0  0.071456  0.070086  0.079170  0.077517  0.075804  0.076337  0.074477   \n",
+      "8   0.0  0.068743  0.067765  0.076678  0.075294  0.073824  0.074307  0.072650   \n",
+      "9   0.0  0.066200  0.065559  0.074301  0.073154  0.071901  0.072338  0.070865   \n",
+      "10  0.0  0.065631  0.064814  0.074306  0.072976  0.071564  0.072718  0.071080   \n",
+      "11  0.0  0.063225  0.062696  0.071960  0.070845  0.069629  0.070705  0.069239   \n",
+      "12  0.0  0.060971  0.060691  0.069733  0.068809  0.067769  0.068771  0.067462   \n",
+      "13  0.0  0.058856  0.058793  0.067619  0.066865  0.065984  0.066919  0.065753   \n",
+      "14  0.0  0.056870  0.056996  0.065613  0.065012  0.064275  0.065147  0.064113   \n",
       "\n",
       "          8         9         10        11        12        13        14  \n",
       "0   0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  \n",
-      "1   0.072637  0.072410  0.064640  0.064320  0.064101  0.063980  0.063953  \n",
-      "2   0.073376  0.073465  0.064948  0.064896  0.064938  0.065069  0.065289  \n",
-      "3   0.080571  0.080502  0.073406  0.073187  0.073080  0.073079  0.073184  \n",
-      "4   0.080980  0.081164  0.073471  0.073476  0.073586  0.073797  0.074108  \n",
-      "5   0.081466  0.081896  0.073618  0.073840  0.074161  0.074577  0.075089  \n",
-      "6   0.077313  0.077403  0.071662  0.071579  0.071601  0.071726  0.071951  \n",
-      "7   0.077630  0.077931  0.071685  0.071792  0.072000  0.072305  0.072707  \n",
-      "8   0.078041  0.078548  0.071805  0.072098  0.072486  0.072967  0.073541  \n",
-      "9   0.078548  0.079255  0.072022  0.072495  0.073059  0.073712  0.074455  \n",
-      "10  0.071805  0.072022  0.067420  0.067457  0.067591  0.067820  0.068141  \n",
-      "11  0.072098  0.072495  0.067457  0.067660  0.067955  0.068340  0.068815  \n",
-      "12  0.072486  0.073059  0.067591  0.067955  0.068407  0.068945  0.069570  \n",
-      "13  0.072967  0.073712  0.067820  0.068340  0.068945  0.069634  0.070406  \n",
-      "14  0.073541  0.074455  0.068141  0.068815  0.069570  0.070406  0.071323  \n"
+      "1   0.068743  0.066200  0.065631  0.063225  0.060971  0.058856  0.056870  \n",
+      "2   0.067765  0.065559  0.064814  0.062696  0.060691  0.058793  0.056996  \n",
+      "3   0.076678  0.074301  0.074306  0.071960  0.069733  0.067619  0.065613  \n",
+      "4   0.075294  0.073154  0.072976  0.070845  0.068809  0.066865  0.065012  \n",
+      "5   0.073824  0.071901  0.071564  0.069629  0.067769  0.065984  0.064275  \n",
+      "6   0.074307  0.072338  0.072718  0.070705  0.068771  0.066919  0.065147  \n",
+      "7   0.072650  0.070865  0.071080  0.069239  0.067462  0.065753  0.064113  \n",
+      "8   0.071008  0.069391  0.069456  0.067774  0.066143  0.064568  0.063051  \n",
+      "9   0.069391  0.067929  0.067859  0.066323  0.064827  0.063378  0.061977  \n",
+      "10  0.069456  0.067859  0.068437  0.066752  0.065119  0.063542  0.062023  \n",
+      "11  0.067774  0.066323  0.066752  0.065207  0.063705  0.062250  0.060845  \n",
+      "12  0.066143  0.064827  0.065119  0.063705  0.062325  0.060983  0.059685  \n",
+      "13  0.064568  0.063378  0.063542  0.062250  0.060983  0.059749  0.058550  \n",
+      "14  0.063051  0.061977  0.062023  0.060845  0.059685  0.058550  0.057446  \n"
      ]
     }
    ],
diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter3.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapter3.ipynb
index 3a4f67502..7fd4fa0a7 100644
--- a/doc/LectureNotes/_build/jupyter_execute/chapter3.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/chapter3.ipynb
@@ -489,10 +489,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Runtime: 0.136236 sec\n",
+      "Runtime: 0.134565 sec\n",
       "Jackknife Statistics :\n",
       "original           bias      std. error\n",
-      "  99.979         99.969         0.14845\n"
+      "   100.2         100.19        0.146591\n"
      ]
     }
    ],
@@ -917,7 +917,7 @@
      "text": [
       "Bootstrap Statistics :\n",
       "original           bias      std. error\n",
-      " 99.8342  14.8306        99.8351         0.14857\n"
+      " 99.9919  15.0954        99.9924        0.150989\n"
      ]
     }
    ],
@@ -975,7 +975,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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iwsGDQGYmy01NdHe4AGdnVSkkIv3CckNECAsDGjUC2rYVncRwWJiVYtAglhsifcRyQ2TiSkqA7dtVa21kMtFpDMuwYcCFC8CNG6KTENGjLEQHICJBAgMBAIcz2+DevSUY+fsUIPCS4FCGJSgIsLRUrb15+23RaYjoIa65ITJxYek9Ud8qDR3tLouOYnDs7IA+fbhpikjfsNwQmbBSSYZt93pghOthmMl4TLM6hg4FYmNVO2QTkX5guSEyYSdyWiBZ4YaRbodERzFYQ4ao9luKihKdhIgeYrkhMmFb7/WChzwD3Rwuio5isOrXBwICuGmKSJ+w3BCZKEkCwu71xL/cDsNcVio6jkEbOhSIjAQUCtFJiAhguSEyWWceNMWtIk+MdOUmqdoaOhTIyQEOcSiJ9ALLDZGJCrvXE84W2ejleE50FIPXpg3g48NNU0T6guWGyARJErD1Xk8Mcz0GuVmJ6DgGTyZTrb3ZtYsX0iTSByw3RCbo4kXgaoEPnneLFR3FaAwbBty6pTpjMRGJxXJDZIJ27ADszPPQ1+mM6ChGo1cv1Un9du4UnYSIWG6ITFBEBBDkdBpWZkrRUYyGpSUQHMz9boj0gdBys3DhQnTs2BF2dnZwd3fH8OHDkZCQUOXzYmNjERAQAGtrazRq1AgrV67UQVoi43DvHnDyJDDI5bjoKEZn6FDg9GkgKUl0EiLTJrTcxMbGYurUqTh+/Diio6NRXFyMoKAg5OXlPfE5N27cQEhICHr06IH4+Hi8//77eOuttxAWFqbD5ESGa88e1U6vwc4nREcxOsHBgLk5EB4uOgmRaRN6VfCox85X/tNPP8Hd3R1xcXHo2bNnpc9ZuXIlGjRogCVLlgAAWrRogdOnT2PRokUYOXKktiMTGbyICKBDB8DTihdD0jRnZ6BnT9V+N2+8IToNkekSWm4el52dDQBwdnZ+4jx//PEHgoKCyk0bMGAA1qxZA6VSCblcXu6xoqIiFBUVld3PyckBACiVSlhYWJR9TzXzcMw4djUncuyUSmDvXgtMn14K5UlLnf/82lL+7+9b+djfuTCV/BsOGmSG9983Q2ZmMerWFZDpCfg3qz6Onfo0OXY1eQ2ZJOnHWRkkScKwYcOQmZmJw4cPP3G+pk2bYsKECXj//ffLph07dgzdunVDcnIyvLy8ys2/YMECfPTRRxVeZ+PGjbC1tdXcAhAZgIsXXTBvXnd8+WUsmjTJEh3HKKWk2OLNN/tjzpyT6NIlRXQcIqORn5+PMWPGIDs7G/b29k+dV2/W3EybNg3nz5/HkSNHqpxXJpOVu/+wnz0+HQDmzp2L0NDQsvs5OTnw8fFBUFAQbGxsEB0djf79+1dY40NPp1QqOXZqEjl2hw6ZwcNDwvTpXWE2dLBOf7YmKOVyRE+ahP6rVkGux5+iv63TBMnrshASv1xvdsDh36z6OHbq0+TYPdzyUh16UW6mT5+OXbt24dChQ6hfv/5T5/X09ERqamq5aWlpabCwsICLi0uF+a2srGBlZVVhulwuLxvoR7+nmuHYqU/E2EVFqXZ6tbKSG/RVHuVKJeR6nH+Yy1H8kDwYZkXFMNezvw/+zaqPY6c+TYxdTZ4v9GgpSZIwbdo0bNu2DQcOHICfn1+Vz+nSpQuio6PLTdu3bx86dOjAXzqip7h5E/jrL2DQINFJjN8w16O4X+yAY9mtREchMklCy83UqVOxfv16bNy4EXZ2dkhNTUVqaioKCgrK5pk7dy7Gjx9fdn/y5Mm4desWQkNDcenSJfz4449Ys2YNZs2aJWIRiAxGRARgYQH07y86ifHraHcZHvIM7LrfVXQUIpMktNysWLEC2dnZ6N27N7y8vMpuW7ZsKZsnJSUFt2/fLrvv5+eHyMhIxMTEoG3btvjkk0+wdOlSHgZOVIWICKBHD8DBQXQS42cmkzDE9Rh2pbPcEIkgdJ+b6hyotXbt2grTevXqhTNneE0courKzwcOHgQ+/VR0EtMx1OUYVqcMRkIC0KyZ6DREpoXXliIyAQcOAIWF3N9Gl/o6nYGNWSGvNUUkAMsNkQmIiAAaNeIaBF2yNS9CkNNpXiWcSACWGyIjJ0mqcjN4MFDJqaBIi4a6HsOxY6qLlRKR7rDcEBm5ixeBxERukhJhkMtxSBKwd6/oJESmheWGyMhFRAB16gC9eolOYno8LDPRvr3qSuxEpDssN0RGLiIC6NcPqORE3aQDAweq1tyUlIhOQmQ6WG6IjFhGBnDsGDdJiRQcDNy/D5w+LToJkelguSEyYnv3AqWlQEiI6CSmq3NnwNGRm6aIdInlhsiIRUQAbdsC9eqJTmK6Hl7yguWGSHdYboiMVEmJ6irg3CQlXnAwcOoUkJ4uOgmRaRB6+QUi0p4TJ1T7erDcCBYYiIFFzpCkMOzr+SnGePxecZ6DB3Wfi8iIcc0NkZGKiABcXYFOnUQnIS+rDLStexV7MviPQaQLLDdERioiQnUYsrm56CQEAMHOJ7E3oyNKJZ4mmkjbWG6IjNCdO8C5c9wkpU8GOp/EPaUT4nKbio5CZPRYboiMUGSkao3NgAGik9BDXez/hL35A+zJeE50FCKjx3JDZIQiIoCuXQEnJ9FJ6CG5WQn6O8VxvxsiHWC5ITIyhYXA/v3cJKWPgl1O4mROc9xX2ouOQmTUWG6IjExsLJCfz3KjjwY6n0QpzBGdESA6CpFR43luiIzMnj2Aj9VdtJo2GuCBOXqlnlU6Wtf5G3synsNoD57bhkhbuOaGyMjs3as67FjGYqOXgp1PIIqHhBNpFcsNkRG5eRO4fBkY4HxKdBR6gmCXk0hTOiP+QRPRUYiMFssNkRHZu1d1CHhfpzOio9ATdLW/CDvzPOy5z6OmiLSF5YbIiERFqQ4Bd7DIEx2FnkBuVoJ+TnE83w2RFrHcEBkJpRL4/XfVJRdIvwU7n8TxnBbIVNYVHYXIKLHcEBmJP/4AcnNZbgxBsPMJ1SHhmR1ERyEySiw3REYiKgpwcwPathWdhKpS3zod/nWu82zFRFrCckNkJKKiVNeSMuNftUEIdj6JqIxOPCScSAv4NkhkBFJTgfh4bpIyJMHOJ5CqcMG5B41FRyEyOiw3REZg3z5AJgOCgkQnoerq5nARdc3zedQUkRaw3BAZgagoICBAtc8NGQZLs2L0dTzD/W6ItIDlhsjAlZSo1txwk5ThCXY5iT+yWyErS3QSIuPCckNk4M6cAe7fV+1MTIYl2PkESmCO/ftFJyEyLiw3RAYuKgpwcAA6dxadhGqqgXUaWtrexJ49opMQGReWGyIDFxUF9OsHWFiITkLqCHY+gagoQJJEJyEyHiw3RAYsMxM4fpz72xiyYJeTSE4Gzp8XnYTIeLDcEBmw/fuB0lLub2PIujtcgK2t6oruRKQZLDdEBmzvXqBlS8DHR3QSUpeVmRJ9+qg2LxKRZrDcEBkoSVL9h8hNUoZvwADgyBHgwQPRSYiMA8sNkYH6808gKYnlxhgMHAgolcDBg6KTEBkHlhsiAxUVBdjYAD16iE5CtfXMM0CjRtw0RaQpLDdEBioqCggMBKytRSchTRg4kDsVE2kKz4xBZGgCA5FXYo3DR3biy8bfA4HbRCciDRg4EFi+HLh2TbUmh4jUp9aam7Vr1yI/P1/TWYiommKy2kIhWWKg80nRUUhDAgMBuZybpog0Qa1yM3fuXHh6euK1117DsWPHNJ2JiKoQldERftbJaGJzR3QU0pC6dYHu3blpikgT1Co3d+7cwfr165GZmYnAwEA0b94c//3vf5GamqrpfERUiaiMThjofAoymegkpEkDBwIHDgBFRaKTEBk2tcqNubk5hg4dim3btiExMRGvv/46NmzYgAYNGmDo0KHYuXMnSktLNZ2ViABcy/fGtYL63CRlhAYMAPLzVee8ISL11fpoKXd3d3Tr1g1dunSBmZkZLly4gAkTJqBx48aIiYnRQEQietTezE6wkBUj0DFedBTSsNatAU9Pbpoiqi21y83du3exaNEitGrVCr1790ZOTg7Cw8Nx48YNJCcnY8SIEXjllVc0mZWIAOzN6IDuDhdgZ1EgOgppmEymWnvDnYqJaketQ8GHDBmCvXv3omnTppg0aRLGjx8PZ2fnssdtbGzwzjvv4Ouvv9ZYUCKTEBj41IeLSuU4kLkTHzb8RUeBSNcGDgR+/hlITga8vUWnITJMapUbd3d3xMbGokuXLk+cx8vLCzdu3FA7GBFVdDTbH3mlNhjofEp0FNKS/v1Va3D27gVefVV0GiLDpNZmqV69eqF9+/YVpisUCvzyi+oTpUwmg6+vb+3SEVE5URmd4Gl5H63r/C06CmmJiwvQsSM3TRHVhlrl5tVXX0V2dnaF6bm5uXiVHzWItCYqoyMGOp/kIeBGbuBAIDoaKCkRnYTIMKlVbiRJgqySd9c7d+7AwcGh1qGIqKI7ha64kNeYm6RMwMCBQGYmcIr/1ERqqdE+N+3atYNMJoNMJkPfvn1hYfHP00tKSnDjxg0MHDhQ4yGJSHUIuBlK0N/ptOgopGUdOwKOjqpNU507i05DZHhqVG6GDx8OADh79iwGDBiAunXrlj1maWmJhg0bYuTIkRoNSEQqURkd8Zz9JTjLc0VHIU177Cg5CwD95fOx92s3LIid9s8DBw/qNheRgapRuZk/fz4AoGHDhhg1ahSsra21EoqIyisuNUN0RgeE+vwmOgrpyADnU3g9IRQZSjsWWqIaUmufm1deeUUjxebQoUMYMmQIvL29IZPJsGPHjqfOHxMTU7ZZ7NHb5cuXa52FSJ+dyG2J7JK6vOSCCRngdBKlMMf+zADRUYgMTrXX3Dg7O+PKlStwdXWFk5NTpTsUP5SRkVGt18zLy0ObNm3w6quv1mhzVkJCAuzt7cvuu7m5Vfu5RIZoz/1OcLHIRoDdFdFRSEfqW6fDv851RGV0wovuMaLjEBmUapebr7/+GnZ2dmXfP63cVFdwcDCCg4Nr/Dx3d3c4OjrW+ucTGYqojE4Icj4NcxkvSGtKBjidwqa0PpAk8PB/ohqodrl59DpREyZM0EaWamvXrh0KCwvRsmVLzJs3D4FPOWV9UVERioqKyu7n5OQAAJRKZdnRXkqlUruBjdDDMePY1dxTx87SssKktCIHxD1ohqkNd0JZyeOmRCmXl/tq7Pp5xOOrO6MQr2iKZ+1uArX4e+PfrPo4durT5NjV5DVkkiRJ1ZnxYSmojkc3GVU7iEyG7du3lx2RVZmEhAQcOnQIAQEBKCoqwrp167By5UrExMSgZ8+elT5nwYIF+OijjypM37hxI2xtbWuck0jXYmLqY8mSAKxdGwVHx6Kqn0BGQ6Eww7hxwRg9OgH/+tc10XGIhMrPz8eYMWOQnZ1dZc+odrkxMzOrclPUw5P7lahxWs3qlJvKDBkyBDKZDLt27ar08crW3Pj4+CA9PR02NjaIjo5G//79ITeRT4KaolQqOXZqeurYDR5cYf7x5+cgIc8HJ7pMq/CYqVHK5YieNAn9V62C3EQ+RQ878zEKSy2xt8N7QHi42q/Dv1n1cezUp8mxy8nJgaura7XKTbU3Sx3U0/MrdO7cGevXr3/i41ZWVrCysqowXS6Xlw30o99TzXDs1Ffp2CkU5e6WSGaITg/AG967IX/sMVMmVypNZjyCHU9g1t+TUZRvhroa+Fvj36z6OHbq08TY1eT51S43vXr1UiuMtsXHx8PLy0t0DCKtOJPbBPeLHXgIuAkb4HwKb117CzFZbVFxvR4RVaba5eb8+fPw9/eHmZkZzp8//9R5W7duXa3XfPDgAa5d+2c78o0bN3D27Fk4OzujQYMGmDt3LpKSksquNL5kyRI0bNgQrVq1gkKhwPr16xEWFoawsLDqLgaRQYnK6AQH8wfoYv+n6CgkSBObO/CzTkZURieWG6Jqqna5adu2LVJTU+Hu7o62bdtCJpOhst11arLPzenTp8sd6RQaGgpAdWTW2rVrkZKSgtu3b5c9rlAoMGvWLCQlJcHGxgatWrVCREQEQkJCqrsYRAYlKqMT+jnFwcKMh4CbKpkMGOh8CnszO4qOQmQwql1ubty4UXayvBs3bmjkh/fu3bvSgvTQ2rVry92fPXs2Zs+erZGfTaTvMpR2OJ7TAt83XSw6Cgk2wPkUViQPw7VrwDPPiE5DpP+qXW58fX0r/Z6ItGN/ZgBKYc79bQh9HM9ALlMiKkqOaTxojqhKal1bClCdc2batGno27cv+vXrh2nTpiEhIUGT2YhMWlRGJ/jXuY761umio5BgdhYF6OFwAZGRopMQGQa1ys3WrVvh7++PuLg4tGnTBq1bt8aZM2fg7++P337jVYuJakuSgKiMjlxrQ2VCXI7j4EGgoEB0EiL9p1a5mT17NubOnYs//vgDixcvxuLFi3Hs2DG8//77mDNnjqYzEpmc83mNkaJwxUDnU6KjkJ4IcT6BwkIgJkZ0EiL9p1a5SU1Nxfjx4ytMHzt2LFJTU2sdisjURWV0hK1ZAbo7XBAdhfREc9vb8PUFN00RVYNa5aZ37944fPhwhelHjhxBjx49ah2KyNRFZXRCH6d4WJmZxiUGqGoyGRASoio31btoDpHpqvbRUo9eu2no0KGYM2cO4uLi0LlzZwDA8ePH8dtvv1V6kUoiqr7cYhscyX4W3zyzTHQU0jMhIcCKFcDVq0DTpqLTEOmvapebyi5ouXz5cixfvrzctKlTp2Ly5Mm1DkZkqn7PbI9iyYI7E1MFgYGApaVq7Q3LDdGTVXuzVGlpabVu6lwRnIj+EZXRCU1sEtHIJkV0FNIzdeoAvXsDe/aITkKk39Q+zw0RaZ7qEPBOPEqKnig4WHXEVF6e6CRE+qvam6Uel5eXh9jYWNy+fRsKhaLcY2+99VatgxGZooR8H9wq8uQmKXqikBBg5kzg4EFgMK+kSVQptcpNfHw8QkJCkJ+fj7y8PDg7OyM9PR22trZwd3dnuSFSU1RGJ1jJFOjteFZ0FNJTTZoAjRur9rthuSGqnFqbpWbOnIkhQ4YgIyMDNjY2OH78OG7duoWAgAAsWrRI0xmJTMaejOfQy/EcbM2LREchPSWTqTZN8ZBwoidTq9ycPXsW77zzDszNzWFubo6ioiL4+Pjg//7v//D+++9rOiORScjPB2Kz2nCTFFUpJAS4dQu4fFl0EiL9pFa5kcvlkMlkAAAPDw/cvn0bAODg4FD2PRHVTGwsUCRZstxQlXr3BqytebZioidRq9y0a9cOp0+fBgAEBgbiP//5DzZs2IAZM2bg2Wef1WhAIlMRGQn4WqWiuS0/INDT2dioznnDckNUObXKzeeffw4vLy8AwCeffAIXFxe8+eabSEtLww8//KDRgESmQJKA8HBgkMtx/G+lKNFThYQAhw8DubmikxDpH7WOlurQoUPZ925ubojkxweiWrl0Cbh5Exj87B+io5CBCA4Gpk8Hfv8dqOQE8kQmrVYn8UtLS8Phw4dx5MgR3Lt3T1OZiExOeDhgawsEOsaLjkIGonFj1SUY+NmSqCK1yk1OTg7GjRuHevXqoVevXujZsye8vb0xduxYZGdnazojkdELDwf69QOszXkVcKq+kBDVpRh4SDhReWqVm4kTJ+LEiRMIDw9HVlYWsrOzER4ejtOnT2PSpEmazkhk1DIygGPHgEGDRCchQxMcDNy5A1y8KDoJkX5Ra5+biIgI7N27F927dy+bNmDAAKxatQoDBw7UWDgiU7B3L1BS8r9ys0l0GjIkPXuqNmfu2QPwQFWif6i15sbFxQUODg4Vpjs4OMDJyanWoYhMSXg40K4dUK+e6CRkaKytgT59uN8N0ePUKjfz5s1DaGgoUlJSyqalpqbi3XffxYcffqixcETGrrhY9ambm6RIXSEhwJEjAHd3JPpHtTdLtWvXruysxABw9epV+Pr6okGDBgCA27dvw8rKCvfu3cMbb7yh+aRERuj4cSAzkxdAJPUFB6s2a+7fD4wcKToNkX6odrkZzhMpEGlceDjg5gZ07Cg6CRmqhg2BFi1Um6ZYbohUql1u5s+fr80cRCYpPFy1ScqsVmecIlMXEgJs3Kg6JJxnuCaq5Un84uLisH79emzYsAHx8Tz5GFFN3LwJ/Pkn97eh2gsJAVJSgHPnRCch0g9qHQqelpaG0aNHIyYmBo6OjpAkCdnZ2QgMDMTmzZvh5uam6ZxERmfPHjNYWABBQaKTkKHr3h2oW1e1aaptW9FpiMRTa83N9OnTkZOTgz///BMZGRnIzMzExYsXkZOTg7feekvTGYmMUmSkDL16Afb2opOQobO0VJ3hmoeEE6moteYmKioK+/fvR4sWLcqmtWzZEt999x2C+DGUqEqFheaIiZHhiy9EJyGDEhj4xIeCkwfjzSszkJFhDmdnHWYi0kNqrbkpLS2FXC6vMF0ul6O0tLTWoYiM3fnzbigqknF/G9KYEOfjKIU5oqJEJyEST601N3369MHbb7+NTZs2wdvbGwCQlJSEmTNnom/fvhoNSGQ0Hn7qtrTEKel7NLFNRJPXx4vNREajvnU62te9gl27mmLMGNFpiMRSa83NsmXLkJubi4YNG6Jx48Z45pln4Ofnh9zcXHz77beazkhkVCQJiIvzwCC3k6KjkJEZ5noUe/YACoXoJERiqbXmxsfHB2fOnEF0dDQuX74MSZLQsmVL9OvXT9P5iIzO2dzGyMiwQbDfCdFRyMgMcz2K+TdfRUwMj8Ij01bjclNcXAxra2ucPXsW/fv3R//+/bWRi8hoRd57Dra2SnRz+hMoFp2GjEnrOn/D1xfYtYvlhkxbjTdLWVhYwNfXFyUlJdrIQ2T09qR3Qtu2abA0Y7MhzZLJgKFDVeVGkkSnIRJH7auCz507FxkZGZrOQ2TU7iqccCq7GTp2vCs6ChmpYcOAxESAJ40nU6bWPjdLly7FtWvX4O3tDV9fX9SpU6fc42fOnNFIOCJjs+d+JwBA+/Z3gUuCw5BR6tkTcHAAdu4E2rcXnYZIDLXKzfDhwyGTySBxvSdRjYTf74JODpfh4MDDWUg75HLVtaZ27QI++kh0GiIxalRu8vPz8e6772LHjh1QKpXo27cvvv32W7i6umorH5HRUJRaYF9mB8zy+w2Ak+g4ZMSGDQM2bQJu3QJ8fUWnIdK9Gu1zM3/+fKxduxaDBg3CSy+9hP379+PNN9/UVjYio3I4uzVyS+og2JWHgJN2BQer1uDs2iU6CZEYNVpzs23bNqxZswajR48GALz88svo1q0bSkpKYG5urpWARMYi/H5n1LO8hzZ215EsOgwZNXt71Qmxd+4Epk8XnYZI92q05iYxMRE9evQou9+pUydYWFggOZlv1URVibjfGYNd/oBMJjoJmYKhQ4HYWCArS3QSIt2rUbkpKSmBpaVluWkWFhYoLub5Ooie5soV4GqBDwa7/CE6CpmIoUOB4mJgzx7RSYh0r0abpSRJwoQJE2BlZVU2rbCwEJMnTy53OPi2bds0l5DICOzeDVibFaGPE08+Qrrh46M6FHznTuCll0SnIdKtGpWbV155pcK0sWPHaiwMkbHavh0IcjoNW/MiKGFZ9ROINGDYMOCrr1QX0uTmUDIlNSo3P/30k7ZyEBmtlBTg2DHgp2aHRUchEzN0KDB/vmrfm969Rach0h21Lr9ARNW3Ywdgbg4McTkmOgqZmDZtVOe52blTdBIi3WK5IdKybdtUh+U6y3NFRyETwwtpkqliuSHSovv3gYMHgZEjRSchkxEYWO427NA7SEwEzvaaoXp88GCh8Yh0geWGSIt27wZKS1U7dhKJ0NPhHBzMH2B3WmfRUYh0huWGSIvCwoBu3QBPT9FJyFTJzUoQ4nICu+91ER2FSGdYboi0JDcX2LePm6RIvGGuR3Eu9xmkpdmIjkKkEyw3RFoSEaE6v8i//iU6CZm6YOcTkMuUOHWKqxDJNLDcEGnJtm1Ahw6qQ3GJRLK3yEdv53M4eZLlhkyD0HJz6NAhDBkyBN7e3pDJZNixY0eVz4mNjUVAQACsra3RqFEjrFy5UvtBiWqooACIjARGjBCdhEhlsNtxXLzoiixlnapnJjJwQstNXl4e2rRpg2XLllVr/hs3biAkJAQ9evRAfHw83n//fbz11lsICwvTclKiKjx2+O2+TvOQlweM3DHun+lEAg12P46SEjNEpXcUHYVI62p0+QVNCw4ORnBwcLXnX7lyJRo0aIAlS5YAAFq0aIHTp09j0aJFGPmEvTaLiopQVFRUdj8nJwcAoFQqYWFhUfY91czDMePY/Y9l+etFbb3fCy3r3ISfY1qFa0kp5fJyX6n6OHbq85RnoVGjLOy63xWj+XdbI3y/U58mx64mryGTJP04b6VMJsP27dsxfPjwJ87Ts2dPtGvXDt98803ZtO3bt+PFF19Efn4+5JW84S1YsAAfffRRhekbN26Era2tRrITPUqplGHChIEYNOgGxoy5LDoOUZktW5pi585n8PPPeyCX68VbP1G15efnY8yYMcjOzoa9vf1T5xW65qamUlNT4eHhUW6ah4cHiouLkZ6eDi8vrwrPmTt3LkJDQ8vu5+TkwMfHB0FBQbCxsUF0dDT69+9faTGiJ1MqlRy7Rz1y1tfo9PbIyxuKdxOXoM3y6xVmVcrliJ40Cf1XrYKcnwRrhGOnPqVcjhv93sGmTS1gYxOCoCCWm+ri+536NDl2D7e8VIdBlRtAtYbnUQ9XPD0+/SErKytYWVlVmC6Xy8sG+tHvqWY4dv+jUJR9uzOlKxpZJyHA8jJkiic/Ra5UQq54ygz0RBw79TRsmINnbO8gLKw+Bg0Sncbw8P1OfZoYu5o836AOBff09ERqamq5aWlpabCwsICLi4ugVET/KJHMsCO9G0a6HcYT+jaRMDIZ8IJnLLZvBx7ZFZHI6BhUuenSpQuio6PLTdu3bx86dOjANk164Vh2K6QpnTHC9ZDoKESVesEjFllZqrNnExkroeXmwYMHOHv2LM6ePQtAdaj32bNncfv2bQCq/WXGjx9fNv/kyZNx69YthIaG4tKlS/jxxx+xZs0azJo1S0R8ogrC7vWEt+U9dLLnjsSkn/ztbqFVK2DLFtFJiLRHaLk5ffo02rVrh3bt2gEAQkND0a5dO/znP/8BAKSkpJQVHQDw8/NDZGQkYmJi0LZtW3zyySdYunTpEw8DJ9IlSQK2pffACLfDMJNxZ03SX6NGATt3qk42SWSMhO5Q3Lt3bzztSPS1a9dWmNarVy+cOXNGi6mI1HM6txkSizwwwvWw6ChETzVqFPCf/6jOos3PhmSMDGqfGyJ9ti29B1zlWejhcF50FKKnatoUaNeOm6bIeLHcEGmAJKn2txnmchQWZqWi4xBVadQoIDwcePBAdBIizWO5IdKAP/Ma4mqBD0a68SgpMgwvvqja52b3btFJiDSP5YZIA7al94S9+QP0cYoXHYWoWvz8gOee46YpMk4sN0QaEHavJwa7HIeVGS8JQIZj1Chgzx4gO1t0EiLNMrjLLxDpm2vXgPN5jTG/4c+ioxBVT2AgAOCFQleEKn7Dzs4LMd7zsbP6HTwoIBiRZnDNDVEthYUBNmaFGOB8SnQUohqpb52OHg7nsDktUHQUIo1iuSGqBUkC1q8HhrocQx3zQtFxiGpslHsMojM74L7SXnQUIo1huSGqhXPngIsXgXGe0VXPTKSHnneLRakkw/Z73UVHIdIYlhuiWli3DnBzA4KcuEmKDJOHZSYCHc9ic1of0VGINIblhkhNxcXAxo3ASy8BcrMS0XGI1DbK/SAOZrXFXYWT6ChEGsFyQ6Sm338HUlOBceNEJyGqnYcXew2711N0FCKNYLkhUtP69UDz5kBAgOgkRLXjIs9Bf6fTPGqKjAbLDZEaHjwAtm1TrbWRyUSnIaq9Ue4xOJL9LJKKXEVHIao1lhsiNWzfDuTnAy+/LDoJkWYMdz0CuawYv6X1Eh2FqNZYbojUsG4d0LMn4OsrOgmRZjhY5GGg8ykeNUVGgeWGqIaSk1U7E3NHYjI2o90P4ERuS9ws8BAdhahWWG6IamjjRkAuB55/XnQSIs0a4nIMNmaF+PUedywmw8ZyQ1RD69YBQ4cCjo6ikxBpVl2LQgxyOc6jpsjg8argRFUJ/OeN/vyDRjh/fg0+LX0fCPxDYCgi7RjtfhDP//kRrlwBmjYVnYZIPVxzQ1QD6+72h6s8CwOdT4qOQqQVIc7H4WiRi7VrRSchUh/LDVE1lUhm2Hi3L0a7H+DlFsho2Zgr8LL7fvz0k+oSI0SGiOWGqJoOZLZDssIN4zx4BXAybpO8I5CaCkRGik5CpB6WG6JqWn+3P5ra3EZHu8uioxBpVZu6f6NDB2D1atFJiNTDHYqJqiGvxBph93piToNNvNwCmYSJOYsx5fTbSOo6GvWs0iuf6eBB3YYiqiauuSGqhh3p3ZFXaoOx3CRFJuIl999hbabAz6kDREchqjGWG6JqWJfaH90dzsPPJlV0FCKdsLfIx4vuMViTEoxSiasrybCw3BBVIaXIGdGZAdyRmEzORK9IXC+sh4NZ7URHIaoRlhuiKmxK6wsLWQlecIsRHYVIp7raX0Rz21tYnRIiOgpRjbDcEFVh3d3+GOLyB5zkD0RHIdIpmQyY6BWBbfd64L7SXnQcompjuSF6iosXgbMPmmCcJzdJkWka5xENCTKsv9tfdBSiamO5IXqKtWsBZ4tsBDufEB2FSAh3yywMcz2K1SkhkCTRaYiqh+WG6Any84EffwT+7bUHlmY8Dz2ZrolekbiY1wgnc1uIjkJULSw3RE+wcSOQlQVM8d4pOgqRUP2c4tDAKpU7FpPBYLkhqoQkAcuWAYMHg+e2IZNnLivFv732YNPdvsgtthEdh6hKLDdElTh6FDh3Dpg2TXQSIv3wb889yC+1wq/3AkVHIaoSyw1RJZYtA5o2Bfr1E52ESD/4WN/DQOeT3DRFBoHlhugxyclAWBgwdSpgxr8QojITvSJxPKcVLj5oKDoK0VPxrZvoMT/8AFhZAa+8IjoJkX4Z7PIH3OUZWJPKtTek31huiB6hUADff68qNg4OotMQ6RdLs2K84rkPv6QGoahULjoO0ROx3BA9IiwMSE1VbZIioope84pERrEDdqR3Fx2F6IlYbogesWwZ0KcP0LKl6CRE+qmZbSJ6OJzjjsWk11huiP7nzBng2DEe/k1UlYlekdif2QHXr4tOQlQ5C9EBiIQK/OecHd9dfhc+VgEYsmQMsLRUYCgi/fa8WyxCr03BkiUOWLpUdBqiirjmhgjAfaU9Nqb1xZveu2BhxmJD9DS25kWYVm87Vq8G0tNFpyGqiOWGCMCalBBIkgwTvSJERyEyCNPqbQcALF8uOAhRJVhuyOSVSGZYnjQMo90PwM0yW3QcIoPgapmD114Dvv0WyM8XnYaoPJYbMnkR9zvjVpFn2SdRIqqe0FAgMxP46SfRSYjKY7khk7csaTies/sLHeyviI5CZFD8/IAXXwQWLQKKi0WnIfoHyw2ZtMt5PojO7Mi1NkRqevdd4OZNYOtW0UmI/sFyQybtu+ThcJdn4AX3WNFRiAxSu3ZAUBDw3/8CkiQ6DZEKyw2ZrJwcYG3qQLzuHQ4rM6XoOEQGa/Zs4OxZYP9+0UmIVFhuyGT9/DNQUGKFN7x2i45CZND69AHatwf+7/9EJyFSYbkhk1RYqHojHuV+EPWteRYyotqQyYA5c1RrbuLiRKchYrkhE7V6NZCcDPyn4S+ioxAZhREjgEaNgC+/FJ2ESA/KzfLly+Hn5wdra2sEBATg8OHDT5w3JiYGMpmswu3y5cs6TEyGrqAA+PxzYOxY1RWOiaj2LCyAWbOA334D/v5bdBoydULLzZYtWzBjxgx88MEHiI+PR48ePRAcHIzbt28/9XkJCQlISUkpuzVp0kRHickYfP89kJYGfPih6CRExmXCBMDFBVi8WHQSMnVCrwq+ePFivPbaa5g4cSIAYMmSJdi7dy9WrFiBhQsXPvF57u7ucHR01FFKMib5+cAXXwCvvAI884zoNEQGLjCw3F0bANPrjMPnK1/G/LOj4W6ZBRw8KCQamTZh5UahUCAuLg7vvfdeuelBQUE4duzYU5/brl07FBYWomXLlpg3bx4CH/sDe1RRURGKiorK7ufk5AAAlEolLCwsyr6nmnk4ZoY2dsuWmeH+fTPMmVMMpRKApaXOMyjl8nJfqfo4durT1di97heJL26PwTepz2PBM78ABvYeURlDfb/TB5ocu5q8hkySxJx2KTk5GfXq1cPRo0fRtWvXsumff/45fv75ZyQkJFR4TkJCAg4dOoSAgAAUFRVh3bp1WLlyJWJiYtCzZ89Kf86CBQvw0UcfVZi+ceNG2Nraam6BSO8VFJhj8uT+eO65FEyZck50HCKjtXq1P2JifLBq1T7Y2JSIjkNGIj8/H2PGjEF2djbs7e2fOq/QzVIAIJPJyt2XJKnCtIeaNWuGZs2ald3v0qULEhMTsWjRoieWm7lz5yI0NLTsfk5ODnx8fBAUFAQbGxtER0ejf//+kPOTYI0olUqDG7svvzRDfr4Zli+vhwYN6qkmDh6s8xxKuRzRkyah/6pVkPOTYI1w7NSny7HzL3BDs7yfkbwgEdMvTNTqz9IFQ3y/0xeaHLuHW16qQ1i5cXV1hbm5OVJTU8tNT0tLg4eHR7Vfp3Pnzli/fv0TH7eysoKVlVWF6XK5vGygH/2easZQxi4nB/jqK2DiRKBx40fyKhTCMsmVSsgF/nxDxrFTny7GrrF5El5y/x3f3PwXpkMOA3iLqBZDeb/TR5oYu5o8X9jRUpaWlggICEB0dHS56dHR0eU2U1UlPj4eXl5emo5HRubbb4EHD4D33xedhMg0zPbZjNtFnli7VnQSMkVCN0uFhoZi3Lhx6NChA7p06YIffvgBt2/fxuTJkwGoNiklJSXhl19UJ1pbsmQJGjZsiFatWkGhUGD9+vUICwtDWFiYyMUgPZedDSxaBLzxBlC/vug0RKbh2bo3MMZ9Pz78sB9Gjwbs7EQnIlMitNyMGjUK9+/fx8cff4yUlBT4+/sjMjISvr6+AICUlJRy57xRKBSYNWsWkpKSYGNjg1atWiEiIgIhISGiFoH02f+Oovvm5ngU5ozBe6dfBgLvCw5FZDoWNlqFZmf74b//BT79VHQaMiXCdyieMmUKpkyZUuljax9bnzl79mzMnj1bB6nIWGQq62Jx4gt403sXvK1YbIh0qYF1Gt55R7W/2+uvAw0aiE5EpkL45ReItOnrOy9AIVlgToNNoqMQmaQ5cwBHR2DuXNFJyJSw3JDRuq+0x5I7IzG13g54WGaKjkNkkuzsVJukNm4ETpwQnYZMBcsNGa2vEl9EqWSG2T6bRUchMmkTJgCtWwOhoYCY08aSqWG5IaN07x6w9M4ITK+/DW6W2aLjEJk0c3PVxTSPHVNdNZxI21huyCh99hkgk0mY5fOr6ChEBKBvX2DIENU+OIWFotOQsRN+tBSRpsXFqU7at7DhOrjIq3+6biLSgkcubPxlvg/8b/2Ipa3WYHaDRzYX88rhpGFcc0NGpbhYdcipvz8wsz7XfxPpk2a2iXjTexc+vTUWaQpH0XHIiLHckFFZtgyIjwd++AGQm/FqxET6Zn7Dn2EuK8X8mxNERyEjxnJDRuP2bWDePGDKFOC550SnIaLKuMhz8B/fX/BD8mD8mddQdBwyUiw3ZBQkCZg2DbC3V+1MTET6a2q9HWhkk4J3rr0pOgoZKZYbMgrbtwO7d6t2JHZwEJ2GiJ7G0qwYXzZaib2ZnRB1v6PoOGSEWG7I4OXkANOnqw4zHTFCdBoiqo5hrkfRy+Es3vl7CoqLRachY8NyQwbvgw+A7GzVzsQymeg0RFQdMhnw9TPf4VJ+A3z1leg0ZGxYbsignTgBfPcd8MknvOIwkaFpZ3cN7/pswYcfAmfOiE5DxoTlhgyWUqk6p027dqrNUkRkeD7x+xH+/sDLLwP5+aLTkLFguSGDtWQJcPGi6pw2FjzXNpFBsjQrxoYNwK1bwLvvik5DxoLlhgzSzZvA/PmqNTYBAaLTEFFttGgBfPUVsHw5EB4uOg0ZA37eJYMjScCUgONwKW2ET+ImAIEFoiMRUS1NngxERgL//jdw4QLg4SE6ERkylhsyOJs3A3syOmOn/wews2CxITJ4gYGQAVijcMSzWWvw75ZXEP7s3PJHP/LimlQD3CxFBuXSJdVOxKPcDmCo6zHRcYhIg9wts/BT8/9DZEZnrEgeJjoOGTCWGzIY2dnA8OGAry+wutmXouMQkRaEuJzAVO/teOfvN3Epj+d3IPWw3JBBKC0Fxo4F0tKAHTuAuhaFoiMRkZZ82Xgl/KxTMObSPBSVykXHIQPEckMG4eOPgYgIYMMG4JlnRKchIm2yMVdgY4tP8WdeQ3x449+i45ABYrkhvbdzJ/DRR6qzEIeEiE5DRLrQ1u5vfOa3BosSX8TBzLai45CBYbkhvXb5MjBunOqCmO+/LzoNEenSOz6/ItDxLMZfnouMDNFpyJCw3JDeyslR7UBcvz6wdi0viklkasxkEn5uvhAFJVYYOpSXZ6DqY7khvVRaCowfD6SkqHYgtrMTnYiIRKhvnY6I1nMRHw+MGqW6phxRVXgSP9I/gYH49OZ47Lz5Knb7z0XTN46LTkREAj1nfwnbtgGDBwOTJgE//cQ1ufR0XHNDeic8vTPm33wVHzX8CYNdWWyICBgwAPjlF+Dnn4E5c0SnIX3HNTekVxISgJcvfYBhLkcwz3ed6DhEpEdeegm4dw94+23AzY1XEacnY7khvXHlCtCvH+BtdR+/tFgIM5kkOhIR6Zm33lKdzHP2bFXBmTBBdCLSRyw3pBcuXlQVG2dnYL/PO7C34GERRFS5Tz5RFZyJEwEXF2DIENGJSN9wnxsSLi4O6NUL8PICYmNVa26IiJ5EJgNWrACGDQNefBE4ckR0ItI3LDck1NGjQJ8+QJMmwIEDqtXMRERVMTdXXY6lc2fVmpsLF0QnIn3CckPCHDgABAUBbdsC0dGAk5PoRERkSKytVZdn8fNTHU115YroRKQvWG5IiIgI1XWiuncH9uzhSfqISD329qr3EAcH4LnnVB+UiLhDMelWYCDC7vXES3/NQ4jzCWwp/BhWg3jKUSKqQmDgEx/yAPCHSx281DAcwcHA4sXA9Ok80Z8p45ob0qn1qf3w4p//wUi3Q/it1QJYmbHYEFHtOcrzEB6uOgfO228Db7wBKBSiU5EoXHNDOiFJwNdfA7Muz8UEz71Y1WwRzGWlomMRkREx7xeIrwD4NxuIyatnIuHXS9jaaj7cLLP/mengQWH5SHdYbkhznrDa+K7CCRMuz0FUxnOY5fMr/tvoB56gj4i05lWvKDSzTcS/Ln6MTmdWYKf/PLSue110LNIhbpYirYq43xnPnlqD+NwmiHx2Dr5s/D2LDRFpXVeHP3Eq4E04mOeh65ll2JneTXQk0iGWG9KKghJLTLvyFgZfWIhOdpdxvuNrCHY5KToWEZmQBtZpONp+OgY6n8Twi5/is1svQ+JnK5PAckMad+5BY3SI+x5rUkOwrMk32P3s+3C3zBIdi4hMUB3zQvza6iPM912LeTcmok8f4K+/RKcibWO5IY0plWT4OvF5dIpbDgtZCU4HTMbUejt4OCYRCWUmk7DA72fsbf0u7twB2rQB5swBHjwQnYy0heWGNCI5GQg+/1+E/j0VU+vtxIn2b6JVnZuiYxERlQlyPo0LF4D//AdYuhRo0QIICwM3VRkhlhuqlfR0YPZs4JlnVJujolrPxuJnlsPanOevISL9Y20NfPihatNU27bA888DwcHA1auik5Em8VBwUkt2NrBpUzOMG2cBSQLeeQd45+B4OMrzREcjIqqSnx+wezewaxfw1luAv79qU9XcuYBNyGOntbC0BKZMAQYP/ufMgDxfjl5juaHq+d85bB4UW+PbpBH4MnE08syaYJrXVrxXbwPcjmQDcsEZiYiq8tj5uIYC6NfACp9LL+O/n47C+i/TMc83GGPc93MNtAHjZimqlsISOb5OfB6NTmzE/JsT8JLXAaxcGY0vmq0uf/ZPIiIDY2tehE8b/YgLHV/Ds3Vu4LWE2fA5/ivmXf83kotcRMcjNXDNDT1VSgqwYQOw5MQGpCqc8arXHszzXQ9v+0xEOk8RHY+ISGOa2t7Bzmfn4Wp+PXybNALfJI3EfxNfwkiPw2ifYI4Q0QGp2lhuqIK8PGDHDuCXX4D9+wG5HHjBKR7/8f0FTWyTAABKWIoNSUSkJU1sk7C0ybf41G8NfkoNxrdJI7Bljje22rtjRr2teN4tlu+Aeo6bpQgAUFKiKjKvvAJ4egJjxwIFBcDKlUBqKrCuxcKyYkNEZArsLfLxdv0wXOz+Gt5//zjsLArw8qV5aHh8E2bOBA4cAJTcLUcvcc2NCSsuBs6cUZ3nYcMGICkJaNJEdWj32LGqowmIiEyduawUnTrdxYLTy3El0xsrkobi11//hSVLAAcHYOBAYOhQ1SHlTk6i0xLAcmP8HjkyoLjUDPEPmiAmqy1istricPazyC2pA2dnYPRoYNw44LnnwDMKExE9Qas6N7Gs6VJ8Ky1FvFcT7L7fBbsjuuLlLc1gjhJ0d7iAIa7HMMTlDzQ5vo7vp4Kw3Bix4mLgbE5THMxqV67M2JoVoLvDRcxtsBG9Hc+ig10C5H+VAHNFJyYiMgwyGdDe7ira213F/Ia/IKnIFeH3u2B3ehfMu/EaZv09BZ7eQIcOqlvHjqqv7u6ik5sGlhsjUFIC/P038Oef5W8JCYBC8T1szArR3eEC3muwqazMWJoVi45NRGQ06lml4w3v3XjDezfyS6xwILMdTuS2wKmjzfFtVDPcL3YAAPhY3UVHuwR0+N+tRcQieHsDZtwDVqOEl5vly5fjyy+/REpKClq1aoUlS5agR48eT5w/NjYWoaGh+PPPP+Ht7Y3Zs2dj8uTJOkwsRnY2cOdO+duV5fvxZ15DXM5vgCJJte++k0UOWtW5ia62NzGpwU20q3sVnewvs8wQEemIrXkRBrsex2DX4wBU1666WeiJ07nNcCq3OU7nNsUXt19CTkldwEd1SQg/P6BRI6Bx43++Nm4MNGwI2NiIXR5DJLTcbNmyBTNmzMDy5cvRrVs3fP/99wgODsZff/2FBg0aVJj/xo0bCAkJwaRJk7B+/XocPXoUU6ZMgZubG0aOHClgCdQUGIiCEktkFNsjQ2mHzGI7ZCjtyu5nFNsjVeGMO0WuuFPkhjtFbsgtqVP2dBlK4WmZAT9rT3Syv4xXvaLQyvYGWtW5CU/LDG7jJSLSIzIZ4GeTCj+bVLzgHgsAKJVkuFZQD1fy6+N6oTf+zvHG9eNeiD7ojesF3mUfWAHAxSIb7paZ8LDMhLs8S/W9PBPulln/m5YJp43fwd4esLcH6tThvpNCy83ixYvx2muvYeLEiQCAJUuWYO/evVixYgUWLlxYYf6VK1eiQYMGWLJkCQCgRYsWOH36NBYtWqQX5eaHH4BLl1TnicnLAx48+Of7R2/Z96JQWGpV6Ws4mD+AszwHbvJs+FilYYDzKdS3uof6Vun/+3oPXpb3uSaGiMiAmckkNLW9g6a2dyo8VirJkKJwwd8F3rhe4IUUhQvSlI64q3BGmsIRf+X7Ik3hiHSlA0phrnpSi0de2wyws0NZ2Xl4s7VVrQWytlZ9rex7KyvVuc0evVlalr9vYQGYmz/9Zm+vOpJMFGHlRqFQIC4uDu+991656UFBQTh27Filz/njjz8QFBRUbtqAAQOwZs0aKJVKyOUVL25UVFSEoqKisvvZ2apLBWRkZMDa2hr5+fm4f/9+pc+tqX37zHFhzx3UMS+CrXkhbM2KUNe8EO7mhahjXgQb80LUsS2CXaN8OMlz4SR/ACd5LhwtHsBJ/gAOFnmwMCut8ufkAhB9iiIloBo7AHJuLK4Rjp36OHbq49ipT8TYWdtkoJVNBlrh4hPnKZHMkKGoizSFI3Lnf4XcXDxykyEnR/UhOydHhtxcICcHuHsXKCoCCgpkKCx8+D1QWKj6KkmaWeXz5psl+OSTUiiVSo39P5ubq/rfT5KkqmeWBElKSpIASEePHi03/bPPPpOaNm1a6XOaNGkiffbZZ+WmHT16VAIgJScnV/qc+fPnSwB444033njjjTcjuCUmJlbZMYTvUCx7bMOgJEkVplU1f2XTH5o7dy5CQ0PL7peWliIjIwMuLi7Izc2Fj48PEhMTYW9vr+4imKScnByOnZo4durj2KmPY6c+jp36NDl2kiQhNzcX3t7eVc4rrNy4urrC3Nwcqamp5aanpaXBw8Oj0ud4enpWOr+FhQVcXCq/cquVlRWsrMrv3+Lo6Ajgn0Jkb2/PX1g1cezUx7FTH8dOfRw79XHs1KepsXOo5o48wja8WlpaIiAgANHR0eWmR0dHo2vXrpU+p0uXLhXm37dvHzp06KCRfWaIiIjI8Andqyw0NBSrV6/Gjz/+iEuXLmHmzJm4fft22Xlr5s6di/Hjx5fNP3nyZNy6dQuhoaG4dOkSfvzxR6xZswazZs0StQhERESkZ4TuczNq1Cjcv38fH3/8MVJSUuDv74/IyEj4+voCAFJSUnD79u2y+f38/BAZGYmZM2fiu+++g7e3N5YuXar2YeBWVlaYP39+hc1WVDWOnfo4durj2KmPY6c+jp36RI2dTJKqc0wVERERkWHgyQ6IiIjIqLDcEBERkVFhuSEiIiKjwnJDRERERsVoyk1ubi5mzJgBX19f2NjYoGvXrjh16lTZ43fv3sWECRPg7e0NW1tbDBw4EFevXq3262/evBkymQzDhw/XQnqxtDV2WVlZmDp1Kry8vGBtbY0WLVogMjJSm4uic9oauyVLlqBZs2awsbGBj48PZs6cicLCQm0uitYdOnQIQ4YMgbe3N2QyGXbs2FHucUmSsGDBAnh7e8PGxga9e/fGn3/+WW6eoqIiTJ8+Ha6urqhTpw6GDh2KO3cqXnjwccuXL4efnx+sra0REBCAw4cPa3LRtE7U2C1cuBAdO3aEnZ0d3N3dMXz4cCQkJGh68bRK5O/dQwsXLoRMJsOMGTM0sES6I3LskpKSMHbsWLi4uMDW1hZt27ZFXFxctbMbTbmZOHEioqOjsW7dOly4cAFBQUHo168fkpKSIEkShg8fjuvXr2Pnzp2Ij4+Hr68v+vXrh7y8vCpf+9atW5g1axZ69OihgyXRPW2MnUKhQP/+/XHz5k1s3boVCQkJWLVqFerVq6fDJdM+bYzdhg0b8N5772H+/Pm4dOkS1qxZgy1btmDu3Lk6XDLNy8vLQ5s2bbBs2bJKH/+///s/LF68GMuWLcOpU6fg6emJ/v37l10sDwBmzJiB7du3Y/PmzThy5AgePHiAwYMHo6Sk5Ik/d8uWLZgxYwY++OADxMfHo0ePHggODi53mgl9J2rsYmNjMXXqVBw/fhzR0dEoLi5GUFBQtd439YWosXvo1KlT+OGHH9C6dWuNLZOuiBq7zMxMdOvWDXK5HHv27MFff/2Fr776quzqAtVS5dWnDEB+fr5kbm4uhYeHl5vepk0b6YMPPpASEhIkANLFixfLHisuLpacnZ2lVatWPfW1i4uLpW7dukmrV6+WXnnlFWnYsGHaWARhtDV2K1askBo1aiQpFAqtZRdNW2M3depUqU+fPuWmhYaGSt27d9fsAggEQNq+fXvZ/dLSUsnT01P64osvyqYVFhZKDg4O0sqVKyVJkqSsrCxJLpdLmzdvLpsnKSlJMjMzk6Kiop74szp16iRNnjy53LTmzZtL7733noaWRrd0OXaPS0tLkwBIsbGxtV8QAXQ9drm5uVKTJk2k6OhoqVevXtLbb7+t0eXRJV2O3Zw5c2r9fmcUa26Ki4tRUlICa2vrctNtbGxw5MgRFBUVAUC5x83NzWFpaYkjR4489bU//vhjuLm54bXXXtN8cD2grbHbtWsXunTpgqlTp8LDwwP+/v74/PPPq/VJx1Boa+y6d++OuLg4nDx5EgBw/fp1REZGYtCgQVpYCv1w48YNpKamIigoqGyalZUVevXqhWPHjgEA4uLioFQqy83j7e0Nf3//snkep1AoEBcXV+45ABAUFPTE5xgabY1dZbKzswEAzs7OGkovlrbHburUqRg0aBD69eunnQUQSJtjt2vXLnTo0AEvvPAC3N3d0a5dO6xatapG+Yyi3NjZ2aFLly745JNPkJycjJKSEqxfvx4nTpxASkoKmjdvDl9fX8ydOxeZmZlQKBT44osvkJqaipSUlCe+7tGjR7FmzZoaD6oh0dbYXb9+HVu3bkVJSQkiIyMxb948fPXVV/jss890uHTapa2xGz16ND755BN0794dcrkcjRs3RmBgIN577z0dLp1uPbwg7uMXzfXw8Ch7LDU1FZaWlnBycnriPI9LT09HSUnJU1/X0Glr7B4nSRJCQ0PRvXt3+Pv7ayC5eNocu82bN+PMmTNYuHChhlPrB22O3fXr17FixQo0adIEe/fuxeTJk/HWW2/hl19+qXY+oyg3ALBu3TpIkoR69erBysoKS5cuxZgxY2Bubg65XI6wsDBcuXIFzs7OsLW1RUxMDIKDg2Fubl7p6+Xm5mLs2LFYtWoVXF1ddbw0uqXpsQOA0tJSuLu744cffkBAQABGjx6NDz74ACtWrNDhkmmfNsYuJiYGn332GZYvX44zZ85g27ZtCA8PxyeffKLDJRNDJpOVuy9JUoVpj6vOPOq8rqHR1tg9NG3aNJw/fx6bNm1SO6O+0vTYJSYm4u2338b69esrrNk1Ntr4vSstLUX79u3x+eefo127dnjjjTcwadKkGv3/YTTlpnHjxoiNjcWDBw+QmJiIkydPQqlUws/PDwAQEBCAs2fPIisrCykpKYiKisL9+/fLHn/c33//jZs3b2LIkCGwsLCAhYUFfvnlF+zatQsWFhb4+++/dbl4WqXpsQMALy8vNG3atNx/4i1atEBqaioUCoXWl0lXtDF2H374IcaNG4eJEyfi2Wefxb/+9S98/vnnWLhwIUpLS3W1aDrl6ekJABU+zaWlpZV9MvT09IRCoUBmZuYT53mcq6srzM3Nn/q6hk5bY/eo6dOnY9euXTh48CDq16+voeTiaWvs4uLikJaWhoCAgLL/P2JjY7F06VJYWFgYxeZ5bf7eeXl5oWXLluWmtWjRokYHARhNuXmoTp068PLyQmZmJvbu3Ythw4aVe9zBwQFubm64evUqTp8+XeHxh5o3b44LFy7g7NmzZbehQ4ciMDAQZ8+ehY+Pjy4WR6c0NXYA0K1bN1y7dq3cf8ZXrlyBl5cXLC0ttbYMomhy7PLz82FmVv5P09zcHJIkQTLSS8H5+fnB09MT0dHRZdMUCgViY2PRtWtXAKqiKJfLy82TkpKCixcvls3zOEtLSwQEBJR7DgBER0c/8TmGRltjB6g+YU+bNg3btm3DgQMHnlrKDZG2xq5v374V/v/o0KEDXn75ZZw9e/apa24NhTZ/77p161bhlANXrlwpu6h2tdRqd2Q9EhUVJe3Zs0e6fv26tG/fPqlNmzZSp06dyo7W+fXXX6WDBw9Kf//9t7Rjxw7J19dXGjFiRLnXGDdu3FOPoDDGo6UkSTtjd/v2balu3brStGnTpISEBCk8PFxyd3eXPv30U50um7ZpY+zmz58v2dnZSZs2bSp73caNG0svvviiTpdN03Jzc6X4+HgpPj5eAiAtXrxYio+Pl27duiVJkiR98cUXkoODg7Rt2zbpwoUL0ksvvSR5eXlJOTk5Za8xefJkqX79+tL+/fulM2fOSH369JHatGkjFRcXl83Tp08f6dtvvy27v3nzZkkul0tr1qyR/vrrL2nGjBlSnTp1pJs3b+pu4WtJ1Ni9+eabkoODgxQTEyOlpKSU3fLz83W38LUkauweZ4hHS4kau5MnT0oWFhbSZ599Jl29elXasGGDZGtrK61fv77a2Y2m3GzZskVq1KiRZGlpKXl6ekpTp06VsrKyyh7/5ptvpPr160tyuVxq0KCBNG/ePKmoqKjca/Tq1Ut65ZVXnvgzjLXcaGvsjh07Jj333HOSlZWV1KhRI+mzzz4r9wttDLQxdkqlUlqwYIHUuHFjydraWvLx8ZGmTJkiZWZm6miptOPgwYMSgAq3h8teWloqzZ8/X/L09JSsrKyknj17ShcuXCj3GgUFBdK0adMkZ2dnycbGRho8eLB0+/btcvP4+vpK8+fPLzftu+++k3x9fSVLS0upffv2Bncos6ixq+xnApB++uknLS+x5oj8vXuUIZYbkWO3e/duyd/fX7KyspKaN28u/fDDDzXKLpMkI13PTURERCbJ6Pa5ISIiItPGckNERERGheWGiIiIjArLDRERERkVlhsiIiIyKiw3REREZFRYboiIiMiosNwQERGRUWG5ISIiIqPCckNERERGheWGiIiIjArLDREZvHv37sHT0xOff/552bQTJ07A0tIS+/btE5iMiETghTOJyChERkZi+PDhOHbsGJo3b4527dph0KBBWLJkiehoRKRjLDdEZDSmTp2K/fv3o2PHjjh37hxOnToFa2tr0bGISMdYbojIaBQUFMDf3x+JiYk4ffo0WrduLToSEQnAfW6IyGhcv34dycnJKC0txa1bt0THISJBuOaGiIyCQqFAp06d0LZtWzRv3hyLFy/GhQsX4OHhIToaEekYyw0RGYV3330XW7duxblz51C3bl0EBgbCzs4O4eHhoqMRkY5xsxQRGbyYmBgsWbIE69atg729PczMzLBu3TocOXIEK1asEB2PiHSMa26IiIjIqHDNDRERERkVlhsiIiIyKiw3REREZFRYboiIiMiosNwQERGRUWG5ISIiIqPCckNERERGheWGiIiIjArLDRERERkVlhsiIiIyKiw3REREZFT+H3GzXaB9yFuvAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "
    "
       ]
@@ -1292,18 +1292,18 @@
       "Error: 0.06844519414009445\n",
       "Bias^2: 0.06453579006728322\n",
       "Var: 0.003909404072811221\n",
-      "0.06844519414009445 >= 0.06453579006728322 + 0.003909404072811221 = 0.06844519414009444\n",
-      "Polynomial degree: 5\n",
-      "Error: 0.05227921801205679\n",
-      "Bias^2: 0.04818727730430286\n",
-      "Var: 0.004091940707753925\n",
-      "0.05227921801205679 >= 0.04818727730430286 + 0.004091940707753925 = 0.05227921801205679\n"
+      "0.06844519414009445 >= 0.06453579006728322 + 0.003909404072811221 = 0.06844519414009444\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "Polynomial degree: 5\n",
+      "Error: 0.05227921801205679\n",
+      "Bias^2: 0.04818727730430286\n",
+      "Var: 0.004091940707753925\n",
+      "0.05227921801205679 >= 0.04818727730430286 + 0.004091940707753925 = 0.05227921801205679\n",
       "Polynomial degree: 6\n",
       "Error: 0.03781367141738902\n",
       "Bias^2: 0.03365768507152769\n",
@@ -1661,16 +1661,16 @@
       "Mean squared error on test data: 1371.99051150\n",
       "Degree of polynomial:  20\n",
       "Mean squared error on training data: 0.00137818\n",
-      "Mean squared error on test data: 1887.86252988\n",
-      "Degree of polynomial:  21\n",
-      "Mean squared error on training data: 0.00118508\n",
-      "Mean squared error on test data: 14859.69908626\n"
+      "Mean squared error on test data: 1887.86252988\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "Degree of polynomial:  21\n",
+      "Mean squared error on training data: 0.00118508\n",
+      "Mean squared error on test data: 14859.69908626\n",
       "Degree of polynomial:  22\n",
       "Mean squared error on training data: 0.00092647\n",
       "Mean squared error on test data: 876.51191552\n",
@@ -1685,16 +1685,16 @@
       "Mean squared error on test data: 128664.31650694\n",
       "Degree of polynomial:  26\n",
       "Mean squared error on training data: 0.00076905\n",
-      "Mean squared error on test data: 19003.94822514\n",
-      "Degree of polynomial:  27\n",
-      "Mean squared error on training data: 0.00068946\n",
-      "Mean squared error on test data: 2379.66219404\n"
+      "Mean squared error on test data: 19003.94822514\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "Degree of polynomial:  27\n",
+      "Mean squared error on training data: 0.00068946\n",
+      "Mean squared error on test data: 2379.66219404\n",
       "Degree of polynomial:  28\n",
       "Mean squared error on training data: 0.00062595\n",
       "Mean squared error on test data: 4082.19983530\n",
@@ -1707,9 +1707,9 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31624/626635268.py:73: RuntimeWarning: divide by zero encountered in log10\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10962/626635268.py:73: RuntimeWarning: divide by zero encountered in log10\n",
       "  plt.plot(polynomial, np.log10(trainingerror), label='Training Error')\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31624/626635268.py:74: RuntimeWarning: divide by zero encountered in log10\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10962/626635268.py:74: RuntimeWarning: divide by zero encountered in log10\n",
       "  plt.plot(polynomial, np.log10(testerror), label='Test Error')\n"
      ]
     },
@@ -2051,7 +2051,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31624/3817475779.py:63: RuntimeWarning: divide by zero encountered in log10\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10962/3817475779.py:63: RuntimeWarning: divide by zero encountered in log10\n",
       "  plt.plot(polynomial, np.log10(estimated_mse_sklearn), label='Test Error')\n"
      ]
     },
@@ -3725,9 +3725,9 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31624/4162706317.py:6: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10962/4162706317.py:6: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.\n",
       "  cb = fig.colorbar(im)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31624/4162706317.py:7: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10962/4162706317.py:7: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
       "  cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)\n"
      ]
     },
@@ -4037,9 +4037,9 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31624/3777801602.py:6: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10962/3777801602.py:6: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.\n",
       "  cb = fig.colorbar(im)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31624/3777801602.py:7: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10962/3777801602.py:7: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
       "  cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)\n"
      ]
     },
@@ -4116,9 +4116,9 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31624/438060758.py:9: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10962/438060758.py:9: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.\n",
       "  cb = fig.colorbar(im)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31624/438060758.py:10: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10962/438060758.py:10: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
       "  cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)\n"
      ]
     },
@@ -4203,9 +4203,9 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31624/3544313922.py:8: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10962/3544313922.py:8: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.\n",
       "  cb = fig.colorbar(im)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31624/3544313922.py:9: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10962/3544313922.py:9: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
       "  cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)\n"
      ]
     },
@@ -4266,7 +4266,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "  0%|                                                                                                                          | 0/10 [00:00"
       ]
@@ -107,7 +107,7 @@
     },
     {
      "data": {
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\n",
+      "image/png": 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\n",
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    "
       ]
diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter6_1_1.png b/doc/LectureNotes/_build/jupyter_execute/chapter6_1_1.png
index e99c397e8..df0446359 100644
Binary files a/doc/LectureNotes/_build/jupyter_execute/chapter6_1_1.png and b/doc/LectureNotes/_build/jupyter_execute/chapter6_1_1.png differ
diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter6_1_2.png b/doc/LectureNotes/_build/jupyter_execute/chapter6_1_2.png
index e61c9dd9b..51b2a3e78 100644
Binary files a/doc/LectureNotes/_build/jupyter_execute/chapter6_1_2.png and b/doc/LectureNotes/_build/jupyter_execute/chapter6_1_2.png differ
diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter8.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapter8.ipynb
index cdece0b6f..427e843d9 100644
--- a/doc/LectureNotes/_build/jupyter_execute/chapter8.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/chapter8.ipynb
@@ -295,10 +295,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "-0.10776220958055382\n",
-      "3.743189104728408\n",
-      "[[0.82379443 2.29894362]\n",
-      " [2.29894362 7.75174305]]\n"
+      "0.04570437990371566\n",
+      "4.420442688206847\n",
+      "[[ 1.01597952  3.06059304]\n",
+      " [ 3.06059304 10.1387933 ]]\n"
      ]
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    ],
@@ -340,10 +340,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.0704374681593734\n",
-      "1.3273472571412799\n",
-      "[[1.         0.58076367]\n",
-      " [0.58076367 1.        ]]\n"
+      "0.07663067400487368\n",
+      "1.9423652864980914\n",
+      "[[1.         0.72782592]\n",
+      " [0.72782592 1.        ]]\n"
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@@ -397,30 +397,30 @@
      "name": "stdout",
      "output_type": "stream",
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-      "[[-0.92200223 -1.78838813]\n",
-      " [-0.90854751 -2.66047048]\n",
-      " [ 0.83618601  2.91748202]\n",
-      " [-0.88821402 -4.10035098]\n",
-      " [ 0.44781662  2.48685204]\n",
-      " [ 1.20493234  2.32729105]\n",
-      " [ 1.02509184  2.42265837]\n",
-      " [-0.84210141 -3.82012236]\n",
-      " [-0.01031541  1.40111899]\n",
-      " [ 0.05715377  0.81392948]]\n",
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+      " [-1.69758412 -6.29866668]]\n",
       "          0         1\n",
-      "0 -0.922002 -1.788388\n",
-      "1 -0.908548 -2.660470\n",
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-      "3 -0.888214 -4.100351\n",
-      "4  0.447817  2.486852\n",
-      "5  1.204932  2.327291\n",
-      "6  1.025092  2.422658\n",
-      "7 -0.842101 -3.820122\n",
-      "8 -0.010315  1.401119\n",
-      "9  0.057154  0.813929\n",
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+      "9 -1.697584 -6.298667\n",
       "          0         1\n",
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@@ -461,37 +461,37 @@
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       "     0         1         2         3         4         5         6         7   \\\n",
       "0   0.0  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
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-      "3   0.0  0.075684  0.080233  0.079381  0.079948  0.080284  0.072483  0.072285   \n",
-      "4   0.0  0.075421  0.080607  0.079948  0.080953  0.081677  0.073541  0.073640   \n",
-      "5   0.0  0.075030  0.080750  0.080284  0.081677  0.082746  0.074340  0.074708   \n",
-      "6   0.0  0.066467  0.071252  0.072483  0.073541  0.074340  0.068082  0.068257   \n",
-      "7   0.0  0.065808  0.070964  0.072285  0.073640  0.074708  0.068257  0.068650   \n",
-      "8   0.0  0.065215  0.070694  0.072098  0.073720  0.075030  0.068406  0.068997   \n",
-      "9   0.0  0.064696  0.070461  0.071942  0.073802  0.075331  0.068551  0.069320   \n",
-      "10  0.0  0.057462  0.062071  0.064444  0.065735  0.066768  0.061869  0.062273   \n",
-      "11  0.0  0.056898  0.061747  0.064145  0.065645  0.066870  0.061826  0.062390   \n",
-      "12  0.0  0.056418  0.061484  0.063905  0.065593  0.066992  0.061813  0.062523   \n",
-      "13  0.0  0.056019  0.061281  0.063722  0.065582  0.067139  0.061833  0.062675   \n",
-      "14  0.0  0.055697  0.061138  0.063597  0.065614  0.067315  0.061888  0.062852   \n",
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+      "5   0.0  0.086394  0.093218  0.092452  0.095821  0.099275  0.087175  0.089365   \n",
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+      "10  0.0  0.067272  0.068624  0.079437  0.079971  0.080322  0.080193  0.080702   \n",
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+      "13  0.0  0.071587  0.074211  0.083015  0.084278  0.085427  0.082657  0.083630   \n",
+      "14  0.0  0.073256  0.076413  0.084340  0.085908  0.087393  0.083511  0.084678   \n",
       "\n",
       "          8         9         10        11        12        13        14  \n",
       "0   0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  \n",
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-      "3   0.072098  0.071942  0.064444  0.064145  0.063905  0.063722  0.063597  \n",
-      "4   0.073720  0.073802  0.065735  0.065645  0.065593  0.065582  0.065614  \n",
-      "5   0.075030  0.075331  0.066768  0.066870  0.066992  0.067139  0.067315  \n",
-      "6   0.068406  0.068551  0.061869  0.061826  0.061813  0.061833  0.061888  \n",
-      "7   0.068997  0.069320  0.062273  0.062390  0.062523  0.062675  0.062852  \n",
-      "8   0.069522  0.070009  0.062631  0.062894  0.063159  0.063434  0.063723  \n",
-      "9   0.070009  0.070645  0.062963  0.063359  0.063747  0.064134  0.064527  \n",
-      "10  0.062631  0.062963  0.057231  0.057361  0.057502  0.057657  0.057831  \n",
-      "11  0.062894  0.063359  0.057361  0.057613  0.057864  0.058121  0.058388  \n",
-      "12  0.063159  0.063747  0.057502  0.057864  0.058216  0.058567  0.058921  \n",
-      "13  0.063434  0.064134  0.057657  0.058121  0.058567  0.059004  0.059439  \n",
-      "14  0.063723  0.064527  0.057831  0.058388  0.058921  0.059439  0.059949  \n"
+      "1   0.077847  0.079975  0.067272  0.068609  0.070043  0.071587  0.073256  \n",
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+      "3   0.087845  0.089414  0.079437  0.080572  0.081762  0.083015  0.084340  \n",
+      "4   0.089793  0.091796  0.079971  0.081322  0.082754  0.084278  0.085908  \n",
+      "5   0.091685  0.094163  0.080322  0.081908  0.083604  0.085427  0.087393  \n",
+      "6   0.085877  0.086774  0.080193  0.081000  0.081821  0.082657  0.083511  \n",
+      "7   0.087254  0.088416  0.080702  0.081647  0.082621  0.083630  0.084678  \n",
+      "8   0.088665  0.090123  0.081150  0.082248  0.083393  0.084594  0.085858  \n",
+      "9   0.090123  0.091913  0.081538  0.082805  0.084141  0.085557  0.087063  \n",
+      "10  0.081150  0.081538  0.077571  0.078106  0.078624  0.079125  0.079606  \n",
+      "11  0.082248  0.082805  0.078106  0.078732  0.079353  0.079969  0.080577  \n",
+      "12  0.083393  0.084141  0.078624  0.079353  0.080089  0.080832  0.081584  \n",
+      "13  0.084594  0.085557  0.079125  0.079969  0.080832  0.081718  0.082632  \n",
+      "14  0.085858  0.087063  0.079606  0.080577  0.081584  0.082632  0.083726  \n"
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@@ -916,10 +916,10 @@
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      "text": [
       "          0         1\n",
-      "0  3.949162  1.987722\n",
-      "1  1.987722  2.004480\n",
-      "[[3.94916237 1.98772232]\n",
-      " [1.98772232 2.00447992]]\n"
+      "0  3.987648  2.034723\n",
+      "1  2.034723  2.038727\n",
+      "[[3.98764765 2.03472297]\n",
+      " [2.03472297 2.03872663]]\n"
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     }
    ],
@@ -949,13 +949,13 @@
      "output_type": "stream",
      "text": [
       "Centered covariance using own code\n",
-      "[[3.94916237 1.98772232]\n",
-      " [1.98772232 2.00447992]]\n"
+      "[[3.98764765 2.03472297]\n",
+      " [2.03472297 2.03872663]]\n"
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     {
      "data": {
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+0qmH9ubGrCt/2U8972VgeALtzY2q1TZThKpFiCtNIdGFjuZGPH33Jt/2452tOHpiEucSmZSTq5tGY6dI9P4I+rlb9x02osUWOfK8sF4hQaJQb1+nXVzRtVpZ8GuhbbjWCmcpRAghVaGUV4ZaNNgnegChiixt7BOxtMbqgW5yuyyKOhKgC0ilNkTSIbeu7zCiRiPeIK76Dbtz5bCKhOj3oiMegF/g2EWysj92lCfX5F5Z/O39iUUiJuKw9+CxrFoYOxIWtqU6X5eLfk+1XndRK23DtVY4SyFCCKkKpbwydF0J6xO9tiuXttGg7bkWYft1JXog/hftaT8MeU170V29qgEdzY2mDmRwZMInaoTBkUk8/6uXfftjC4tYJIKk55niUzu9kUh62LCmBfHOTArHjh4AMPsDwLiy6rkxrsVIFwTr/bbFmV2zIe9DiHe24MD2eKiW6jBdLvHOwn1iKk212oZd1FrhLIUIIaTilOPK0HUi1VfwrlqIMDNNXK+rHUxjkQhuXd+R1Z2hOfXyWd+QOACm1kP+lnSN7Oedmy7xLa6CCACxc8+X3hARY4uQW9L7LK+l9y9XesauCZF90ukWO5LiEglhhWjYLpdar7uoVttwELVUOBut+BYJIUuaoCtDKX4UL4lCcZ3o5QoeAKKRTFeJIDNNgvZLXtee2WKnaQaGx30RgHX3PpW1yEvxrPDsyKQZFgfA1IxIzQYAX3qpb8s6aI9RD6nUyu7+IWzbP2jabyVi846HDgNIiSbtTipzYWT/OpobTXTF1bljv28tQvT+aOM0/Rn3bu4278NVFDt0/43z/u5doqbWuCvHIp+vrbhcuL6basCICCGkopTryjDfifxD1/tTBWFnmty1ZV2Wu6jtU5Ey7GozC6qIB4lyiGCRmgzA3zGTMflqQU9Xm6/dVHj86CjsT0ZeKxNlafP5pgAZ0STof0cAPH33JpMiEb+Pax84hPbmRme0CADaVzXi16fP+sSYNjJLJD1fa7EWCbJ/duuu/m4KWRBrpe5iIVILhbMAhQghpMLYgkFX8Nsnwa37Ulf1ekEU9h48hmdeHMc1l7RlPW9P/xCeeH7MTIQFUouczFIRtGtqLJLd5SIdLCKeMl0nLT4vEXl9WfwFiXLsuN6d0gBSUQ15HRnQp1+no7kRHS3LTfRE/m3T3tyYNQBPFn0gJbzsAlkPwHUPHPItRlI7MjY1YwbSyWclwioSga8tV1qHdW1NPtOynq423/4XI0Rrqe5ioVFLAo5ChBBSVYIq+KX9U/7d09VmhIFecKXWQ+6/a8s6HDkxabw55DV1rYV4VYhrqtRb2JNgJVIhJ2vpfjlyfMrURcjra6Eh6RjZlpzwDw9POKMaTQ115vm6jqSpoQ6jUzPmvci/7cJTICV6xpR5mxYhImzsLh0gFZFpaqhDU+N5mJ551aSPRCBd98AhrFbpI9mn9uZG3Lq+A0C2iZg2OJPPJkxhcKELYK3VXSwUak3ARTzPcWTWCNPT01i5ciVOnz6Npqamau8OIaRM5Lo6s0279L+lW0LQ0YkVDXU4c3Yuq3sDSEUZkp6Hky+fNa8pC73ubnEVbuo0hkyM1a8vQ+C0GZnLN8SFRBb0bBb7uZK+sVM3+jVGJ18xgqK9uTEdLcm00+qOmVzoVJL+u6mhDu+/rjMrRaW/E9lPbZ7mIfMd6YnDjFpUlkr4iBSyflOIEEJqAhEfEu63Fzg7AmAPUQPge64rFdK3ZR0ePzpqFmGXa6oUlnrIiAo5aduPkwVdPz7emXEGFTFiL+hBQkDPgtEiSBu0uT6LXOjH626ZXOIon1CR/dLW9AB8aSD5vPR8HdtmniJk8VLI+s2uGUJIwezJ0eGw9+Ax7Am4Ws+FruCXeg3pprEXXimq1MPU9HOBVLeIdKQAKZEiIqSpoS5r29FIJp2ifTWOnJjE7v4hXPvAIezuHzJdJgB8IiQWiZhoQCwaMR07Hc2NPhEiz9P7JkjqRASW+J2cSyQhI2X0Z1Ffl/sU3rdlHZLp19Tiom/LupwRmjAiZNv+QYxOzaC+LoqB4Qk8axmYaQ8XICOyunY+WfMipBzHNwmGQoQQUjBS12GfrGXx0YPYwiILrixY2/YPAkiJBN1+CmQW7ITnIRpJ1SBIwaV0cOw9eAwvz7xqnnMukTQC4Cf3vQk9Xa145sVx7D14DFv3HYZdTmD7fuj6CC1k9FRbe4GNd7ZkUiSrGn3iI19qZGB4Al99fsykYP712hZfEWsEwOxcMktUafaYjp5WE7mQLiEb+zMOYnBkwteZc8VFqwDAdNBIUa+dMhve9RbzOUVQ2/Ub5Ti+STBMzRBCiiKorqOYK90g51I75G+jHT5dtRku4p0tprBTRwkKTXno5+ri1KaGOly2emVW2sh2Iy2UVGRo3Ffr0dRQh5/c9yasueeboZ7fu7nbGLFpdO2HHb0JwlUUC/g/CyD1Od18ZbvpYtJpLZmBE9T9BFRn9olst1TH91KENSKEkLKiPSJcdR2FLBxBJ3h7gQsjFOQxhSyoYV876Hky5t5egHXhqX2fEIsAiRBnYLswV4sg+70GbctVtKufX18Xxexc0vFMt4jSr5evGFfep2xL9l/+lufbk3OfPT4RWNBaCYHiqluiCAkHa0QIIWVFt9xqZ0YABYeug1owZVYKkKkJ0amJnq5WtK9qNP8GYOpG5Oo+LC9NvuL7O2L9P+f+KxGiHz8wnFpEdU0JALNfsjjny4hIG6183pIe6tuyztSfyOfd1FDnFCGAf3Ce/mwuW70STQ11gSJE3qP9eT74zy8aASltvPZ+SypKxNb02Tn0dLXilvTjRUDdur7DpHTEJfbIicmswXlCvhRJqWo8asV5dLFDHxFCSMHYngNysrZD2fYVq6ttUBtgSYg+kfTw1efHfK/d0dxoZsckvdRgN/H8kMFnYtglYkSTKzWir+blKl3bq9uI8NAD4HTRKuB3ThXsSAAA5ItJ2+9DRNZjR0dN3Uoh0Z8IYFpvgUzaS4zbbDqaG3F65tWs1M25dH2KfNeu/Xbtlz33RtBzbHQRMlD4cLZSTZetFefRxQ4jIoRUgcVWlW8vYEFXrGGKAHenXVHFnGvo/htNseWpl2fSkZE2nwmW/FvcPrXgWBaLpjpQ0mIhH9Np75Hzm+oDH6PfrQgfiYgkPC9rW9EIzHtwiaRCmT47h99NpzxQXn9hKuzd09Xqe93VqxrQt2Ud2q3uHA8ZAzKJYknxqF2wKsJJ0iny+vKa02fncF26myiIXJ+5WMLLfB4t7CQCoecQyQwfPUE5iHhni+9YK7TGo9TzcEgwFCKEVIGFXpUv+xnvbDGLr90xIe2nGtdwO33CP7A9bhZsSUlIrYg2GgPgE2tb9x02r3HnpkvM7RGkumXk+VIkCaRSIy5SnTLjJkqSr0UWyKQu9Lv9VyqVkfRSqSZbJM2HcwkPR09MmRoKncoCgHdsuAgAAms3pNNIF4+KABBSwinzb6QfNzY1g3hnC5oa6vDb6bPm8bbogXrtIOy2bW0dL8eIK0WS7zekZ/9oAVOoCCnlYEbihqkZQqqAy055IVXlS13H4MiELxLwuW+nTtDt6bZXe3GU5+o2Ul0EuHXfYYxOvuIrHpWrZLEVT6o2WSC1aOhaArEWt1MWImKSnofRqZnAItHRqRmcOp1ZXHPVTmhkW3anj+xHrqhBsdi1OfHOFpxM28Dr7elOHsGIMw8YezljHW93HtmfkxaDdmTnopblWcKnvi4a2Kqc8hmZ8IkQ6caxBw0GpUjy/YbkGCukxoPW8ZWFQoSQKqFPpLmmv9YaumNGt6iKGAHg64CwEaMviVbo0fAiKC5ufY2vpkP+f1F64JsOzQ8Mj5uuC22z/tLkK2ah1B0hQQWQvsm4yWwTMJug+8RevaerFT87edrZkRKJ5K8NyYWvVkPV5gDpYmGrHmb67JyZeCtCBciIirGXU39r8ZcLl+W+9lzR2ELOtq63o10DwxNOoZFrOFvQb6jYGo9cnTi1/vtciDA1Q0gVWYhV+RISt9MptndE0Hvp3dzte7yMhpfXk8VI0j0aWUwB4NnjE8a5U7ou9OP0lfnsXNI4pTrfk1W3EYtGsCLdnRKUVrBFSETdLu22UuAp2xc8D6HTb3YKSbfAavTk3qD0TzQdWWrypY08XzeS7ZDqeo/C0RNT5t/68+jbss5pkBbvTBUY286ungdzLLnSH/HOlsAUSdBviDUeCwdGRAipIguxKl9PoQVk0qrf0Gp0aibwvcjj7fSLy7zMtaC+cGoagyOTodpzdZTAtTR3NDfit9OzWd0xiaSHVY3n4czZuVDD4QB/AeuzI5N+0TQymbX9sOF9OzUi4ma1FZGxJ/fGO1sQTXefAJlIhIgumZ47MDxu9lWmCuuIivYliVq3GzfbpGdul1SRy3dEolhyDKX2q818Fnv6hzKFx+nb8qVIXL8hIHsicDWny5Lc0NCMkCqx0J0b7Rw+kGm11QPpDmyPZz1HahG++J1hZ4usTnvkarvN17rqmtAb5rlhp9O6ticGZ7FIBDuu787adrEOq/V1UbT9q2U4+fJZ3+0uAzMRGTIbJ+i9yHPbmxsxnW7R1dtz1cdEAKwI0TIsqRdd+yHfhX2MF3vsB/2GZPJvLTm1LjVoaEZIjbMYqvLtk7wOgYu7qeT7BSlUlWFyutgSSC1yfVvWYbVKO8giZqciIkhFB3LNSNFGYDZBC2mu4spciPeFjigMjmQP6yu2a2Z2Lokz1j5Lp479CUjaQzqQgvbXU4+XWhYglTYKKtL14P7s7AiV1AkN73qLidQ8fnR03m21Qq7fUFAdkDyGIqS2YGqGkCqwGKryg8SSNqbqaG70hcKlUFWPuQcy4XIPqVoHGdRmF0/qq3/draPJN58miLBX+kHIfkg0Yv/TI+lUSCa9Mt/W3SBzs6bG87JM2fJ16dj7EotkoiD6+NORkaAoUq7o0t6Dx0wb8OjUDDpalhvBrQtMddrG9Rp2FGMx/IZICqZmCCEFo0PgMkAO8NdkSKsskOpUsdM29mN1CkW3kUqqQxAxsiwWwbkcg1qiEWRN1JXXLmauTKHoxTnXHJdcBKV3qo3+DF2pIXuGjH7ehjUtuGvLOqy79ykTERu6/0Zs3XfYOVdGH2uP3HZ1Bd4dKQVMzRBCyoYOiT9y29W+OSO6mybe2ZqObLT5uho2rGnxiRAJ2w+OpAomxYPkiefHTKpDc3U64pFLhABuESLpokqgIwTFiBAgFbX40tMjBT2nUCu8WCQSqvBXXjcWiZjulmjEL0LkMZIyG7M6aQ5sj+OuLeucBaY9XW0A4EzbADD3F8NiczJebFCIEEIKwg6JS15ekJoNESQDw+PGsOpcIolnj0+Y19GzVwaGJ/DCqWm0Nzc6Cyy1d0kxRCLZqY325saCF+5Ko4fahXF5DZJnQe3CCc/Lm46qr4umo1ApZ1hZ1JNe5vuWmhMRNbbNvhSTahfcoftvNDUjgD+iJm6ocvt8CrgXupPxYoepGUJIKFwD6+T2J54fyxIO4ryqhYMO6dspkqA6g3hnC144NZ01dK0UlPr1yomkd4KG05V6OzYdzY14+u5NVpSi1Vfz40p76a4Zuc82HXMJEcHuvCqWhd6lttBgaoaQBUythpGDripFhDQ11Pk6YB4/OmoKVsVPI8gvRA9U0/R0taKnq80nQkp59bpQRAiQSe9UQ4QAGW8YjfiPSKrGFa1KeJ5Jv61oqENPV6tv4dcRtceOjGY93zUmoBiChudRhFQfChFCaoxSh5FLIWz2qCtWvW/b9g9idGoGy2IpkyzdjiviJOU02mYWG9cVc1CLqbTfigiRKbEkQ5j6jrDkq2V59MhLGBgeR09XK1ao7abs+f3fqx6AJw61Z9JW80HH49jLGZt+neIrVTv7QnQyXgqwfZeQGmO+A/HsFIoIG0HaIPVr5ns9GYKmx7ZL6yUArF/TkhmSpoTC9Nk55wAyIOPA6TI0s5HIBSVINuWI6jQ11OH913VmdeucOTvn8+hwpVKkfdm+3Us/fmB43Hls2504w7veYu5zHb9BqUIg2LRsIToZLwUoRAipQeYzEE8Lj97N3c7hYYUIG+39IW2UUngKZIad9aUXB5dXhogZLTrCCJAwzHeAXC2jPUgqyfTZOTx46EXfbfV1UZ/okRSLjlbU10Xx9ivbAWSON1n0Pc9D7+ZuJJIeopFI1rH92NFMWsZDKtomtSG7+4fw2NFRY5IGZI7zwZGJQPdebRkfVCMC0O692lCIEFJBCrmK693cXdQI83wzNQoVNvr1XMWIq5sbccv6DgyOTPgiFuKBkUh6vrHyrhqR+RRgLlYRApRWhIT9jNubG3FyasYIRUmLvWoJx4HhCXTtfNJXiDo7l8SDh140YtRe9PcePGaErYhZiYxpLxopchYxIn/b3Vr24+zoitSXBLmwApw9Uwuwa4aQChIUiXDdLrfJFaW+L4ygkStG/XwtbP7zv+0qKLTtmi0jJ3w9EXZ0asaYXOVyOS1390c+ijUZmw+xCHDhquLm2FSTXNb02tBM/3tFQx1+et+bzOO0YZkICNsMTTpk9h48ZiIgsu0gQ7Nt+weNsNGiqNDfC23fS0sh6zcjIoRUkLD1H/nCyHb6RbDdKbXweOzoqC8/rqMUQHDtiJzEXcjzJWQ/OjWD9uZGXNSyHAPDmdbd+roo/r8V9fj9mdmKdH+EodIiBEhFOH53Zrbi250vWoTYUTG5x/Z+WdV4nu81erraMDgyaQSG/Xjxkrn2gUMmBfO5bx8z2+7panOKhgPb4yYyAyBLhADIKTIYCak+FCKEVJh89R+FhJFtQaMLCe3CPAl937npEvNcqfsAUn4dcnUp29aFqraZVpAHh7Z8F2bnklm3LUXmE4UpphamkO3le2xQAatgiwpp99XH8ONHR9NdVKnIx9p7vmmEjI6oAamZQ1oA6fSMZu/BY77HxSLuyAepXShECKkCueo/wg7zChI0chuQbSqmC07tK9tnRybhIXVlK9uQfD6QEhMd6XqQ3f1DWSKkqaEOl61emeUPUktpCNdclEpiL/T55uVoikmiFyJ6wjx2YHg872NEoGqRC8AXCZH6EtseXiJqY1MzPvMzOYZTUZPxLNEux7IMQdSFrqT2YY0IIVUgV/1HodjDw3QaRw+ak6FytgBpX9WIky/P+BaF9uZGvP3Kdjz8zHEjOIyhmKNeINd9pLYppDNHLPFFXAYJO+20mvQ8E6mTSIikUuT5+v9XW8enHMM6AqcFtx6sp0WLHPOs/6gOdFYlpIbRqZeh+2/MMgkr9LVsXwSJqGjjpgPb4+jd3I14ZwuS6QVAiESyF5OxqRl87uAxTJ+dw7JYBD1drWYREKGhh6D95L43lUWElNKsi7jJJ0J0fdDY1IwRIU0NdYHRpUz0YiJLhGzbP4iE55nZNB3NjeZ1PADPWsZo0r0jURaZTSPGaiJCpJ34wPa4EUKcI7MwoBAhpEy4HE11KFmnWIoRI0GCRk68tkABgNHJVzCYTsEI+VInkjoQp0tBrmAlFJ5PhESKWA8WkgX7YkWGE0oHlKC/GxGMuo5I3y8Te/cePGaiFcO73oKerlbjumq256Vez96eCJlHbrs6HfmYNG6uIjzkOJe23XhnC+tFFgBlvdzYtWsXnnjiCfziF79AY2Mjenp68MADD+CP/uiPyrlZQspK2FZAV2dLIplpL9QzNOz6j3yEKWjt27IOiaSHrz4/Zm6LKjVQSL1E0MRbmbYq9zc11KGp8TxnYWqlMjbVbgteCBRaKzOqIiEuJFqh0zAaEasymybe2eoTJXYr73TaCl4jQkNM+nSNlfYQKdQnh1SfsgqR7373u7jjjjuwYcMGzM3N4d5778UNN9yAF154Aa95zWvKuWlCykahjo52262rvVDuD0tQQauwelWD2VcRBbIfkmsPsxCFKTbVV77TZ+dCRzHKJRgoQtzorhgPxbm2imiQNJx+jSCxqp+na5XinS3mGBaR4kKieu3Njb7iV5dVezEGgKT6VLRY9fe//z3OP/98fPe738W/+Tf/Ju/jWaxKahU5cWoDJl0IGtaYrNRozw+JmAwMj5ur1ELG3hfyWEGLi3wdISsa6rCq8Tyn0Kl2d8tiQ76XVLdUmzkmihGDujgUCBarusMKgO+3AWSOT/k3AGP1rl9fP0ZHXWyPHXn9SvzOSH5q1tDs9OnTAICWlhbn/bOzs5idzZj9TE9PV2S/CCkUKbpz2VzbJ8BKXqnZ3QFyghbCCgvX4pKvGFWuuEXA5GtLPXN2DmcC9ocipHTo7zKa9tjo3dyNP73vn4qqwdGlPi7LfyAlYp++exP2HjyGeGcLBkcmjXDXXSw6wiGCQ4sQaQHWwxblsS6rd/vCQO4ntU3FhIjneejr68O1116Lyy67zPmYXbt24WMf+1ildomQgrBrQ8I4OgLVm/ipa0YK7WhxXeHme74WIaR20E6uUmcxODJhvqd86beO5kb8TjniitfHrSraYTN9ds5njidRmA1rWnyzlMQ5VebNZDxu2gAgHcXJiJF4Z4t5rqBrTTas8d9PMbIwqJgQufPOO/GTn/wE3//+9wMfs3PnTvT19Zm/p6en0dHRUYndI8SJFh928andKRJB9gmvlBM/g4pkxf1Un+Q10tlSDE0NdTj7ajL0pFyKkNpjdi6J+roorrhoFQZHJn3iISiioXGJlN9Nn8VjR0azbtcpNREHdjG1II6oItAHhsdzplMSSc85ayasASDnzdQuFREiH/zgB/GNb3wD3/ve99De3h74uPr6etTX11dilwgJhavzZXf/kLGqlgiAnIC1o2OpJ34GzZexZ8YIriF1hRJGWFRjeBwpjNm5JF445U91L4tFEe90C5F89T3nEh5+d+as7zb7t1BfFzURCpsggS6REGG+4kE/L+j3Y89WIpWnrELE8zx88IMfxNe+9jV85zvfwdq1a8u5OUJKjks4iAgBMm2LumZExEi+K7VnXhz3/a1xnWSD5svoFkh7HHoYgoREWIHBlEztE4tGsr6fc4mkOUZ0JCNXqkZ/z1qoyHMkBRhB6rhwmYkVItBLKR7CDpwklaesQuSOO+7AgQMH8H/+z//BihUr8Jvf/AYAsHLlSjQ2NuZ5NiG1gWumiyBGTYC/gHXvwWOhrtRynWRtQyd53ODIhG9fxJcBgK94VlhWF8W5HILCFhvSkllIlEMWp2IGs5Hyoztj7HSMHcm4JU/thy06pSVXul3kdbRpH5AR1wBCpVL0baUSD/kGTpLqUNb23UiAleLDDz+M97znPXmfz/ZdUkvITBd91ech+6S6bf8gEkkPj/5Fdj4b8Ieb9Uk1kfTwxPNjZjqpLn6Vk7hcIco+LItFceemS8xrSPGfa2aH7jooJxGkWnMZIak9tI+HpqO50XS55DtGXEJEjjOJjLiO3/lEHkrdAm/PZyKlp2bad2t4nh4hBSGdLyIA9AnXlSrJFTIOqjsRYdPUUGdy62KcNjA8YbYp/5eTMuAXGbKPsjiI78OeCogQpN8DRUht8uxIdj1RLJIqSN178BgeP5opQA1Kt8lt9nHm8vXQx+98xEMpW+Cr1cVGguGsGULy4JsPk/YL0Vd9csLdtn/QPC6o7mNP/1DWbJnezd1mABiQOtE/8fyYER9ify3FsbLtoftvRPuqRnOyF+y8/K3rO3yvr+eBkKWBfOeSstOtsFLqsbt/yNSGdDQ3ZokQewBh0DA5Ob7FgXW+IgRwi4diX6dUAydJ6eAZiZAc2KkTKUyVE62ek6H/Dhp29+zxCWzdd9hMDt3dP+QbiQ5krlD1uHUtUtqbG01dykWtywG1bR0lEXb3D/lqRmbnklgW40TSpYKr6Pjxo6PwPARG7nQsWwbSiTARUSPCQGqZBobHzXN6N3djWSxq0ofzFSGlEA9BRbIUI9WHM7YJyYHd+aKjGHL/syOTZqy5y9XRrvDXOXjt8SEnf7lCDUps3ro+461zYHsc1z5wCGNTMzh6YjKrkwHIiBndGdFwXgznEkyfLAW0CBFRkm9+0NjUDFY01OFPVq/Mqim5442XmL9T/h9+MzHAn8qcT/qjlC3wYfxG6DVSHSo6a6ZQWKxKag37xChdMpIvt/PkruK6fAWB+Vph450t2Li21XfCvO6BQ4GLS5jBdWFgi+7ix3bg1RE2LWRtUS2RQsB/fOtZR0Guw7kW9zDCAEDJxENQUS3bfAunZopVCVls6Ksx6VAJmm8RVFynZ2O4yLfYD45MIprOvQ+OTCDpeVlCQ4uPrJkxRU69pQhZmLim7NpDBZsa6nDZ6pUmzSj3iQixu23k2I93tuDk1IzxsNGPkfbdnq42n6OrLcjjnS2BEZNcx6pL2NNrZGFCIUJIgegZGdpHRJ/EBkcmnJX5YseuoyjF8NOxl7EslhIjKxrqsk7YQdNQg+4jixeXQap9k4iQoOhZyoE1VQMi6cRYJIKerjbs7h8yRdVSNC21UvHOVt/vYmB4PGe6shghQa+RhQ9TM4QUiJzk7HSMoId9BZ10i02X5HtePjdUpldIEHJs5DpGRGDoYx/wD1YM+l2ESVfqgXe2kMiXZqHXSG1RyPrNrhlCCkCLiuFdb8mquNc+Iq7KfACm/VeQSIULu7dFbLSDyOeGOn12ju27xIe05Yr4kP+vSN+ujzY5tvWxD8C0tgMw6UqJAMpvQzppZHEHYLxtejd3I97ZYlI4tgiR1viglmHX69dCuzAJB1MzhIQkTAV/vsp8KdwTYpGIz5NEE3RlKi2RiaRXVGqHA+qIJij68SerV+LoiSmcSyRN3ciKhrrAY19HRKSFXZv3AfAt7nbaRWpJbCTCGOTPI5TKqKyUE7NJOChEyJKl0FY9LTL0c3X7311b1hnjMv1cefzGtZn5MUeOT5kT90uTrwBIiQ8PwJmzc74FQouSCJDlFZILpmNIoTQ11CHpeb6FHQD++MJUiF2PHAAy7r3yWPHISaVaMgWsdk2I1Iy4cBWEB1Eq8VDqidkkHIzRkiWLXK0FmY/ZYeC71MnJfm7v5m4jQuS5OixtZsREI3jktqsRVfNqEp6HsXRU5Cf3vQnbr+v0bdd2uSwkBiIiJFdImywd6usyBmS5HjN9ds5EJ9avaTb3RSMRX5pETP7GpmYQ72wxhmMSwQDgi/Q9eOhF30DHwZFJbN132CcAdJTELgh3UUqjslwRTTE1JKWHERGyZJlPtX2Y527dd9iczPXjHz86mp4VE8G5hGfEgkzQfSw970NC3WFOfXYRqywSImB4Al266GNjdi6JeGcrRidfCSx61oZlAJyiQtIkUhOVui1VZKqP9b4t6zA2NWOm8orJmXYhHpuayfrNSSQESImRXGmWMEZlYQkzMZuUHnbNkCXPfKrtwxqXye228ZicjCW0rQfb6WmoOmVjE5R6KdYvhCwe7C4qVy2STVNDHV5/YZMR0VIfotvN450teOS2q43YzmdW9qf3/ZPvGJXjXAzPAOCR21LTqm2TQPltsIV2YcGuGUIKoNhqe6n4dz1XTsI6J77u3qd8ImRZLGrm1owp8zEZbKdDytNn55wiRO7TSBomkfSyBpWRpYUtQpLqutOVohFR+8KpaQB+4zM9D2lwZBLr7n0qUIQA/nSlfYxKgba8lkRTrn3gkImWSGeOpC139w9h677DRX4SpJahECFLnmJb9aTuQz932/5BX52I5JYBf4GpPYND2hk10sZYqJjQAoRFqkuPCOBr8Y5GYOo2RPC2pw3I5PHC6y9sMjVJEoXTeIBvuGI+4a4nV9v7ODA8kVVgKvsnaUo9yZcsXni5RMpGrQ+Q0i6ndrX94MgENqxpCb1/d266xNi2y+slkh627juMkwH2600NdT4XVrn6tMVDMWKCAmTp4gG+tm7Jzum0jCz4dgrw56emcebsHDqaG3HhqkacennGiuJFzGuEaZOVYlZdEyL7CKRSPLqWI5fTqqSDyOKDQoSUDe0hMN8ZEOVARIj2J8g3B0ZwuaXqqMaXnh7BZatXOn0R5MQuV50DwxMmly8FrITMF1075BqyKBNztZCQx61ubsTJqZQI8c+eyRybd266xDxnYHjcKRJiUX9hat+WdcY2HUh14Qi26KfF+tKBqRlSNlwtdLU0QGrDmhZzgrSdUe2x5ja6Ut+uMZHQtkvM6DSLtEkCqVx+R3MjRQgpGSJ0gxgYnsCRE5Pm99m7udu4/A6OTJqoneuI7OlqxTMvjpvZMoMjk76Upnjp6IiITlGKaNe/PU0pXVJJ7cOuGVJ2Sj0DotTMd/9cz9/TP1SQ3wch88WeqOuivbkRt67v8EVIRChIBMU1z6inqxU/PXnapG1uvrLdF1GMd7b6DP30xYakaAFk1YSI6ZkdTan1cwbJD7tmSE1R61c389k/fdIVM6fd8xAhuebOEJKLMMfcres7zFwXQSJ3Ep2zRYgUlm6/rtPMSXrw0ItZdR8uESK3A9lD7CRi6oqmuH5TnPeyeGGNCCk7pZoBkQ+58hLrabsuRW63i2Tt/du67zB6utryFtm6rvyOnMiuCSmEYibyEpKPCFKCIDOkzj3XRdD+I9IpI2nIrp1PmnlHB7bHze8gVz1HWNMxWqwvTShESFkp9wAp3ZkjxbFylSadL/btfZYIce2f7YhqPxbIPrlKqFqHtm1DKRfaKIqQQohEgHyHjqRsBkcmTHRBrNRdRaxAtv/IwPCEEegiQvRFhYgQHVHUv027EFULev0bK6VLKlk4UIiQslGJqxtXZ454bwwMTxgra1uEyElSBEoi6ZmTozx2d/8QHjs6ilvXd5ipudJuqGfMCBvWtJjtSYolTITjtasa8OvTZ3mSJQUTRr96SKX8JIUireVho3digjY4MmkMzIDUwDrdfm6Lk2K65mixvjShECFloxJXNy5ho9tvR6dmzGyLPitXrUUHAF/OW6IaMgdD6Olqy3ki9TwYZ9R8LKuLoqEuajwdCCkl+ti+ZX2H+V3EO1vx1efHMJbuisnnOZNyPm1NPzdVWyLHf1TNjdFpGmB+s5zI0oJChJSNSl3d6BOehIh1ukMmeMpj7HkwSc8z8160CLG7B+yTsP0eYtEIxl4OLyrOzSVxLk/ahpBi0QPqEknPCIWB4fGCxe/RE6loYE9Xm0+E263uQRFPeoKQXLBrhiwKdOeLiBBtc53wPKcISaVm2rLmvUSQnVZ5dmTStBsG7QNnu5BqUl8X9Vm2dzQ3ZjkEe14qPSN+Nx3NjYHdWvJa5xKeOf7lNyQRzwPb4wDgS1dKxFGQkQa12DVHqg+FCFkUSOeLiJCO5kYkPC9rxoUYMNkhYnvei500koK/CGCuCnU74TseOozrHjhEa3VSVWbnkr5jd3RqBl99fgxA5pi/5pI2c59E/YJSifJacvxrIaELTaUeRIsR3VlmF7gSouHlG1nw6MFaOrUiRkva4XRgeMK0H9rRET3vxcZT/9fdB0DqpKtncuTqRiCkWMLUc7iYnnnVJ7wTSQ/PHs+kVB489GLeri0RIVpI6Db4XPUgAMrWNUcWB3RWJQsa+wQrPiHSSgvAV+UvbbnLYlEM3X+j7zVEyATNe2lvbjQpm3hniynUkxN0NJIaMOZypiSk2rjEQHtzY+h6kZ6uViQ9z/yGXLUetiOq63EsWF0aFLJ+MyJCFjRBnTlb9x0GAKxe1YCB4XH0dLX5TJzOJZLYtn8QL02+grGpGVOoqjsNNPVWd4u8TiT9WgDwr+pTV6wUIWShEFaEdKjfRy60p0g0Anzo+sxv057GrWtIamEaN6kerBEhCxrbEMnm4tbXYDBdZCdRDwDGZ2QsncJpTxfrvTT5CgBktebOziWdhag6bsL6EFJqIvkfEprd/UPo2vmk73egsQtWZT4MkN0GL0PvbLRLsd2dL3UkcpEgokMiJLFoxAzLI0sLRkRITVOsbfsjt11tTnA6VRIU9Xjktquxbf+gqTEZGM4+yU6fnQusIcmVvw/jrkqIi2j62J4Puu1Wusk2rGlBvLPVRC8Af5eYK2UjBacAshyKgfwuyrqORNvL63bgIH8esrihECE1gR22FeQqSp8Y9UnOZdsu6BOfFhBahEjIWUSNmJHJSbmnqxXPjkyaYr6gkeiudI5AEUKKRURIUC1HmBqPVEoyc3wmPClWbcvy3AFS9VPfv3sT1t37lBEpQKYF11XfEdZFWd+mC1nlNtaNLE0oREhNEGQHLUgKRSyldWQjyNdDkCJSzbJYFHduusSIGbGF11GNWCSS1XXjIt/9hMyHnq5WjKZThjZhajwyg+5azQgCsWu3O81EbGzbP5glUmLRCBKOIm6gMBdlXUcCgEZnhEKElIegCAfgLkzLZwct90me2mXbrl9HtiO321d95xJJXxtuBNk1HmKCFpZYCcLohNj87OTp0PVHQSlCqfawC6ntdvdMajL7dhEmO67vzvq95Tr2XZ01ejYNjc4Ii1VJWbANjgRdmGYjjoy7+4ew7t6nnIZj2rZdTl76ebI9LUJ6ulp9IkSKTmVCbywScaZcbILcJwWKEFJqxP00LPLYnq5W1Nf5Dfokcrd6VYPPdVjExoHt8VTUJP33aLqbLN6ZKWxNeJ4R8HK85/pN2+iLizs3XWJup9HZ0oYREVIWih14FTRS3HZOlRCyTPrs3dxtpoHqsK+u39BFe0LYtIoeZNfe3IjpmVcxfXaOURBSVlyt4LreyY70yW1yXLsiJFH1G5LnitiQIlYg89vQUcnHjo6aqdZP373JWaAa1IbrinDqf9PobOlCIULKRjEDr+ywrVwl6cJU/X998hKfEB1CTiQ94xXS09Vmtq3NzWxscdHUUIf3XrMWDz9zHNNn5/D7M7OYnUuyG4ZUHD1qwAN81ulyvxYmKxvPyxIiUguS8DxT7Cq/I23LLqkZuwD1ugcOYXRqBmvv+SY8ZBulBXW9SB0J4C5MlQsJ2Q5ZOtBZlZQdqb4XN9Og+hE5kcU7W3zttwCyToqukyUAX7Qj10lPWnU1sUgEr6mPWQWrQEK5pRZrs03IfAlqHbedfEVc5DtWdcG2vu1cIukT+zLUTiMiJBaJYHjXWwpySy20fowsTApZvylESFmxLZ+DxIFrLgWQckiVyIV9UhQxoa2ngwSJq4g1X9utTUdzI56+e5OZVUPIfIhEUpNwgwgzKkDEiS06bNESi0Tw2lUNpstG7hdjM/07kGiJXBDYwsHUhKQfJ6/FrheiocU7qQmCDI76tqzLGhonBmL2yUwiIwPD48bvQ17L7prRz9V1IsKe/iEzgybe2YKfnTwNIPhK0+bU6bPYuu8wRQiZN8tiEUQikcDUnq5J0mjRrWs8Xn9hk6l/ch3PCc/DRS3LjRDxkPHQAfyiR16zpys1pVe31gPZtR0SGaEIIcXCrhlSFoIMjrQA0R0yEs1wncx6N3fjkduuDuyosT0MdJ0JACNyYtGIiaCcnJoxV5BX55mfISSSXmBdCSGFcC7hOUWIdHTZfjZASjAPDE8gGomYTjA5xlO+IBNob24MTN9IS67w2+lZ8+/RqZmcdvLxzhZTUGpHHUUQbds/GOatE5IFUzOkLITNA9v1I/nI93hJ5YjJWdTyG9FFqrmKTdkNQ2oBWeQltakjFy4rdSA7wic1Izodc/TEFM4lkoHRQLswvG/LOvPbcU3WlTQp0zNEKGT9ZkSElIVcw+jkdnFvtDtkggZfBXXU6PtFZEQjEQyOTPq6a3b3DyGq/BNydbxQhJBKIoPkgExURERIT1crhu6/EX1b1oWa7KyP3J6uVl86JhaJIOl5prNM6ktgPUcbm4m4eOS2q50iBAAObI9nefkQEhbWiJB5UWwFvNRqSAGqXNWJfXuhA7WA7PZAOaFKHrxd5cTDErZ+hJD5IDVNEvGwXU2lNkqiEiKu5XnxzhacVDOSdEeMXf/hsnaX55xLJI1zsWzfTnm6RiYAbjt3QsJAIULmRdCMmFyeAlJoKsJATrJ6hgyQEjna1yDfQC0RPHvSQ/IGhid8QiLMXA4bnlJJOZGoh0QoZPFPJD3csr7DJ7oHhsfR09WGnq42JJKeSa8si0URTT8XgC9qoQWHh8xvQKdb5HnidGpP6QXCXQjY/yYkLBURIl/4whfwqU99Cr/+9a/xx3/8x/jsZz+L6667rhKbJmWmGAdVO3Khr+ykK0CLGF2MqiMw9hWYRGBi0YgvHE1ILaLNx3Qaxvbt0CZ88ruIRSO+NKWO9GlBAWQ6YuKdLfj1y2fNNgdHJnwt7/LbERJe6u+wFwKEFEvZi1UfffRRvPvd78YXvvAFXHPNNXjooYfwpS99CS+88AIuuuiinM9lserCweUXEubkpK+sdDjZFh76JGmLlFQ3zLiv88ZlWEZIrbAsFsE5Nck2l/mexvbbkecAqdTjRS3LTbTj0SMv4eTLZwH4237lN7qiPobt/6Yryx/ELuyW0Qg0ICOFUFOGZhs3bsSVV16JL37xi+a2173udfjzP/9z7Nq1K+dzKUQWFoV2wADIqvgH/EVwtvmYffLVpmTtzY24dX2HuZ8W7KQWsY30pKtFd6bo38A7HjqMWDSCeGerr+tLBIUtauzX17+Dvi3rTApU/s4VxSzEMZUQTc10zZw7dw7PPfccbrjhBt/tN9xwAwYGBrIePzs7i+npad9/ZGGQr6Ml6Dli6a6ngdrEO1t83S+DI5mTrJxQpTtAUj0dzY0UIaQm0SZiQEqIyDRbMRET7xsg43+jIyG6DmT9mhYj0F0F2S4R0tPVarpctu47jIHh8UCxEe9sYQEqKStlrREZHx9HIpHABRdc4Lv9ggsuwG9+85usx+/atQsf+9jHyrlLpAyELWQLes7gyIRvGqjLxVFcI2WyqD11NJnOr8tJuBSnTc6VIeXEHiinkTbY3s3d6UhI6riW9nPtGhzvbPXVbNidXhIR+dy3jznrUDKdOG2+fWA0hFSKihSrRqyrXc/zsm4DgJ07d6Kvr8/8PT09jY6OjrLvHykefbJKJD1z8gSyC1h1LlkXrNoTdQeGJ7D/6RGcOTsXaNsuIkSuDCVcLYV5xXTI2FCEkHLS1FAX6CQMZE+tlmiGjgLGO/0TqEVsCBFkIiIi9rUIkaJv++KBIoRUkrIKkba2NsRisazox+9+97usKAkA1NfXo76+vpy7REqM7mgJ8vZwtfK62nL1MK0zlgiQ1I99tWd3CIQxfCoF+QaWEZKP6bNzPuGu6d3cbUTIslhUdc5k0i5aUOzuH8LjR0d9Q+jsqCGQEiOubQaJH4oQUgnKKkSWLVuGq666Cv39/Xjb295mbu/v78db3/rWcm6aVAhdLV9oK6+rLVciHLFIBBvWNpsTrDZ5EjqaG02NSaGTdOcLRQiZL3L8Apnfzp7+ITx7PDVPRtdcbds/aIY0Ctc9cAi3rO9I15b4DcqkANZOL0qdld6mECR+CCk3ZU/N9PX14d3vfjfWr1+Pq6++Gvv27cNLL72E22+/vdybXtIU63g6X4KurOy0DZARMXsPHsOzxydMeiXjqdDmc4scTZ9YX39hk5kh09HcGGoQXVNDHVY2njeviEksGsHyZbGsaA0hhSCiQcS1LgSNRSPmeJZaDrsVvW/LOiPOdUeZFuO3ru8wjxExoh8XZEJoF5xTjJBKUPZZM+94xzvw2c9+Fn/913+Nyy+/HN/73vfw5JNP4uKLLy73ppc04njqmseyOy1SykXv5m7VWpi6ssq3P3oGzMb0wLrd/UNZU0FTJ9Q2HNgeR09Xa2hhMX12bt5pm0TSw79QhJASsSyWckN99rg7kieuwzoS0tHciN7N3bhlfaZ27uivptC3ZR2S6TCdpEBF6Lz3mrXo27IOG9LdNSJGtADSkUuZa8O5MaRSVKRY9QMf+AA+8IEPVGJTJE0xjqelIteVlWt/XB4hOr/tyne7huJVAmZkyHyRqIV4f4xNzZhxBtK2K78J3UHW1FCH0akZ49cjjzk3l8yq69iT53euI6J0TiXVpuyGZvOBhmbzp1jH00LQaSD7pGaPB9cFqQnPM2kb/fzPfnsISS8zcE6PL5eTuDy/vbnRZ1tNyEJEd8DIb0UEh0aOey3M5d+FGAlqqpXGJYubmnJWnQ8LSYjU8o+5GMfTQnBFNoIiHr2bu9G180lzMh3e9Zas1wEyJ1cRIbLvrscAnJJLFh4rGuqy6o1EmMt06nzIb6ucFxqEFEPNOKsuJapZk5GLYhxPC6V3c7cv92xHRg5sj/sKVkVgJDwP1z5wCHsPHvMJjJ6uVqxf04ymhjqTlrH3PRpBYBSkOp80IYVxJl1AaqNFSEfadTUIEfis6yALmYrUiCwFqlmTEUQxjqfFot+/RGDsnLPtJ7K7f8jYsgu6DXH67JxpR9Stjva8GSBTR7IxbQdPSCVZFoug4byYr1VWD6EDwkXt7LlLuig1DPHOlpL9xguJ8tZyRJjUPoyIlBCJDMhiXEsixN6/ckVGtA9I0P4AfmdVoSM9J0PER09XK56+e5OzOyaRTNWHCBJhkecTUi5cY5HOJbwsJ96xqRm8NPmK+VuLEHkJiWjYv4UIYH6rn/t28G9Vul8yadA2E32cL4VEeWs1IkwWBoyIlJhaMQXSZmH2/sn9pcZOA8mVWSLp4dnj/q4YV7h5dGoGsUjEiBBxjjywPY6t+w5jcGQS7c2NePTIS7i49TU+G/fVaQMneR1CykWuqjpJtUgUJMhUTEftBkcmcGB73GfPLpvQw+2A7GigvI6uwSoVhUR5azEiTBYOLFYtMZXoUqlFcqWBbMMl+2Rqt+YGFdXq1xQq7ahKljb50is9Xa3YsKbFRAhc9Kk2XTHsE7Giu8FEWOuuMRHn9m/BPs+UMh1SyDltvuc/pngWDyxWrRKVMAXak+P19h48VjF/Db0fLhFiD7WTqblyxahFinTQCEFFtQPD41mFqEnPc6ZiltX5D21XOJ2QQgkSIRGkakIGhifMQros5j8Gl8VSB6GkUHq62jA6NYP6uiimz86hqaEOd266BD1drb5o30Y1wuDaBw4BSEUc5Dcjh7b89kudDnEZFJbisS6Y4lmaUIiUiErVZFTqh5pP8Dx7fMLsh2vwnZyM+7asM/UbEaREyWe/7Y+UNDXUmfHkgv0e9x48hsGRSVOUKgyOTDpTMQ2WEKnduB9Z6EjEQosHSVNqxMAMSB3fu/uH0NHcaKbjvv+6TsSiEV+Er6erFRvXpjxGkN7Gtv2DpvssFonAS79eLBopSzqkkM67+Xbpuc6ZTPEsflgjUiIqVZNRqVysDi3bIV/ZVk9XmzMdY++HZ/0/6cFESACYORhJKySdyYOP++Zv6ILWIOzCQULKhf5lNzXUmeM23tliZiIB2WnECDL1TPo3I8e7nebp27IOgyMTGBiecKY4pcak1CIkbOddqbr0OAl46cEakQVKJWpRgk4selv2fug8thSY6hOoC+0qKX8nPQ+DI5O+nHxHcyNuvrIdTzw/xoJUUhPo4zMWjfguOOQ3oYtV7XoowF/7IQT9vvXiru8TEWKbBM6HoAuLXOeBMI8NS7mNGEl5YY3IEmC+udiw28jXjmy37A4MT2DrvsPYtn/QRDHCipDVqxp8V43LYlHfSX50agZHTrhTMYRUCl1vpCVFIumhPp0SFBO+CGD8cABk1UMBMGkXTdDvWyKv+j79ugnPK1kaOFeU124RLuSxYaiEESOpHRgRWaBUsjsn15WJvR86ZWK3MtptjDbsgCG1ihy7+VKCAMxjRBjkO+4Bd9dL0O/bvk8/fzHUU4SJxJLap5D1mzUiC5BKOqbmmqQbNOBOTsS2AMl1MtYdNhpXKJuQSiN1TAe2x/FHf/WUKTB1oUWIPNcmAuCudLQRyF3zZbfq2u7EmkKm5tZiqywnAS9NKEQWGJX8oYY9Icr2DmyPGzGic+f2idgV+ZAct32f3KZrSAipBgPDE7jugUM5RYggIqRvyzo8dnTU11EDZH4b4ikyODJpvEXy/b7lvj0OQdK7uTt0gXyYgvRKUw0jRlJ9KEQWGK4fqlzZuHKxxV7ZyMko3tlibtMnxHja28De3oHtcVz3wKGc4etRZXtt3lc6x520pulKy6/rOYSEob4uGko82EgkT4vqQuuTBkcmAt1VZbF/5LarsW3/IBJJL+dCLEJF7rN/0/q3GOZipBbdUHOdpxgJWbxQiCwwXD9U3d6n75/PlY02JHNdbQ0Mj2NgeCKr0G7vwWMYnZpxOlD2dLXiZydPm5O5znGL14i0LUpURC8AHc2N6FBDxAgJQzEiJBaN+GpCXClCLS5ctSNSvG3Xl4jxmYgRKebWc5hcPHLb1QDcKRWdLi3kwoOtsqQWoBBZBJTjysY+kem5MUdOTJq2XP3akpax0ytyIrYFxPo1zea2q9VzXB4JAHB65lWMFiFC7LZKQvIhHTAiql11StNn57Im7PpeI23iJyLk6bs3+dKbUk9lH+/5LhxKnVKplflYZOlCIbJIKOeVjX5tuTLs6WrNOgnqEyuQMnSSWRo6QqKFieTQ9ZWj3q7OrxdrUkYRQophdi6ZJWLtNM/JqRlf6sX2zPGQ8b8Bsi8aRqdmskRIvt9sqS88chWkE1IJ2L67yCiFCVBQNb28togKu4hVIiGp+pE28/yunU+aq8N2FZ4em5rJmi7a0dyI1WnPBXFuzTdojJBawBXVsAtMBfktASi6Bb8ULfxslSXlgu27SwCXWJArGzFTKvbKxhX61bMzZBKoHX1JJD3EO7MjJdps6Zb1HQAy0RXZXwl9j07NmLkdgyOToXwbCCknYXxAmhrqTPeLvYgPDI9nRS90BKLYlMh8UypslSW1AoXIAsUWC3ZUQoSC3F8I9slI/1tf6YmQCDoJ6s6bnq4287zUnJpMTUgiPUFXBIdudcwlQmiARipBkAjRx9/02TmMpdMsdhGp/D4TSS+rjkN+V8VcOMw3pcJWWVIrUIgsULRYkJy0nBhLYXTmEiP6pCXbDIq+6Dkz8jp9W9YZy3jA36I7OjVTsLCgCFnaFNuWG5Z8Znr6N6eHNbqw0x5AdgqnkN9qKUwN2SpLagUKkQWMXUSqRYi+v9grm97N3cZoTEc9pDDVng46MDzuqw3Rr/PY0VEz9hwAlsUiOJfwjBhZFouEEhYSOYlEgNqtbiLlJky6ZL5s7GzJ6/RrC/9cvzW7JT5fCicIplTIYoPFqouA+Rao6noT/W854UUjQNLzT8UNmo0BpApSb1W1IEGRDtdikusqNxYBEh7bcUn5cdUmuYqmIwCOf/Im3235rNOfeXEc11ySLdjl/nw+ILVozU6IDYtVFwFhTzalaL3T9Sbyb92C+KHrMy2J7c2NOfPK0m7rqgXxbTMSwWWrV2bdlyvUnkivAhQhS5f6uijq66JliYbI/Bd97K9oqMOZgGF3Iky27R/Ege1xAKnf7ZETk+b5Lp+dXB0pYX67TKmQxUa02jtA3IggsMdfS+QhFo34QrRD999o6i8KHZndu7nbV7uhxYMeRtfT1ZozD55Ierh1fYeZuptK16TmzmQ91vNY40EKppCakIjrwLOIRVMP0tEOcQtub27EmbQ3iHR7ae5SQnvb/kHzerpYXH6L2uyPYoEQP4yI1Cj5TIvkvlLlie16E8E2R8uVB9dW88+OTJpcun6068qSLbqkEMJGQ8IknRNJzxiOyfEb72wxM5ZuXd9hUpa6MLW9uRGJpOcb9KijkZKS3N0/5BvoKJETQkgGCpEaJpdb6p5+t+nQfApUE8mM38eyWCpYJmkfAHjHQ4dxzSVtztCwCBQdWbEJStNQhJBiCVMvJBG6oBTh03dv8t3mb4NPtZ3ftWUd3vHQYSOaRaAAmanTz7w47pvHZMYipH10KEIIccPUTI3Tu7nb1H/ozpW78uSZiylWO3Ji0pw0tdHSuUTSpIPypYtyoSfohoiaZx5byIPJkiKfCJGRAyJC7GM04Xm+41nSlBvWtGSlOmPRiGkzd403uOaSNt/r6MiivR1CSAYKkRrHVYxaru1IDlu3J65f02z+He9szTo5b913OCtd1J5u0QVSgkOuSCXy0dRQh2V14Q+92u3rIuUkrP7sUMebpqmhzowLEHakBYQ+Rnf3D2HrvsPmmBYhr2unOnd+0+cbIo8NskTftn/QiHoAWTUjhJAMTM3UMKUwLSp0O0AmhC3eJD1drYh3tpqQdbyzxZcuEhJJLyv94iElYH528rTJ7cv/C23DLbeBFaktwh4ZenaRRtxONfr3JLUdy2IRDI6kJkoD7t9W0kvNhDmwPW5+L0HDJXVhqn78fNyOCVnMMCJSowSZFokIcF1Z7T14DHsC6jNyoa2eRUwAmdz2hjUt6N3cjfZ0mLunq82XLpKrxmePTyBphS/k5NvUeJ5zu00N4bUwRQgJQgSHHR0Zm5pBR3OjKT4FUsZhEgGMd7bgzk3dpkBV/7a08Lfb413pUnmOFiFAJt1jIo5sPyfEByMiNUrQHIierjYMjkxiYHjcaSjWV0RtiK4nEW8SPQdGWoXlZD84MuFLFw2OTKRvnzSvY8+kkQXBLkwttzsmQAO0pUJ9XdQZHRmdmkFHy3Lzt45+RCMRZ9RRR/vs++zjX3fLhJnfQrMxQvzQWXUBUs7R3UFDuYDUCVnPtYl3thoDs56uVtOyqx1exfVVfBo4qI6UG4l+iNgIsoO3ZzMJcswC/vlKe/qH8MTzY6Zg1U67bFjTQpFBSBo6qy5ycrX1ForL0l0LHI09XE8EhXQmAP4Qtlw5SkuwffImpBz0dLXhsaOj5u+gqJtLhGzbP+g7ZjVHTkymoivNjdiwJiV2ejd3+5xYCSGFQyFSAxQzO6J3c7cRIXaeupDX13bUiaRnriZdYuHor6bSufRMVEMm5wKpK9FHbrsa2/YP+lxaN6xpMdspd9cA0zBkT/9QVqFrvkm6gL/IVI5ZXVwq4mNgeALPHk8d//YAyErCmTNksUAhUgPoWS9h6z4KmTET9Ppb9x3G4MikKShNzYZpy0rHAKnoy7m5pC+/Dvg7G3q62sxrCvHOjOeCFijzJWj6LkXI4iff5F3XYDpbhEiqUI5HHe2TqJ22agf89VODI5MmhVOKlGgxFHPeIKQWoRCpAfLZudsnuULbeoNeXwRDvDPTnisuqkDqhJ9IejhyYjLrNXu6WvHS5CumKFCfsOW5r7+wyQgDXQBbihHutVvZREpBkNAEUqmWsMeQqyZJnivF0xJBsTtdgIzLaiLp+X53YaOR5aTQ8wYhtQqFSI0Qtu4jqK1Xnit/22FbacvVsy9sEzKJrgCZk/Xn0qkU28NjVIkQwG+fHUFqsYhGIuYqUrZx6/oOPHrkpYp0y5DKk0tAFEKphObPTp7Oum367JwRKBIZcVmw69/V0RNTOJdImtZ2l8lgNVIhpawXI6Ra0EekhsjlTyDY7YF70r4H4lUgEQgJ2177yUPYe/CYmQoqYWpxfBwYHgeQ8lvQ5mTvv67T52wpIkRuk6I9OTELcmKXAtZ19z5lrirHpmbwpadHcPLlswBS7pYuHxFaui9cPA8FecMUiwjZdscx6HqcJt7ZggPb4+jpajUiJMiCXf8mxeDPNfF6d/+QM3IIFO/vE5Yw5w1CahkKkRoijJ27PWNGz39xzZgZe3nGREo6mhvhIZMzT3kiTPpO5LFIxJxcXRel+rbRqZmssLeIkNGpGTOzRhwpe7pazcLQ1FCHsakZ50IR5mo431wbUj20c24pcQmci1qWO1MvuejpajOpwr4t6zC86y1ZowsE/ZvMV+zqKsYOO4dpPlRqDAQh5YKpmRqhWDv3fHliuU+LEW0qpltv5cow6OpNoh32v+2/b1nfYYr/RIzsPXjMNzI9bH4/kfTwo9GXs1xVWZRafuzvuBBK0b2kIxEJz8s6ZlJupeO+26R7Ze/BY1n7viwWxZ2bLjG/hVzpTamN0i2+Uojd0dyYlQoBUtFF1+/QHpInlKKzpVJjIAgpJxQiNUDYuo8g8uWJpSYEAE6l0yJASniMqmLTA9vjWHvPN30n8NWrGvCODRfh8aOjGJ2aMYuTfZLXIW7duptIer7q/nhnKw4PT4Ra4AaGJ9De3Fgz1u6lqn9YKMznrdoipJgCZX2VH8TGta0Ym5zBRa3LTcE14N53eR1pUc/lfioiRIsIaU2XFKedCnE5s+oheaXubJnveYOQWoGpmRogly20rvuw2aNCyXaeWN+va0JEkOhQc3tzo2lZ1FvqaG7EM/dsBpCpCcm1OO24vts36yPpeYhE4GuDDEr5BKELYqudjFlKIiSIYr+DIBFSb01htmfFuESInm8Ui0bw/Xs24cD2OHo3d/uKUDWSfkxFKNrwyG1XO/dH0psb1rQ4J+0ODE+Y34ErFWL/Dg9sj2elfUrV2VLseYOQWoMW7wsYVwpGX0FqC2u5X9AGT/p+OYE9/MxxX4uj6zWC0Fe/rvZJ18wZsjAolWFcUNpHjpcVDXU4kz6G9GPluLWPWZ060Y+3O8NE2IYVAfIbk9+VbQsflBqRx+e7nZDFSiHrd9kiIidOnMD73vc+rF27Fo2Njejq6sJHP/pRnDt3rlybXHLIlY9EGvq2rMOdmy4x97tEiJ6sK1eN+vm9m7sRi0Z8IkSuUuVkromlX0SubOvroqY9cvWqhqyrU+3EGqt2iIMUTClESEdzI45/8qas29ubG/GriT+go7nRiJBYJGJERUdzI9avaTZTcgEY4bynf8iIDCnIFmQoY3tzI/q2rEO8syX0+3B1zdipEPkNimGf3VGTb2IvIUudstWI/OIXv0AymcRDDz2ESy65BD/72c+wfft2/OEPf8CnP/3pcm12SaPrQwaGx9MuqBNm3HlPVxsA+PwTJHrR3tzoLHR9LF0bMjA8jnhnC6LpSbqAP7IhdRyzc0lTb3LtA4cA+K989b8TNRuLI8D8ilVzMTo1g+vSx4agO61ckTp53i3rOwCkBtrJcSpeNTqNd5dDgNtmZWGwO1Jchad63kxQvUauib2ELHXKFhF585vfjIcffhg33HADOjs78e/+3b/DX/7lX+KJJ54o1yaXJJIntq+2HrntanO12LdlncmJy8n7+Cdv8rXTjk3NGM8PvQDcur4jPXV3EkeOT5mwea70yq8m/pC1MJCFR6lFiI5SSKTtxCdvMseSy4pdjPiEwZEJc0wDGR8ciXoAGZffz307U7sR7wz2GglCi3KJcATNS9qwpiWwXkOnc+xICSGkwl0zp0+fRktL8GCo2dlZzM7Omr+np6crsVsLGrkadF1tyX+Au0DuwPa4bzaMLnSVtsOB4XFsXNvqC0+fOTuHM2fnAq+YT7581nclysmkJBoB7GzI6uZG7D14DKNTM1nOvQCyaioy0b1WX7RBZr70dLX6OmekSHvH9d1ZqZh8A+MkohimIyXXa0mXDTtbCAmmYl0zw8PD+PznP4/bb7898DG7du3CypUrzX8dHR2V2r2qsSfHlVEYR0bXVZvraiuowl7SNcK5RFKJkNRwryMnJnEukUQ0kjq5S82IPrUHGTaNTr6Sc//J0uBf1WeuecRwTNIrQCa1176q0dfSqsWxa0GPRuDrUOnd3G06xIBMhMX26tBGgBrtwxG2IyXXa9ktwLleh5ClSsERkfvuuw8f+9jHcj7myJEjWL9+vfn71KlTePOb34xbbrkF73//+wOft3PnTvT19Zm/p6enF70Ymc8EzUJ8BIJMk2xDqMztE75/63SNKyUTdEJldwwB4Ct+nj4756sFEfRxLNEPICOObWGw9+AxE2WRSODgyIQpxBaXX9fvaz4D44L8Rwp9LUZCCElRsBC58847sXXr1pyPWbNmjfn3qVOn8MY3vhFXX3019u3bl/N59fX1qK+vL3SXFjTzOSHm8hGQ+3OhJ/CGaakdHJnAsroozoUwGHOF2oOIIBWmz1dTMi+nz8jiLo4tV2FpfV0UryaSWWmVYhidmjEttCJCZL+jkUz9h8xF6ulqxeDIhHPbYdyDJaqnJ0MHiZH5Dozj8DlCiqesPiInT57EG9/4Rlx11VX4yle+glgsVtDzl5KPSLE+A/ly3bkspOW5+upTs6KhDqsazwsd1ehobsSFqxrx/349XbCLZj7nTfGvKNeCS8qPGIHZglN73+TzrXH5hwCZWgz9ONtmfcOaFudvQWpMlsWiGLr/xnm9x1K+FiELmZrwETl16hT+7b/9t+jo6MCnP/1p/P73v8dvfvMb/OY3vynXJhc0hfgM6LoSOz8tdSVhhm3JSVncIm3OnJ3DLes78rpfCqNTMzj1snuQnUZqBPS+TZ+dyzl1V6I7HjjwrlqEmaprf4fyd1NDHUanZpxRr/Vrms2/JZIBwFerobtkXJHADWtaTG1ILBLxpSr70k6ptgjZk/b+cA2MK2ZiLofPEVIcZeua+da3voUXX3wRL774Itrb23331bCZa9UI6nxx4aorEa8CCUXbhX1BSHtkUGeLy0k1V4TEdZ8dxZg+O2ciILp10/NyO3e2NzdieuZV3wTfQiMvJDdBqbf2EKkzIPUd6pk88p3q78lO28kxKx0vA8OZCJ19DItFu31cx6KpmhPX7yfoN6DnyciIA/07KmQODIfPEVI8ZRMi73nPe/Ce97ynXC+/qCj0JOaqK9HTbsOIEEnLSD5eyDdkTKNbKm30YuOpv2WhExM13bqZr67EXggpQkrPlRetQjQSyfpOC/GEsa8zpO4jkfR8tUi6YDXpuDixI4NBtU/FiADd0aKH0mkxH1ZAcPgcIfOD03erTLEnsd7N3WbsuBTHyYld2h3f8dBhXHNJ9tUjkLkajHe2mKvdXPNkgkRCvDM7mlJfF8X/t6Let3iJ42rS8/DCqVQNyVg6EqJdWcOw2AtPK42OWNkiRAtT+dwLnRUkx45Me9YCVo7ZsakZX9t4UGTQPpaL/f3o9I5rYu6GNcF+R7leSxO2aJyQpQ6FSJWZz0msp6sNgyOTWSJEe4EEtQbrq0HAXwToSnkYnweVHtGCRS9ms3PJrCvojuZGvDT5irldtl3MKdolQljEWjza6l+Oh6aGOpx9NWlEiNxfXxd1Ri+aGurw+gubTBeWRkSIFsn6+IulxwY0NdQ5B8oJrsLrYn8/+nV6N3cbESJ+JIUQVAyu94MQEgyFSJVxncSK6YRJeF6Ws2S80/+3qzVYnFXlRCwLTlD9xUUty3Fgexxr7vmmuU2ukF1pnfbmRt+gOyAleh498lLez6aQGhCKkBRB0aJ8Qs2u4bA/99df2GQiWeP/4h9cKY//4UsvO19bR0RsTxDpMokgO9VmRzZcNRulEAGF1GcRQkpPxZxVSXjyuT7GohFfW6IwMDxhTvLisArA/FvPkpET7SO3XW3cKWORiE+E2Cf++rqomSOjkbkh5zdle8CMTc34REiqhmUcJ18+m/dzYA1I4WgRorudwgq1poY6XHHRKvN3T1ermTUkxcWzc0nTOdXUUJcSGdGIM7Wm63/u3HQJ4p2ZlIcWAGI+JpN17WMs3tlSFnEQ1pmYEFI+GBGpQfKZnMl9YhEd72w1EQ37NSR6okPPvZu7TdQFyMwAEfv20akZxDtbfIPEgFSIXebI2F0UYWoGZKiZHb4vtOZASO1jdiqA+KNJQfU9rijJ9Nk585lKquToiSlz/2g6hbK6uRGrmxsxODKZ+l5VCqSpoQ4/ue9NuO6BQ75i5GePT+CR26522qjLbbes70BPV1tFjMFYZEpIbUAhUqPkcmrcY5087dDywPB41sA7O/SsW4Bd9u0n05EMibgU2kXhagnWi55u8SzWBv6FU+6hiO3NjWhPL5JLFR1NCioCXpEj9SXRMSAzy+XKi1eZ9IwMhHv+Vy9npeOmz86ZlEsukSnRkT39QybFIsIgrKfOfGCRKSG1AYVIDWMX0bnmxgS1Lm7dd9gMtHPd326ZkunWRSCTbpHCPdu5Mhc9Xa15h93ZfhNhkKt8aQUN8hMZm5rBres78PNT0zhTxvROrRTIuua2uHD5uWj055jwPN/fqaGHfj+QwZGJwFZvOWY7WpYbISLHowgAAL4onxzfA8PjFanZYJEpIbUBa0RqmHxOjUGhZcnpy/wN11XfWDraoetHpINBGJ2aMds8sD3uuy8XA8MTJiSfi0J97d5/XSeaGuqyrlSldkE7vu7uHzIipFxOrOUSIfk+N5swIqSpoQ69m7t9NUWajuZGTJ+dM/dLAap2U9UdVhLxsj9Z/R2konMTvuNs78FjWdEP+9iUaAtrNghZGjAiUiXydcYMDI+bE3KQSVO+0PKjR17yFYXadSaJpOeLukgNhwgf3f772NHRUAue5o43XgLA7UtSDHv6h5yLfwTALes7sLt/yJkKWGgh9rB+Kpp8ZnDTZ+fw1efHcFHLcuf9MpDuwPa4qe2Qz9KOuEjkTCIssUgEO67vNqk9+zuId7bmTDUKrNkgZGlCIVIlXDbtALIK+RJJzxea1s/JZYcuNSK2WZM+yetZG3qxkdkfMoMmSEjkSk2kCh3HcTKgPqCYtIY83l50PfhD/IUYspWSYotuw6Lfg53WCvPexgJmvej7u3Y+aVrBD2yPO1Nyv5r4g88DJOF5ePzoqO91gMx3rI9ZV6pRYM0GIUsTCpEqkaszJt7ZYuZpBEVC7OhGru0Enfy/+vwYxtQVrCwu2tTKtbDK4/P5UujuC7seYT5LStCiG2Q3n+s5pSSfCMklHMPgE1/qZezPN9/fGvs+bbkukRHAX3x88uWzOPnyWSMaRKx0NDfi1MtnkfA8xCIRDO96izlOB4bHASBn7QdrNghZmrBGpIpIPcfu/iF07XzSCItHbrvad+LV3goSCQnKr9vYdSbb9g+a2+XKVcLpG9a0+GoIXIWeQeIESF0Ba58IoVJ+ILJQ2vUi5aSpoS50DUouEZJvf121HbFIBH1b1pnPd/WqBlProWs77L/18+3vRuqABkcmzffc0dxoHE/t19EuvaNTM0aEJLxMJE/XLOWr/diTox6kmIm4hJDah0KkyvRu7jYnbj2+HMikaXq62nKakgVhmzXJVe22/YMmDC4RldGpGTx46EVfRMFeNmXhci27EoYPSsUIYQoxCynWdBXQjlomajZ9W9ZljasvBokmFBrlcG06XzTFjvQsi0VN9ELE329OzxpR+f7rOrP200bqgbTI2XF9dkGrzIcB/KLmc9/OHF/xztRz2psbMbzrLU6hoU3JtAjXjwlj5kcIWVxQiFSZvQeP+a4idcRCC47ezd0FeSu4Cv8ObI8bMXLkxGR2jj69sNkLUSwSMQufq7ajqaHOOGPqBdV1FZ6y+nbvc3tzo3HqdD3XRtw6g6IJQbfv6R8quGPHRVA0QRB7e7lP9me+m25qqDPCUoSq/n5Gp2YwMDxuvsdcESnd2QIgnUbJTm89eOhFczz95L43me3J7BhJKd66vgOAX2jIvmxc6z+u5DFayLkEiutYJoQsHiKeV4pTcnmYnp7GypUrcfr0aTQ1NVV7d0qOfYKVXLuIEtu0bHf/kBEj+U7KubpyZDu6O8ZefMQ5VRex6n/bzqp2Z4XcX0hRqtRQ9HS14mcnT4dK6UgBZ7kLRfPR3tyIi1qW+z5H2Tf5DHLto11Mm6uuQ0+v1d+d6/sMel379j7lvgtkIlzRSMZ5d1ksiqH7b8w6fqSmCchuyXV1gIWh0OOdEFJbFLJ+MyJSJYIiFq40TTHzMO5SLqz24w5sj5sTfASZsL++oh9Lh+N3XJ85+Sc8DyskUuGlFl+5kpar8XhnC9qbG/G7M7PyMMQiEcQ7W/JGORJJD/V10dAiBMhEWML4lpQKV1rn92dmsz7H2blkqrsFKUEwmhZmQCZatCyWeawgUQc7clRfF1VRhgm0NzealEi8s8UcGzrCYRfpuubFtKc7o7Q5mYhHeX0gFT257oFD5vVle1KUHJRuKVSEyGtVwl2VEFJ92DVTJVytinaaRk7m8/FWcLUJSwErkFlwJET+8DPHjQiQq+6+LevwpadHMH12Dn+yeqVvoq9EReTKOZoe6e57r56X5coZxOxcsuAOFxn0lut5y2IRnHONpS0CVwxRtm23EHvWvnnIRCUkQqKjRiJAU8dC9jZ0sWYEuY+NoGiU1HzIdgaGx813ZkfHRHTId2477gYdi/OdFcOJuIQsHShEQpDPfEwGyxWC/Xi7dRfI5N31oiHbyuetYO+z9taQf9vpFdmnJ54fw+jUDH568rSvSLGnqxUb1mQKDh87Omqe/6Hr1znbZ+2WYCCVohjL0/5bakolQvIxODKBDWtacqZh5LOUllf9uITVNmsj76KnqxWJpIdb1nc4fTcePzoaOlUlEQ35fuOdrb7vcmB4As+qVmxx3LXFjxyL+fxC8hE0tkBvixCyeKAQCYGOKiSSnlngbS8PEQrynLDCJWiyroS5e7ranL4hhUZCtBiRRUe6EOS+u7asw11b1pk6gKCrWrmKlgXVNhFraqjD+6/rdN5eaC1HvLPFDFurRZbVRXFuLollsYhZtO1ZLTaSWtE+HTrqIHQ0NxrXWI12KwWArfsOA4CZbuvy/wAytR+PHnkJANLTm1sQjaT2XVIxUqvz0uQrGEu35S6LRfGT+97kO84FO7JXbDSD7qqELD0oREKgT4RyYtdpC5coyeWaapuQudI0WowUU+znOnmLRXosEjGhdXs/hAPb42aCaq6r2nhnK46emHJOYAWyi1gLFRM9Xa1Iel5VRUguQQEA6y/OONECmXqZfKZiOh2ivw8tOm5Z34HBEX+USUTmwPC4ER4S1RAB6SqeBTKpoY6W5eYY6+lqSwuSTMpNjlG7EDafqJhvNIPuqoQsPdg1UwDGyyC9uMoC4rqKCzohhxUTpeoasPfZ1ZFT7Pb1IlMs83Ub1ZTTxj3ffvZtWZc1CydMJ484kEoqDQiezSPfhUbSa1oAu7ZrR0Z0BEb2X29bbyvsMRx0P9tvCVl6FLJ+U4gUiEQJZEEPs1AXKyZ0RGLo/huL3meZHyKL3tZ9hwMjLLrdUupV7L91kaNciedq03WNqW9qqMMfziUW1BWuLXT033bEQ+7LF00BskWA3c4NpETN03dvwrUPHMqa5aJfQ9ftBL2+bekPwHeM6jbeQkRFOWqpCCELk0LWb6ZmCsDOfccikZypi/kU7ZWqa2Db/sGsTpyerjZjuS37Kdt0XSFLK+jgyGT6v+yiVA/BYsQlQpoaz0u5tEbcXSjlZkVDndPCPogIsrtyZueSPs+TjuZGrG5uxODIpHlsLhHS1FCHFQ11vs9Y89LkK+bfo1MzRpgKHlIC4s5Nl/iiKPb3oOt8AJgImaQW9TEKpNp0tX+IJleKJN+sGGklp1AhhGjoIxISl126LPDnEkls3XfYaUutBUsu349c28rlG5JrNodcUfd0tfpstwFdAzDu26bu0hFfCOnuAFJX+gPDE8YHQ7wuZPFzLahCT1ermYXipYs5PS+8pXvQ41Y01OW1bLefe8aq39C4XspTjxOfEJnBIiLk5ivbTcdTPuRzuLj1NT7vD23lL8XAfVvW+USv/ozPJZK+GhL5HmQfbbEs/hzaFVULXjnuRnbdFHjc9W7uLkow0L6dEOKCQiQErnoPWTikVkQiDLYttX5MGDHh2pYU8LmeH3Ry1yJEez7YYmRwZNI3v8YeuCfdE+J3YV/pS1fsXaaw1j39FkgVPkpK4OTLZ83cklx1HbKgBj2uo7kRZ87OOaMqWmAE2caLIJB/y0JuL4mxSGbOinyfo1MzaVOxFjx99yZTR5JvIjIA8zlIO3TflnXm39rIS747nQbUhmVApqBUi5DhXW/JEst7Dx7LirTJ937npkuy9jHIoKxYaN9OCHHB1EwIdCV/kFCQwr/d/UNZ9tt2USsA33Pt9uBcxYHPvDhunq//L9uVllxbhAg6tH7XlnX43LePBaaOtu47jLGpmSyrd5tMm3F2NCSClEjRqYN4Z6ZQMl/qQtpFgwo4g4pB5bPXNRpNjefh7KtJX3pjdGrGDI2LRiI+gzEp6BQzN90Jo9/Dres7AjuicqFbcOX73rrvcNa0ZEmhAJk6D1e3kogQmVkk3TMSWQkqTAXgE1D6GC11t0qpDM8IIYsHFqsWSL6CPCng1J01dpuvFh9BoiOooFQe176qEbduyJhZ2XNq4p0teOS2q3O+F3uBd3X9AHA6gGrs2gMRLPoKPeF5ZlGUfevc+U1nHYLG5SEiRbO2OLHrPnq6WvHSxCsYezklVmwDN9nH45+8yRdB2rCmBUdOTPrEpH4vG9Y2m+8GgK+9Wj4HVxeNRrYjKQ67PkcXq4qgzNdZI8JNF7na+yi3yTGo91d/9+Wu1yhVITYhpDZhsWoZyVeQ17u523eS3bAmdbVtR0JM2ka5UcrjdNdCEGMvz/gWIy1CAOStVXD5ntjuq31b1uGxtENnUPdHU7rYsm/LOl/LpyyeumPn+3dvMovc3oPHskSIS+jI4qmRTh5Ne3OjeX15H3a7qvytBYkHGCdTeUzS8/DIbVebBV2EWGaoXJsRQrrbRH9ugyMTOZ1jtXmYS4S846HDOPlyZp/2Hjxmjr3rHjjkfF/TZ+fSn2u22VgqxZY6Jlwiw56AW05o304I0VCIpClV66F9ktVX1lpkyAIS72z1bVt3MEg3hDauChIPrgm6ufZRhJBtopYJ/acElCzYQSkUKdR0OX/as3Oue+AQbr6y3ZeK0p4Xkg4Jeh+29wWQ6cB5+5Xtge9Xv6br3zI/Jd4pZnWTRkzK/rkiV/ZQtj1KhNiW9i4xp48HcTfVzrxjUzO4qGW5iWAA8Nm/y+3akl0LmoHhcQwMjzuLS13jAioB7dsJITYsVk1Tiop+V7eLDu9L0aBunezd3G22vW3/oE/ESDeEXVCqux8EHYrPV2AoBagDwxPYtn8QALJeLxqJ4JkXx9G3ZZ0RJTYyfVcWxmWqM0VETbyzBTuu7zbW7k88P2Zu14u8dvLs6WrNKhZtT3eoPHjoRXNbLD1I76KW5b7PXzp8hGQ6VSVdLhnH2tS2xGrdFhixdM2IFmy64NLuiLpLvQcpjNWeHVLsCmS6eDKdMG2pYyR9nB3YHve5+IrtvggaOQ5ESMjkZvlcejd3mzbtSnSp5Ore2nvwmLnfTgOVuiCWELLwYEQkjcsSvZCK/nwzMvSVv9SO6MfpAtcD2+O++gApSpQFRranjdUAZBWcBl1pyoIp27j2gUO4qGW5Lx2kCyTtugpBrtgBf7pDX/lHI9nRDylqlX9LvULmKj4lquzaEgBmH0UsiLiQ+S76c5bnD45MGsEkn/veg8cQ72zBwPCEEWAiuLQYlNcPSh9s7GzxRWl00afUZbi+U/0edfeK3oZ+zpp7vgkg2w5ekOiTK91R7DFdCK7ZRrJfsj3atxNCXLBY1aJYN9RcqR27kNS2WdepElkYdfoGgK/4VRY+qeHQQqHQokNd2GgTNDDNxi7MBdwFrkELtF6sRJC1NzfiVjXszbYt1+/dLooV7OJU1/ZcqTD7e3HZqOvPRxfP2rUi8m9XLQngtlK3ERECACc+eVPW/fnGCZRqXEA+5jvWgBCyeKDF+zwpZUV/PpEhV+Fina63vX5Ns3mMLp4E4Fu0XXNDwp74g1pj7XoGu2BTsEWAqzU0Folgx/XdORdDvR96YJtdc7EsFsG5RPYh6y/UzRZwAAIFQy4xYH8+4jjqsmUH/B008jr6O9ViJ5cIEmyhaEdEws53qVSXSqVEDyGktilk/WaNiIWron8+6HoMV+0IkOkMsbctj5E23APb42hP1z/s6R/ypXIk1x7vbCko3253nwDImhyrazlk/4GUWBFJoNNNvZu7Tb2C3AfAV3vhCs/Le/v1y2d9n49ESNqbG50iBABeu6pBvacJU+shESgA+MHxSWfUYuj+GwPrYPTnCqREiKRy5PlS4/DgoRezREj2dzputq9vBzLFp4JO5Zz45E1ZdT170oXMQYt9vLPFdCiV8pjOhW3IRhFCCMkHhYiiEGv1sIi5mKtAL7XItJntyLa1y6W0eArfv3sTYpGIiULoq+Pezd145Larsxa0XO9XUiRAxsXUfqbuGpHtSPGlyA0pwN178Jhvvg0A0zGka1pkMdX7oo3T5PVkob11fQfG0oWaGimYtetYBkcmzGIor+cSEfKe5HNzfd9S+CnYNR168Y2pzhfX8SSzelzHGZBdv6MjILqAddv+QcSiEWd7cyYK1+bzqynVMZ2LSooeQsjigMWqafIVm+q/CyFMgZ7uXnANKNOPz1WUaL92mPerozV6e5qO5sassL/bXyOTHnK5yQLAjuszhbnb9g86zcN0oejgyCSe/9XLvlZaIFP/If8JsUgEG9NFqLYxmBSGBkURgoomXTUlQErsSC2EFllHTkwGHk86nZbrOJNIml2YKgWsiaSXtxhV7iv1MR0EW3MJIcVAIZKmlBX9unA1jH/Dnv7smgG9H64r7Pme6BNJz7TE6kXLLgqVv7ftH8zMPUm6HWNlX1Y01GVFcgCp3RjHxrWZlMvh4ZTxl26t1eIhAmS10g4MTzht5+VvXXuj5wLZHSuuz8y+TX/mA8PjWakz8fXQHUFiiuY6nsTgTv5vb1e+60f/ItgV146CAW7L9D397kLRcnSplEvIE0IWPxQiafI5phZCmFZGe9tBbpM6ElLKE/1dW9bh2eOpBV2u0mUxlf+3q78lgnFgezww3SSFmn+yemXOdlYgVZwrLbcRwNfWe80nD+Lky2fNftgdQ8l0S64WIWJvLtb4Y1MZ51ktlqQmRvw18n1mz7w4nlXLI6mz3f1DpjNI9l9HgVzutqU8zvTzXCK2HNsKgq25hJBioRApA4X6N4SJdASd6CU64TrR52vf1W6t+jVsJ9FEMpVukPuD9kVeTw8B1K8jhZwDw+NZ7asidnSHiJ5QK+LOnjwLZIpr9x485ntPgqRQUv8WIZSKzuRrb77mkjazXR050vtg1+tUevGtBcv0SooeQsjigkKkTOQKmWtcw+30c8XePehEL9EJOxUSdhqs3pau9bD3RV5zT/9QQYuO6/2LYyzgjy4I2ntEJtTqokfb90Ss4e36FqkjGRieMDNvAFiCIvdMHh3pkb8lgiT7GUGqM0gLgEotvqzLIIQsdNg1U0Yq0cooHTi6E0IvTs+8OJ7VoSJs2z+Idzx0OKvrQ7pfNMXYgrvev67ZADJCRZDFvaer1bwvbW8vLb5IP0a3+EqUY3d/ykL++3dvMqLDJULCelzojpp19z7lEyGxSATHP3lTVWzKC7FMD2PBTggh1YARkTISJmRupxLsNI74QLgWTJ16kQXYFX0QG/fv373JPFe3h9r7qaML+dJKLvS4evv9DwyPI97Z4qulkC6UjuZG31C8eGerT8gAGbt5bQgntSR+4682X02I8LlvH/O52oZF12HYJm5BdurlppC6jELrlgghpFJQiJSJQkLmudI4Qc+zFxAZcGZHX2Si7NjUDK574BCevnuTT4RILYe4t27dd9jcZ+8PgLypGcC/6LneR9Cid8v6DrPfrjoSETHRSAQb1vhnvLis5HWqRl5DPEUKFQp2i25HcyOevnuTeV+DIxO+riL9vLCTmwulkBRZoXVLhBBSKShEykAxHS5BnQ/FLCDaSEoe9/jRUYxOzfiGp+mFXGolRNBo3w3bBKyUnxGQiSzo9xhNm6G1Nzfi+2rBz0RCWn2fjS4L1ZbwrvcqhmrxzkyBbtCcIImqiPGbbgGWSIj2RQmyX68FwtYtEUJIJaEQKQOFhMxzpTHksXepK3zXAuIysbIjEr2bu33D0w5sj2PrvsO+x+h93N0/5DNOK+TKWbfsuqIquvjTjphIdCHXgq+LaW2kxkVm5chr2F0vcptEWFzeIvZsH+kGsiM8ekqu7KtLLOYajFjOyIkmSPASQki1oBApA4WEzMOmMYIWENeCp90/Bbtgde0934SHwgbkhUW/f73Ptousve14ZwsGRybxq4k/ZH0W8jrSzqvfO5CdehERIqkpQQsaAEaE2HUx1z5wCGNpL5VbVcpItitiS0SlrnkJijbUQp1GLbT6EkKIpiJCZHZ2Fhs3bsSPf/xj/PCHP8Tll19eic0uGvb0D+HIiUnnAiJ1E1qY2EPWdCThwPa4ESER5HYSdU2HheM5Qdj7Ilbu8c6WwIhOT1ebz77cFmA69SGFqHZNiJifbVjTkjc6oz1PRIxIQSsApwgJig7lizZUu06Drb6EkFqkIkLkwx/+MC688EL8+Mc/rsTmFhRhFko9i0VfeQ+OTBgPEiD3QqOfKyLEA0wBq2t/8k2HzUXQvmTqLNqcj3O9jkuAaYM0+ezs1IuuIwGQNyWhhUwsEsGO67sLEg1hog3VqtOgBTshpFYpuxB56qmn8K1vfQtf/epX8dRTT5V7cwuOoDSGjhaEqZcIWmgeOzKKsZdnEO9szXqMzEmxF0x5bREeR05MGhGhZ6QE1TXkW/RcHTlBC3uQsLIFnP0auutGnptLJPRu7vZFQrTvyHz3tdDISTmgBTshpFYpqxD57W9/i+3bt+PrX/86li9fnvfxs7OzmJ2dNX9PT0+Xc/dqiqCFUi8gueolghaa79+zydf5oR+j20+B1KKkoy+yTZlDo2stctU1hFn0jp6YyrkQ5xMzuQa7SXGuCKUwImHb/kETCZHUjjwnnyldodGGatRp0IKdEFKrlE2IeJ6H97znPbj99tuxfv16nDhxIu9zdu3ahY997GPl2qWaJddCqReQXPUS+Raa1EyatrxXxCJsXNGXjuZGbFjTkjdFkW9fwizEucTMwPA4BobH0bs5/3TjMCLBrqHRaSS7GLbQfdWfrezfYq3TqIWuIELIwqNgIXLfffflFQtHjhzBwMAApqensXPnztCvvXPnTvT19Zm/p6en0dHRUeguLigKuZqez5W0awFwLRzyuG37B7OiLwPDE/jid4bnVdcQdiHOtWD1dLUZC/N83Sf5RIL2CLGH1sl+idlbMfuar9A16LteiIt6LXQFEUIWHgULkTvvvBNbt27N+Zg1a9bg4x//OAYHB1FfX++7b/369XjnO9+JL3/5y1nPq6+vz3r8Yifs1XQ5rqRzLRy2odmB7XGsu/epedU1lKpgspDuk2IjRYKIkGL3VbPYLdmr3RVECFmYRDzPK0uV2ksvveSr8Th16hTe9KY34X//7/+NjRs3or29Pe9rTE9PY+XKlTh9+jSamprKsZsLgqCTeSlO8vm6W/T8Gf13MduUq3zxE7Hfi9we9mpf9nU++5RvX6sZkQj6bmp9US/n90IIWRgUsn6XTYjYnDhxAmvXri3IR2SpC5FSL9xB2AuHiA5ZQMQ1VMy9gOxOlUIW51IKKx2lGbr/xiLefX6qKUoW6qJeie+FEFK7FLJ+Ryu0TwuGWhqXLuF5lwjRt893Eezd3G0WOrFI11fhUsg5NjXjc3zd3T+ErfsOm3oS7Zoq++n6vHo3Z4+qL0aEuGpmyoF8D/br6++hXOjvZqFYslfqeyGELA4qZvG+Zs0aVCj4Mi9qKTcfJudeiqt1e+HQs1zs9mHx5+jbsg59W9bhsaOjGJuayZr/ku/z0u+tGGOvSnafVLP2YaFZsi/mriBCSHngrBmLWiu4y7dgz1c4BS0csuDZ7cP6MXYqR7uehvm87HbkYkWI/TnZn0UpmK9wKoaFtqjTvZUQUgwUIg6qsejk25+gBXs+wqnYhcPenzAD34K2X8zVfrVcQosVTsWwEBd1urcSQoqBQiSASi46Nna6xV6wt+47bFpKZV+BwoVTsQtHkIAo5POaz9V+tVxCK5kmWYiLOt1bCSHFQCESQDVz8zrdAmQPdBscmXTOSilUOBWzcAQJiMGRidCf10K82q90moSLOiFkqUAh4qDauXl7UQ6aMKsfW2rh5CqC1R4jckXeu7k70CJd759moV3tL0ThRAghCwUKEYtyLjqFdLj0bu429uNB6ZZKu66mXEhThanxzlazbREhMqcm3+e10K72F5pwIoSQhQSFiEU5F51CO1weue3qQFt1HQkph3ByvUYs6vcYAZbGIr3QhBMhhCwklpQQma/nxnwXnUI7XOY7pVbuL8X+BkVl5vt51YKVOiGEkOqxpIRILZiVhe1wKcWU2lJcrZe7e6gWvhNCCCHVY0kJkfl4bpR6P3It7rVUHFnu7qFa+U4IIYRUhyUlRIDaMCvLt7jXSt1FpbqHauE7IYQQUh2WnBABqmtWFmZxr4XiyFJFZcLWgFTzOyGEEFI9luT03WpNBw1a3O1JtLVArqhM35Z1oaMyYSfXcmIrIYQsTZZcRKSaZmW1knIJQ6miMmFqQKptIEcIIaR6LCkhUu0i0FpIuVSDXDUg1f5OCCGEVJclJUQWUkRisRFUA8LvhBBCljYRz/Nq9kw/PT2NlStX4vTp02hqaqr27pB5IJEPqQFhVwwhhCxeClm/l1REhFQH1oAQQggJgkKElBXWgBBCCMkFhQgpK6wBIYQQkgvWiBBCCCGkpBSyfi9JQzNCCCGE1AYUIoQQQgipGhQihBBCCKkaFCKEEEIIqRoUIoQQQgipGhQihBBCCKkaFCKEEEIIqRoUIoQQQgipGhQihBBCCKkaFCKEEEIIqRoUIoQQQgipGktaiOzpH8Leg8ec9+09eAx70tNhCSGEEFIelrQQiUUj2O0QIzK6PhaNVGnPCCGEkKVBXbV3oJrIKPrd6chH7+ZuI0Jco+sJIYQQUlqWtBAB/GLkwUMv4lwiSRFCCCGEVIglnZoRejd3Y1ksinOJJJbFohQhhBBCSIWgEEGqJkREyLlEMrCAlRBCCCGlZcmnZuyaEPkbACMjhBBCSJlZ0kLEVZjqKmAlhBBCSHlY0kIkkfSchanydyLpVWO3CCGEkCVDxPO8ml1tp6ensXLlSpw+fRpNTU3V3h1CCCGEhKCQ9ZvFqoQQQgipGhQihBBCCKkaFCKEEEIIqRoUIoQQQgipGmUXIt/85jexceNGNDY2oq2tDTfffHO5N0kIIYSQBUJZ23e/+tWvYvv27fjEJz6BTZs2wfM8/PSnPy3nJgkhhBCygCibEJmbm8OOHTvwqU99Cu973/vM7X/0R39Urk0SQgghZIFRttTM888/j5MnTyIajeKKK67Aa1/7Wtx44434+c9/Xq5NEkIIIWSBUTYhMjIyAgC477778Fd/9Vf4x3/8RzQ3N+MNb3gDJicnnc+ZnZ3F9PS07z9CCCGELF4KFiL33XcfIpFIzv+OHj2KZDIJALj33nvx9re/HVdddRUefvhhRCIRPP74487X3rVrF1auXGn+6+jomN+7I4QQQkhNU3CNyJ133omtW7fmfMyaNWtw5swZAMDrX/96c3t9fT06Ozvx0ksvOZ+3c+dO9PX1mb+np6cpRgghhJBFTMFCpK2tDW1tbXkfd9VVV6G+vh6//OUvce211wIAXn31VZw4cQIXX3yx8zn19fWor68vdJcIIYQQskApW9dMU1MTbr/9dnz0ox9FR0cHLr74YnzqU58CANxyyy3l2iwhhBBCFhBl9RH51Kc+hbq6Orz73e/GzMwMNm7ciEOHDqG5ubmcmyWEEELIAiHieZ5X7Z0IopAxwoQQQgipDQpZvzlrhhBCCCFVg0KEEEIIIVWDQoQQQgghVYNChBBCCCFVg0KEEEIIIVWDQoQQQgghVYNChBBCCCFVg0KEEEIIIVWDQoQQQgghVaOsFu/zRUxfp6enq7wnhBBCCAmLrNthzNtrWoicOXMGANDR0VHlPSGEEEJIoZw5cwYrV67M+ZianjWTTCZx6tQprFixAmfOnEFHRwdGR0cX7dyZ6elpvsdFAN/j4mApvEdgabxPvsfK43kezpw5gwsvvBDRaO4qkJqOiESjUbS3twMAIpEIAKCpqakmPuRywve4OOB7XBwshfcILI33yfdYWfJFQgQWqxJCCCGkalCIEEIIIaRqLBghUl9fj49+9KOor6+v9q6UDb7HxQHf4+JgKbxHYGm8T77H2qami1UJIYQQsrhZMBERQgghhCw+KEQIIYQQUjUoRAghhBBSNShECCGEEFI1FqQQGRoawlvf+la0tbWhqakJ11xzDf75n/+52rtVcr75zW9i48aNaGxsRFtbG26++eZq71JZmJ2dxeWXX45IJIIf/ehH1d6dknHixAm8733vw9q1a9HY2Iiuri589KMfxblz56q9a/PmC1/4AtauXYuGhgZcddVVePrpp6u9SyVj165d2LBhA1asWIHzzz8ff/7nf45f/vKX1d6tsrJr1y5EIhF86EMfqvaulJSTJ0/iXe96F1pbW7F8+XJcfvnleO6556q9WyVjbm4Of/VXf2XOMZ2dnfjrv/5rJJPJau9aQSxIIXLTTTdhbm4Ohw4dwnPPPYfLL78cf/Znf4bf/OY31d61kvHVr34V7373u/He974XP/7xj/HMM89g27Zt1d6tsvDhD38YF154YbV3o+T84he/QDKZxEMPPYSf//zn2LNnD/72b/8WH/nIR6q9a/Pi0UcfxYc+9CHce++9+OEPf4jrrrsON954I1566aVq71pJ+O53v4s77rgDg4OD6O/vx9zcHG644Qb84Q9/qPaulYUjR45g3759+NM//dNq70pJmZqawjXXXIPzzjsPTz31FF544QV85jOfwapVq6q9ayXjgQcewN/+7d/iwQcfxP/7f/8Pf/M3f4NPfepT+PznP1/tXSsMb4Hx+9//3gPgfe973zO3TU9PewC8b3/721Xcs9Lx6quveqtXr/a+9KUvVXtXys6TTz7pXXrppd7Pf/5zD4D3wx/+sNq7VFb+5m/+xlu7dm21d2Ne/Ot//a+922+/3XfbpZde6t1zzz1V2qPy8rvf/c4D4H33u9+t9q6UnDNnznjd3d1ef3+/94Y3vMHbsWNHtXepZNx9993etddeW+3dKCs33XST95/+03/y3XbzzTd773rXu6q0R8Wx4CIira2teN3rXoe/+7u/wx/+8AfMzc3hoYcewgUXXICrrrqq2rtXEp5//nmcPHkS0WgUV1xxBV772tfixhtvxM9//vNq71pJ+e1vf4vt27fjf/7P/4nly5dXe3cqwunTp9HS0lLt3Siac+fO4bnnnsMNN9zgu/2GG27AwMBAlfaqvJw+fRoAFvT3FsQdd9yBm266Cddff321d6XkfOMb38D69etxyy234Pzzz8cVV1yB/fv3V3u3Ssq1116LgwcPYmhoCADw4x//GN///vfxlre8pcp7Vhg1PfTORSQSQX9/P9761rdixYoViEajuOCCC/B//+//XTQht5GREQDAfffdh927d2PNmjX4zGc+gze84Q0YGhpaFCdEz/Pwnve8B7fffjvWr1+PEydOVHuXys7w8DA+//nP4zOf+Uy1d6VoxsfHkUgkcMEFF/huv+CCCxZValTwPA99fX249tprcdlll1V7d0rKI488gueffx5Hjhyp9q6UhZGREXzxi19EX18fPvKRj+AHP/gBent7UV9fj//wH/5DtXevJNx99904ffo0Lr30UsRiMSQSCdx///349//+31d71wqiZiIi9913HyKRSM7/jh49Cs/z8IEPfADnn38+nn76afzgBz/AW9/6VvzZn/0Zfv3rX1f7beQk7HuUQqN7770Xb3/723HVVVfh4YcfRiQSweOPP17ld5GbsO/x85//PKanp7Fz585q73LBhH2PmlOnTuHNb34zbrnlFrz//e+v0p6XDpmGLXiel3XbYuDOO+/ET37yE/z93/99tXelpIyOjmLHjh34yle+goaGhmrvTllIJpO48sor8YlPfAJXXHEF/uIv/gLbt2/HF7/4xWrvWsl49NFH8ZWvfAUHDhzA888/jy9/+cv49Kc/jS9/+cvV3rWCqBmL9/HxcYyPj+d8zJo1a/DMM8/ghhtuwNTUlG/UcXd3N973vvfhnnvuKfeuFk3Y93j48GFs2rQJTz/9NK699lpz38aNG3H99dfj/vvvL/euFk3Y97h161b8wz/8g2/xSiQSiMVieOc731nTP6Sw71FO8KdOncIb3/hGbNy4Ef/jf/wPRKM1o/8L5ty5c1i+fDkef/xxvO1tbzO379ixAz/60Y/w3e9+t4p7V1o++MEP4utf/zq+973vYe3atdXenZLy9a9/HW9729sQi8XMbYlEApFIBNFoFLOzs777FiIXX3wxtmzZgi996Uvmti9+8Yv4+Mc/jpMnT1Zxz0pHR0cH7rnnHtxxxx3mto9//OP4yle+gl/84hdV3LPCqJnUTFtbG9ra2vI+7pVXXgGArJN5NBqt+ZalsO/xqquuQn19PX75y18aIfLqq6/ixIkTuPjii8u9m/Mi7Hvcu3cvPv7xj5u/T506hTe96U149NFHsXHjxnLu4rwJ+x6BVPvgG9/4RhPVWsgiBACWLVuGq666Cv39/T4hIunSxYDnefjgBz+Ir33ta/jOd76z6EQIAGzevBk//elPfbe9973vxaWXXoq77757wYsQALjmmmuy2q6HhoZq/hxaCK+88krWOSUWi9X8WphFFQtli+L3v/+919ra6t18883ej370I++Xv/yl95d/+Zfeeeed5/3oRz+q9u6VjB07dnirV6/2/umf/sn7xS9+4b3vfe/zzj//fG9ycrLau1YWjh8/vui6Zk6ePOldcskl3qZNm7yxsTHv17/+tflvIfPII4945513nvff//t/91544QXvQx/6kPea17zGO3HiRLV3rST85//8n72VK1d63/nOd3zf2SuvvFLtXSsri61r5gc/+IFXV1fn3X///d6xY8e8//W//pe3fPly7ytf+Uq1d61k/Mf/+B+91atXe//4j//oHT9+3HviiSe8trY278Mf/nC1d60gFpwQ8TzPO3LkiHfDDTd4LS0t3ooVK7x4PO49+eST1d6tknLu3Dnvv/yX/+Kdf/753ooVK7zrr7/e+9nPflbt3Sobi1GIPPzwwx4A538Lnf/23/6bd/HFF3vLli3zrrzyykXV2hr0nT388MPV3rWystiEiOd53j/8wz94l112mVdfX+9deuml3r59+6q9SyVlenra27Fjh3fRRRd5DQ0NXmdnp3fvvfd6s7Oz1d61gqiZGhFCCCGELD0WdsKaEEIIIQsaChFCCCGEVA0KEUIIIYRUDQoRQgghhFQNChFCCCGEVA0KEUIIIYRUDQoRQgghhFQNChFCCCGEVA0KEUIIIYRUDQoRQgghhFQNChFCCCGEVA0KEUIIIYRUjf8fkppEUiJMBR0AAAAASUVORK5CYII=\n",
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\n",
       "text/plain": [
        "
    "
       ]
@@ -1044,12 +1044,12 @@
      "output_type": "stream",
      "text": [
       "Eigenvalues of Covariance matrix\n",
-      "5.189621963782685\n",
-      "0.7640203256838339\n",
+      "5.269217029290255\n",
+      "0.7571572558830478\n",
       "First eigenvector\n",
-      "[0.84835621 0.52942586]\n",
+      "[0.84614892 0.53294653]\n",
       "Second eigenvector\n",
-      "[-0.52942586  0.84835621]\n"
+      "[-0.53294653  0.84614892]\n"
      ]
     },
     {
@@ -1057,7 +1057,7 @@
      "output_type": "stream",
      "text": [
       "Eigenvector of largest eigenvalue\n",
-      "[0.84835621 0.52942586]\n"
+      "[-0.84614892 -0.53294653]\n"
      ]
     }
    ],
diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter8_65_1.png b/doc/LectureNotes/_build/jupyter_execute/chapter8_65_1.png
index a44444ca6..23a504d1f 100644
Binary files a/doc/LectureNotes/_build/jupyter_execute/chapter8_65_1.png and b/doc/LectureNotes/_build/jupyter_execute/chapter8_65_1.png differ
diff --git a/doc/LectureNotes/_build/jupyter_execute/chapteroptimization.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapteroptimization.ipynb
index 31f5150cc..b19d84d2f 100644
--- a/doc/LectureNotes/_build/jupyter_execute/chapteroptimization.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/chapteroptimization.ipynb
@@ -924,14 +924,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31672/483257001.py:18: MatplotlibDeprecationWarning: Calling gca() with keyword arguments was deprecated in Matplotlib 3.4. Starting two minor releases later, gca() will take no keyword arguments. The gca() function should only be used to get the current axes, or if no axes exist, create new axes with default keyword arguments. To create a new axes with non-default arguments, use plt.axes() or plt.subplot().\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11016/483257001.py:18: MatplotlibDeprecationWarning: Calling gca() with keyword arguments was deprecated in Matplotlib 3.4. Starting two minor releases later, gca() will take no keyword arguments. The gca() function should only be used to get the current axes, or if no axes exist, create new axes with default keyword arguments. To create a new axes with non-default arguments, use plt.axes() or plt.subplot().\n",
       "  ax = fig.gca(projection=\"3d\")\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       ""
+       ""
       ]
      },
      "execution_count": 1,
@@ -1101,7 +1101,7 @@
     {
      "data": {
       "text/plain": [
-       "[]"
+       "[]"
       ]
      },
      "execution_count": 5,
@@ -1802,16 +1802,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[0.29633889 4.15119514]\n",
-      "[[3.90793019]\n",
-      " [3.18761375]]\n",
-      "[[3.90793019]\n",
-      " [3.18761375]]\n"
+      "[0.27637358 4.69167569]\n",
+      "[[4.08692465]\n",
+      " [2.84462849]]\n",
+      "[[4.08692465]\n",
+      " [2.84462849]]\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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SdevWRVJSklnQV9OLL76IZcuWoUuXLpgwYQIyMjJw+fJlHDhwAEuWLMHs2bORnJyMZcuWIT8/H88//7xpaYb8/Hw89dRTyM7Oxp133gnAEFzNmTMHmZmZaNOmDTZu3IjXXnvNZmDiqYYNG6JHjx54/vnnUadOHcyaNQu7du1yOL3+/fffR15eHvr06YORI0eiadOmOHPmDHbu3IlNmzZh/vz5PikvkVsCPICbSJK2bNkiRo0aJVJTU4VarRaRkZGiRYsW4v777xc///yz2bEjRowQderUsXqeHTt2iF69eomYmBjRoEEDcdddd4lDhw4JAOKFF14wO3bx4sWiTZs2IiIiQjRr1ky88sor4oUXXnA4m0wIIS5cuCD++c9/ioyMDBERESFiY2PFDTfcIJ544gmzmUAAxNixYy3Kae2cU6ZMEUlJSSIkJMRippA1H330kUhPTxcRERHi+uuvF5988okYMWKE2WwyIQxT4l9//XXRtm1bERkZKerWrSsyMzPF6NGjxZ49e4QQhllgd955p0hJSRFqtVrEx8eL7t27i8WLF5ud69KlS+L//u//TK8bHx8vevToIVavXm123CeffCI6duwo6tSpI6KiokRaWpq4//77xYYNG0zHdO/eXbRq1cqiXtbqUFBQIDIzM0V4eLjVz7K2kydPigkTJojU1FQRHh4u4uLiRPv27cVzzz0nLly4II4ePSoSEhJEjx49zGZZ6fV60b9/f1G/fn3TzL6zZ8+KBx98UCQkJIjo6Ghx6623ilWrVonu3buL7t27m55rnOFVcwq8ENeWQag9+9D4XTt58qRpm/H7MmvWLJGWlibCw8NFZmammDt3rtlzrc0mE0KI33//XQwZMkQkJCSI8PBw0bhxY9GjRw8xe/Zsu+8Xkb+phBAiUIEYERFJl0qlwtixYzFz5sxAF4XIpzibjIiIiBSNwRAREREpGgdQExGRVRxFQUrBliEiIiJSNAZDREREpGgMhoiIiEjRFDFmSK/X4+jRo4iJibGaNoGIiIikRwiB8+fPO8xD6ClFBENHjx6FRqMJdDGIiIjIDaWlpT5bZR1QSDBkTCFQWlrqVBoFIiIiCryKigpoNBqrqYC8SRHBkLFrrF69egyGiIiIZMbXQ1w4gJqIiIgUjcEQERERKRqDISIiIlI0BkNERESkaAyGiIiISNEYDBEREZGiMRgiIiIiRWMwRERERIrGYIiIiIgUjcEQERERKRqDISIiIlI0BkNERESkaAyGiIiISNEYDBEREZGiMRgiIiIiRWMwRERERIrGYIiIiIgULeDB0MqVK9G/f38kJSVBpVLhm2++sXns6NGjoVKp8NZbb/mtfERERBTcAh4MXbx4EW3btsXMmTPtHvfNN99g3bp1SEpK8lPJiIiISAnCAl2AvLw85OXl2T3myJEjGDduHH788Uf07dvXTyUjIiIiJQh4y5Ajer0ew4cPx9NPP41WrVoFujhEREQUZALeMuTIq6++irCwMEyYMMHp51RVVaGqqsr0c0VFhS+KRkREREFA0i1DGzduxIwZMzBnzhyoVCqnn5efn4/Y2FjTQ6PR+LCUREREJGeSDoZWrVqFEydOoFmzZggLC0NYWBgOHjyIJ598Es2bN7f5vClTpqC8vNz0KC0t9V+hiYiISFYk3U02fPhw9OzZ02xbnz59MHz4cIwaNcrm89RqNdRqta+LR0REREEg4MHQhQsXsHfvXtPP+/fvx5YtWxAXF4dmzZohPj7e7Pjw8HA0btwYGRkZ/i4qERERBaGAB0MbNmxATk6O6edJkyYBAEaMGIE5c+YEqFRERESkFAEPhrKzsyGEcPr4AwcO+K4wREREpDiSHkBNRERE5GsMhoiIiEjRGAwRERGRojEYIiIiIkVjMERERESKxmCIiIiIFI3BEBERESkagyEiIiJSNAZDREREpGgMhoiIiEjRGAwRERGRojEYIiIiIkVjMERERESKxmCIiIiIFI3BEBERESkagyEiIiJSNAZDREREpGgMhoiIiEjRGAwRERGRojEYIiIiIkVjMERERESKxmCIiIiIFI3BEBERESkagyEiIiJSNAZDREREpGgMhoiIiEjRGAwRERGRojEYIiIiIkVjMERERESKxmCIiIiIFI3BEBERESkagyEiIiJSNAZDREREpGgMhoiIiEjRGAwRERGRojEYIiIiIkVjMERERESKxmCIiIiIFI3BEBERESlawIOhlStXon///khKSoJKpcI333xj2nf16lX84x//wA033IA6deogKSkJ999/P44ePRq4AhMREVFQCXgwdPHiRbRt2xYzZ8602FdZWYlNmzbh+eefx6ZNm1BYWIg//vgDf/vb3wJQUiIiIgpGKiGECHQhjFQqFRYtWoSBAwfaPGb9+vW45ZZbcPDgQTRr1syp81ZUVCA2Nhbl5eWoV6+el0pLREREvuSv+3eYz87sI+Xl5VCpVKhfv77NY6qqqlBVVWX6uaKiwg8lIyIiIjkKeDeZKy5fvozJkydj2LBhdiPE/Px8xMbGmh4ajcaPpSQiIiI5kU0wdPXqVQwdOhR6vR6zZs2ye+yUKVNQXl5uepSWlvqplERERCQ3sugmu3r1KoYMGYL9+/dj+fLlDvsN1Wo11Gq1n0pHREREcib5YMgYCO3ZswfFxcWIj48PdJGIiIgoiAQ8GLpw4QL27t1r+nn//v3YsmUL4uLikJSUhMGDB2PTpk34/vvvodPpcOzYMQBAXFwcIiIiAlVsIiIiChIBn1pfUlKCnJwci+0jRozA1KlTkZqaavV5xcXFyM7Oduo1OLWeiIhIfhQztT47Oxv24jEJLYNEREREQUg2s8mIiIiIfIHBEBERESlawLvJiIiI/E6nA1atAsrKgCZNgG7dgNDQQJeKAoTBEBERKUthIfD448Dhw9e2JScDM2YAgwYFrlwUMOwmIyIi5SgsBAYPNg+EAODIEcP2wsLAlIsCisEQEREpg05naBGyNkvZuG3iRMNxUqXTASUlQEGB4V8pl1VGGAwREZEyrFpl2SJUkxBAaanhOCkqLASaNwdycoBhwwz/Nm/O1iwvYDBERETKUFbm3eP8id17PsVgiIiIlKFJE+8e5y/B0L0ncQyGiIhIGbp1M8waU6ms71epAI3GcJyUyL17TwYYDBERkTKEhhqmzwOWAZHx57fekt56Q3Lu3pMJBkNERKQcgwYBCxYATZuab09ONmyX4jpDcu3ek5GAZ633B2atJyIiM3JagVqnM8waO3LE+rghlcoQzO3fL906uEkxWeuJiIj8LjQUyM4OdCmcY+zeGzzYEPjUDIik3L0nI+wmIyIikjo5du/JCFuGiIiI5GDQIGDAAPl078kIgyEiIiK5kFP3nowwGCIiIiL/kOjAdQZDRERE5HuFhYaVtGsuIJmcbBgcHuAxTxxATURERL4l8dxqDIaIiIjId2SQW43BEBEREfmODHKrMRgiIiIi35FBbjUGQ0REROQ7MsitxmCIiIiIfKdbN8OsMWPqkNpUKkCjMRwXIAyGiIiIyHeMudUAy4BIIrnVGAwRERGRb0k8txoXXSQiIiLPOVpdWsK51RgMERERkWecXV3aXm41a8GUnzAYIiIiIvcZV5euvaiicXVpZ7rBbAVT+fneL68VKiGsLQkZXCoqKhAbG4vy8nLUq1cv0MUhIiIKDjod0Ly57UUVVSpDULN/v+3uMFvBlEqFCiEQC/j8/s0B1EREROQeT1eXdiZVhx8wGCIiIiL3eLq6tKNgyk8YDBEREZF7PF1dOoApOGriAGoiIiKyztF0eePq0keOWO/WMo4ZsjUzLIApOGpiyxARERFZKiw0DI7OyQGGDTP827y5YbuRp6tLO0rV4ScMhoiIiMiccYZX7fE8xunyNQMiT1aXdiaY8gNOrSciIqJr3J0u76hLzR5r6wxpNKiYPh2xw4cH/9T6lStXon///khKSoJKpcI333xjtl8IgalTpyIpKQlRUVHIzs7G9u3bA1NYIiIiudHpgJISoKDA8K9OZ/94d6fLG1eXvucew7+upNkYNAg4cAAoLgbmzTP8u38/8Le/OX8ODwQ8GLp48SLatm2LmTNnWt3/73//G2+++SZmzpyJ9evXo3HjxujVqxfOnz/v55ISERF5katBijucGfdTm6fT5d3lSTDloYDPJsvLy0NeXp7VfUIIvPXWW3juuecw6K8+x88++wyJiYmYN28eRo8e7c+iEhEReYezubw8fQ130mR4Ol1ehgLeMmTP/v37cezYMfTu3du0Ta1Wo3v37li9erXN51VVVaGiosLsQUREQcIfLSq+5MrgZHc5s7LzxInW3ztHM7xUKkCj8WsiVV+TdDB07NgxAEBiYqLZ9sTERNM+a/Lz8xEbG2t6aDQan5aTiIj8xJ1uHynxJEhxhSdpMjydLi9Dkg6GjFS1PgwhhMW2mqZMmYLy8nLTo7S01NdFJCIiX/NHi4qveZrLy1mejvvxZLq8DAV8zJA9jRs3BmBoIWpSo2/yxIkTFq1FNanVaqjVap+Xj4iI/MRRi4pKZWhRGTDAPy0W7k4j99fg5D17nDvO3rifQYMM76e70+VlRNItQ6mpqWjcuDGWLVtm2nblyhWsWLECXbp0CWDJiIjIr/zVouIMT7rq/DE4WacDPvzQ8XH20mQYBXCGlz8FvGXowoUL2Lt3r+nn/fv3Y8uWLYiLi0OzZs0wceJETJ8+Henp6UhPT8f06dMRHR2NYcOGBbDURETkV4Ga7l2buzO0jDzN5eUMZzPBP/yw5IObsi3H/fI6AQ+GNmzYgJycHNPPkyZNAgCMGDECc+bMwTPPPINLly5hzJgxOHv2LDp27IilS5ciJiYmUEUmIiJ/k8J0b2901RkHJw8ebDi+5rm8NTjZ2YAwPd391/CRKxeu4NcPtkP7VTm0W5tg62X/TN9nOg4iIpI+Y4oIRy0qtVNEeFNJiaFLzJHiYkOXkj020k/grbc8H5zszXL6wcFfD6Po3X3QFqvx87EsXEDNxo5zABr4/P4d8JYhIiIih/zRouKIN7vqfDk42R9dcR64fO4yVr63HUXzL0C7PRm7rqQBSDbtT1CdRG7qbuTeEYJOI5Nw3c2+LxODISIikgfjdG9rKzd7o0XFEW931RkHJ3ubFALHWvYsOwDt+wehXRmN4pOtcAntrxUX1ehcbzvyOp5F7ohEtLs7AyFhtwKA3xZNZjcZERHJiyfZ0T193UB31bnCl11xDlw8cRHFM7dD+80laHc2x5/VKWb7m4aUIbfFXuT2C0PPCVmonxJr9Tz+un8zGCIiInKWcTYZYL3FRWoLEvopcBR6gZ3f/4miDw9DuzoGK8+0xhVcW+8vHFfQrcE25HauQO4DSWh9ZzpUIbYXTzZiMORFDIaIiMhrAtjiIiUVhyvw8zs7ULT4CrR70lCqM1+tunlYKfIy9iN3gBo5Y7MQk+T6LHAGQ17EYIiIiLwqUF11AST0Ar/P/wPaT8tQtLY+Vpe3QjXCTfvVuIzs+G3Iu+0ich/W4Po+qU61/tjjr/s3B1ATERG5yleDnyXmzJ9nseztndAu0UG773oc02cAyDDtvz58P3KzDiF3UDS6j2mF6IZ+mPrlAwyGiIhIuWy18Ciw5QcA9NV6bJy7C0WfnYB2fTzWXciCHtfSX0XjIm5P3Ibc7MvIfTQV12WnAkgNXIG9hMEQEREpk7WxP8nJhjxcBQWW22fMCMoxQSe2n8TSmX+gqAhYeigDp0QWgCzT/lbqPci74Qhyh9TDraNbQV2vY+AK6yMcM0RERMpjK8eYLVKdLeaIlRau6qsC6z7dAe28MyjalICNlVlmT6mHcvRM2om8nlfRZ0waNB2TAlR4DqD2KgZDRERkYlwvyJlkpjVJbR0hR6y0fJ0KTcATutfxBYabHXpj1E7ktjuOvGEN0OmBLIRHh9c+W0BwADUREZEvOJvVvTYhgNJSw/NtDZ6WyFijq3O/Rth9QwEI1JzPFac7ic8wAiHQ40qzdOT21qP32HQ0adcSQEu/l1MqGAwREZGyOJtjzNXn2xqD5KexRgd/PQztrH348ecwvH38CSRBIKTWMSEQEFBhTvI/odp3wLNATSKBnzcwGCIiImVxNneYK8+3NQbpyBHDdh+MNTImPNUuOI+ibRpTwtPuKEEyjtp8ngrCELDZa+FyxFrg16gRMGvWtRW6HZFQMMVgiIiIlMVRVndbbGV71+kMgYG1cwlheN7EiYYs9R7e7J1JePp4s5+BbU6czN0WMluB38mTwF13AU8/Dfz7347PEcBWtNpqt6AREREFN2NWd+DaLDFH7GV7dzQGqeZYIxddPHERP0xdj3FtVqBF+EFc37s5JizsjiUnO+ASotE0pAwPXr8K8yetwakDF7GqvC3ufOd2507uTguZvcDP6LXXgPnzbe83BlO13zNjK1phoevl8hBbhoiISHkGDTJ0XVnLMTZ0qPV1hmzlHnO2hcWJ44wJT7UfH0bRL8aEpx1M+8NxBbfW3468LuU1Ep7WCmoctXzZauFyhrODz8eONbxXtQNHP7aiuYLBEBERyYu3xpoMGmS46Vo7V36+86/hbAuLjeOMCU+1i69Au+c6HNK1ANDCtD8l9DDyMvYhb6Ax4emN9l/H2PJlbeyOvRYuZzgb+J08aX1MkiutaH5Md8JgiIikT0IDLSnAvD3WxFaOMVdyj7nYElMz4al2bX38Wt4K1ehkOtyY8DS320XkPWJMeJrset3i4oDTpy23ffCB++NyXOlasxY4ebEVzZsYDBGRtElsoCUFUABmbDmlZkuMSmVevr9aYs5PfglLJq2zmfA0PXw/8ryV8NTe6tq1gyNXdetmmDV28qTjY60FTh62ovkKV6AmIumy9UtdrqkRyH2OVo2WwurQVgL3cnUjvKKagn9fngA9rpUrGhfRI2E78nIuoc8jzZHWI8U7ZfDH+7RggWHWmD0ajfXXMJbPUSvaX89lOg4vYjBEJENyuPmR/5SUADk5jo8rLvbrWJOaTmw/iWVv78SBb3/HweNq/IHrsQrdTEFQK/Ue5LY+gry7jQlP1d4vhL/ep2eeMcwas0alsv+HivGPHMBqK1rN5zIdBxEpm0QHWlKASHCsSfXlalPCU+2mBGyszITAbQBuA2BIeDowaT1yb7+C3LEtoOmYDiDdt4Xy1/v0738DHToAY8YAp05d267R2J51Z2RrJp+9GXs+xmCIiKRJgjc/CiBXx5r4aND90U3HoH1nD7Q/hWHZkZY4J9qY7TcmPM29pwE6P5iF8OhONs7kI/4ck3PXXYbAxZ332d5MvgBgMERE0iTRgZYUIK7M2PLioPsr5Zew47m52Kk9iJ8OpWPO1XuhR2PT/jjVGfTW7JJOwlNfrjFkjSuz7rz5XC/jmCEikiYXB1qSAjgz1gTweNC9MeHpuSWrce+5d8zyfJUiGTPVTyKyYzvkDW+IDve3RGiExL5/LozJkToOoPYiBkNEMhVEv9TJS6y1+hjHqQwY4Nag+5oJT7XbNNh5JQ13ohALMBiolfldqFRQAdL/7tl7n6Rc7loYDHkRgyEiGQuSX+rkRbbGA7kwk2qvLhVF7x2okfA02rQ7HFU4rEpBI3EcVjOX+btV0t3xT0GwWClnkxERAZIbaEkSYGusiZOD6SfevhUz9NkArq3tkxRShrwWe5HbLwy5HU6j7j3HbZ/AnzMZPRn/JKExOVLHYIiIpI+/1MkZTg6m36JvYz/haUGBc6/n65mMUl1xOwgxGCIi5QqCbgS6piK1LcJiEhB1/oTV7i09VDgX1ghPPBmB7yZU2U54KoWZjL7I7s7vu00hjg8hIvIhnc4w1qOgwPCvTuef1y0sNAy2zckBhg0z/Nu8uWG7JwJVHwUSeoHfv96NV3JLkF1/C+Kb18V959+DgAr6WuGQgAoqFRD31XsY8EoXxCTF2D6xcXq6yuqIIcN2jcZ709OtcWXRUWf46vseLIQClJeXCwCivLw80EUhopoWLhQiOVkIw692wyM52bDd16+rUpm/LmDYplK5//qBqo+CnN57Rnw18VcxssVK0SSkzOIjTA/fJz5KeVFU1ksw36HRuPY5GL8jtb8nnn5HnDVvnuX309pj3jzn62Lt+e7WpbpaiOJiw+sXFxt+9gF/3b85m4yIAiNQSVh9lfMskEllg6n7o1Zd9J27YuOXe6D9/ASKfovHugtZziU89cZ7EsiZjN7KMebo+w4A8fHA8ePOvz9eXNTSEb/dv30aakkEW4aIJKa62rIFpfZfqxqNZ39t2vrLtbjYub+4i4ulVR9bgqk1ykpdTqOB+CemiRBUmza3Uv8hnmxfLH7690Zxufyyb8vkpxYQq6+bnGy/RceZ75Sz3/dp05wrl69aVW3w1/2bwRAR+Z8vApKa7AUI3ux+8Fd97NXTjzcmX7l66arYOfpNoQeEzsZ7V66qJ5Z2/5c4tPZIoIvrP97oqnP2+x4X5ziwCkDQ76/7NwdQE5H/+TIJq7G7qna3gHE68p49zp3HlZlCgUgq62i2EWCYbSTRAdxHNx3Dpw+swhDNGiRGlaPO+29CwPasnnqiAr1WPA/NkbX+LGZgGbO7N21qvj052fluV2e/x2fOOB6M7e1B3RLCqfVE5H++mrrszHTkDz803FyOHrV+nK1ElvbGoARiKrYrNyYJrNF05cIVrP5oB7RfnkPR702w9XIG8FfC0+4ogQZ26lKTq9PJ5c7TRUe7dQPi4gzBjiOOgvVABP1+IvmWoerqavzzn/9EamoqoqKicN111+HFF1+EXq8PdNGIyF2+mrrsTIBw+DDwyCPXXsfaMW+9ZX6zcTQtORBTsWVwYzr462G8f+9K3Jm0Fg1jLiPniXZ4dV02tl7OgAp63FJnG/7vthJ8+NBvzp9Upi0PHjEuOnrPPYZ/XQkEQ0MNfyA4w1GwLoX1l3xE8i1Dr776KmbPno3PPvsMrVq1woYNGzBq1CjExsbicWc/YCKSltBQw8yTwYMNgULNFhpjQFE7IHGGszf+9HRDN8MjjwCnT5vvi48Htm0DqqoMv9RPnQKGDLFsRaq9CrAv6mOPBG9Ml89dxqrZ21E0/1rCUyDZtL+R6iRyU3cjN1eF3hMy0TCjtWFHCYCPXHghqbU8SH0233PPAW+/bfldN7LVGlqbMeg/csS1VlU58OmIJC/o27eveOCBB8y2DRo0SNx3331On4MDqIkkytpAZ1fXg6nJlYHM9tZeqfkIDXV+wKi362OPt2YbeWjPTwfEO4NLxB2NfhNRuGj+1uGquLXeFvFSz2Kx4b87hO6qzn5dnPnsfDEQ3RNymc23cKHt74krg+39vP4SZ5P9JT8/X6SkpIjdu3cLIYTYsmWLSEhIEPNcmOnBYIhIwrw5ddnZAKGqyrWbrys3Z3fr487zArAw4IXjF8T3L/wmxt1QItLCDli8FUkhR8WD168U8yetFmf2nXX+xM4Ep34K8Jwmt9l83grW/Rj0Mxj6i16vF5MnTxYqlUqEhYUJlUolpk+fbvc5ly9fFuXl5aZHaWkpgyEipXAmQHC2BcnZhyvT8G2V2d3WBR/fmPQ6vdj+7R7xxt+KRa+4DUKNS2YvFY4qkVN/k3g1r1hsXbBb6HV6919s4UIh4uO904Lha4FcW8oT3vrjI8hWoJZ8MFRQUCCSk5NFQUGB2Lp1q/j8889FXFycmDNnjs3nvPDCCwKAxYPBEJFCOAoQnF17xZ2WIXfK6mnrgpdvTOWl5WLR5LXikcwVolloqUXRUkJLxaNZK8Q3U9aKiiMVHr2WhepqwwKAcXF+aXlwW6DWllIYpuP4i0ajweTJkzF27FjTtpdeeglffPEFdu3aZfU5VVVVqKqqMv1cUVEBjUbDdBxESmJvUKuzqQ4ccTd1R80y+iI1iIuEXmDrgj+g/bQMRWvq49fyVqhGuGm/GpeRHb8Nud0uIvdhDTJyU6EKsTJzzpsDiaU+KLmgwDCz0JF58wyzwMgt/krHIfnZZJWVlQgJMV8BIDQ01O7UerVaDbVa7euiEZGUGacjW+NoVowzvDFLLIBrBZ3dfw7L3t4B7Q86aP9MR5k+A0CGaX96+H7kZR1C7qBodB/TCtENb7Z/Qm/nq7L3+UmBBGfzkfskHwz1798fL7/8Mpo1a4ZWrVph8+bNePPNN/HAAw8EumhEJFf2pvbbe07N1ZyTkz1P2OnHtYL01XpsnLsL2s9PQLs+DmvPt4IeXUz7LROepgJIde7ktpLU1l5+IJgE8zRzBZJ8N9n58+fx/PPPY9GiRThx4gSSkpJwzz334P/+7/8QERHh1DmYtZ6IrLLWmlGbsQXoq6+ARo28223jrczkNpzceQo/vr0bWi3w48EMnBINzfa3Uu9BbusjyB1SD90ebQV1PTda1CXS1RcQzzwDvPaa9X0qVXAGgX7mr/u35IMhb2AwRKRQzow7MR7z7bfAJ58AFRXm++PjgQ8+8M1NzRhIOGpdcDKQqL5cjd8+24miL05DuykBGyszIWokGohBBXol7UDu7VfQ57E0NOvc1M7ZnOTjgE4yan+XTp4E7r7bdqvi008D//63f8sYhDhmiIjIE86OYQkNNeRtmjHD+o3NmZxO7vJkJe6/bs5nV+/E2lVX8dm2G7H0SGucFTeYHdYuahfy2h1D7j0N0PnBLIRHd/JuHWSQFsRj1r5LoaH2u1e//BLIzw++1rAgxZYhIgo+tsawGAOMmt0XUujmsXaz1Wisjkm6cuEK/pjwDpLnvor6V06atpciGY9jBkpUOeidvBN5ffToPTYdTdol+qbMRsHeMmTru+QMudZZQiTZTVZaWgqNRuOzwvgKgyEiBXE1uJHKzdxOl97BXw9DO2sftMURiC7bi//ifsCsA8ywmBqggv6rrxE6ZLDvymmt3F7s6pMUR98lRzit3mOS7CbLzMzEpEmTMHnyZNSpU8dXZSIicp+r09Wl0s1TYyr55XOXseq1LRYJT0OgwwHchdqBEACo/vpP6FOTgL/f6b/Aw1dJd6XA0XfJEU6rl43a15Ndy5Ytw9KlS5Geno5PP/3UV2UiInKfq8GNRNaL2fvzQcy8awX6Jf6G+AY69J7SHv/ZlI2dV9IQAh26xmzFnJvegQaHbf/irhno+dOgQYaux6a1BmQnJ8t7RpW7AbBKZejm5LR62XCpZahLly5Yt24dPv/8czz33HN4++238Z///AfZ7BMlIn9wZnaYq8FNgNaLqTxVieKZ26EtrIR2Vwr2Xm0OIMW0PymkDLlpe5HXPwy3j2uJBqltgILtgBOLHgdksPKgQcCAAdJeNdpV7gTAcm8NUyp383hUVlaK559/XkRHR4uBAweKPXv2eJ4cxEeYtZ4oCDibzNTZzPU183f5Ifu7XqcXO77b61nCU+bD8i9H3yVAiNBQaedQkznJ5yarrKzEpk2bsHDhQrz99tsIDw/H2LFjMXXqVMTExHg3YvMQB1ATyZwrs8NqHg9YH8NirevGhRldzqo4XIHl7+5E0TdV0O65Dod0yWb7U0IPIy9jH3IHqNFjXBZikhz87gzmwcpS5ei79PXXQMOGwdMaJjGSnE02e/ZsrF+/HuvXr8fOnTsRGhqKNm3aoFOnTmjXrh3mzp2LP/74A4sWLcLNNzvIY+NHDIaIZMzdqe/uBDceJgd1JuFp9/htyHOU8NQeWzdngKse+4oPAmVyjiSDIY1Gg06dOpkeN998s0VC1OnTp2PevHnYtm2b1wvrLgZDRDLmydR3P2Q+P7v3NLZMLsAfv57E0uNt8Y0YAD2uvUZ6+H7ktjyEvL8bE55Ge16+wkLgkUeA06fNt/tytWyl88N3iSxJMhhyxvHjx5GUlARdzYSGAcZgiIKCUn8ZFxQAw5wYNeynNV301XpsmrcLRZ+dwKXVm/DY5f9Ag2stBofRFJ/XG4/YPp2Q+2hzpPVIsX4iT7K8FxYCf/+75XZ73YBEMiTJdYackZCQgOXLl3v7tETK5smNU45qBn7Hjzv3HB9OfT+58xSWvrMbRUXGhKdZuBO7sABPwbjcoVFT1VE8e34KMHQB0KO79RN6kuVdpzN8F6wRwhAQTZxomNmlhGCZyAuYjoNI6lwdPCx3tvJA2Wpt9sGgYWPCU+3c0yjaaJnwNBZnsTckE/H6E7A64sdemTxN/+HPFbOV2hpJkiHbliEi8iJjK4C1v1mCsRVgwQLgrrsst9sLhACvrOlStuU4tG//Ae1PoVh2uKXVhKe5bY8hb1gDdEk/ibC8E7ZPVnuV65pcXSHboqA+WDFbpzMEWSUlhp+zsw0JaidNUk5rJCkagyEiKfP0xikn8+c7HvNTu4UoOdntGT1XK69i9YfbUVRwDtrfm+D3yxkAriU1baA6i97JO5HbW48+49LRpF0mgEzDzoIC517EWkDiaTDjbHdgQoLhX0etO9YGY7/0kvVzOtONRyRDDIaIfMUbXQxSyZvlLE9mRw0Z4tz5//MfIDHRrff00Joj0L77J4qWR+DnsiycRzvTPhX06FBnB3Lbn0LuvfG4ZWQWQiO6WD+RJyk8PE3/4WjFbKORIw3BZUGB7dYdWwOxbQnG1kgiwP0VqOWEK1CT3zm7WrIjclpx2N06G1f5daaegBDz5jldpEtnL4ml+RvEpPbFomXEXotTNVKdEPelrhJzx/wiTuw46Xxd3Vnl2hvPNbK1YrYzD+Pzvv7atfddit85Cnr+un8zGCLyNuONytZNyJWAyBs3Tn/wpM7OBnxO3oT3/HRAvDO4RPRNWCeiccHsqSGoFl1jfhf/ur1YbPjvDqG7qvO8zu6k8PBG+o+FC4Vo2tS9QEalEqJRI/cDIWeC0upqw2c1b57hX1e+o548l4IKgyEvYjBEQgj//IJ11MrhTvDih7xZHvG0zvPmOX8DtnKeiycvih+m/ibG3VAiWoTvt3hKUshR8UD6SvH1E6vFmX1nvVt3a61hzuam8uS5Rj/95FlA48nDXlDqScuot1pVKSgwGPIiBkPkt1+wvurW8saN01c8rbMrLUMLF5oSnr45wHHC09/n20h4ao+rQXMgW0BcCSS99XAU3HrSSujNVlUKCgyGvIjBkET5qyncn79gnb05uTDuxUSqXQee1tmJzOD6kFCxduDLYnTLFSIltNTikJTQUvFo1grxzZS1ouJIhft1kVurhKtdjLUfDRu6HgjZu2Y8aSX0RasqyR6DIS9iMCRB/rrp+PsXrJwGPHuLN+q8cKH1IOivxxAUmO1S45LoHb9evDmgWOz84U/XW39slUFurRJOBJJ2v/fz57v2PEetkZ58F5R47ZBD/rp/h1ifY0bkQ8YVlWuvn2Ncw6Sw0Huv5co6Pd5gnPasspGJXKUyZLvu1s07rycFXqqz+OthzVVEID18P8a3WYEfpq7HmZN6/HjqZjzxTTYy77jO9czvtTla3BIwTCeXUM5FAIap7TNmGP7f1vtvy1tvGa63hQsNCV5ri483rP1UXGzI+1ZcbFgV2976Qp4sBSG3ZSQoqHCdIfIvf6+o7O9fsMab0+DBhrrUrKcXV0uWFDfrbEx4+uNnZXioeAwaAaj915kKhgDpq0bjEF5WCoSm+qYOcl7cctAgwyKItVOYaDTATTcB339vHsSFhhpWljYGNYMGGa632itQZ2e7/j0N5PpLRJ7wabuTRARFN5lUx4u4yt9N4YFqepfygGdfcaLOJ3acFF889ou4L3WVaKQ6IQAhuqM48N0jvhzr5S+1f0fMn+//br9Ar79EQcdf92+2DMlBMGUs93dLjaPVeo1JMb3dbWX8a1tJSS6t1Lm6Q2f89sUf0N5WAu2mRthwsSUEupqeEoMK/K3+SuCcE+f/+WffvZfB0CoRGnqt1cqYDNZfLbA1y+Buy6gSW1VJOnwaakmErFuG5Dio055AtNRIfZ2eIHN08zHxyaiVYojmV9FAdcbio20XtVNM7lQsSt7aLK5cvOLejChvD7YPtlaJQA9GDvT6SxQ0/HX/Vglh7U+H4FJRUYHY2FiUl5ejXr16gS6O84x/3dkay2Bs1di/Xz5/LRnr5Kilxtt1sta6ptG4neSTrjEmPNV+eQ5FW4wJT6+xTHiaaH4CR98Ja4wtBd5MGGoc2A9Yb5WQU3LSggJg2DDHx82b5zg5rrs8yc3njbx+FBT8df9mMCRlJSVATo7j44qLpTeo055A3XT4C9ZrjAlPtcsj8FNZFs7j2nWlgh4319mJvPYnkXtvPDrc3xJhkQ565G19J+zxReAcLEFzsP7uIMVhMORFsg2GpPDXna8Ey01HIS6fu4xVs7dDu+A8tNuSsaOqhdn+RqqT6NN8N/LyVOg1LgONWjZ0/UWsfSec4e0bejAEzYFqgSXyMn/dvzmAWsqCYVCnLUocYCwzfy4/iKL3DkC7MgrFJ1qhEu1N+0KgQ+eY7ci95QzyRibixqEZCAm71bMXrP2d2LEDeOklx8/79lvvBkM1ByLLFQcjE7mELUNSxr/uyB1utmxUnqpEybvbUbSwEtpdKdh7tbnZ/qSQMuSm7UVuvzD0HN8SDVLr+6b8Rs529TRqZKgrrwFLbIElmWM3mRfJNhgCgmtQJ/meC8swCL3AriX7oP2oFNpf6mLF6daoQqRpfxiu4tb625DXuRy5DyThhkHpnq/07AqdzhDMnTzp+FiOfbHNl91+wdClSJLGbjIysLW6bHIy/7ojc8bAufbfN8Y0JwsW4HynXvj5nR3QflsF7R/X4aAuDUCa6dBmoYeRd/0+5A1Uo8e4LMQk3ejfOtQUGgrce6/he+4IUzTY5qtuv2Ba/4wUjy1DcsG/wMgeB8swCAAnVIloJg7gSo3WHzUuo3v8NuTeegF5jzRDRm6qf1t/HOGsKGmyFXizxZq8jN1kXhQUwRCRPU4GDdkoxpHw5shreRC5g6KRPbYVohtG+7587grGtbbkjp8J+RG7ycj/2PokS/pqPQ58tR7XOXHsl89uReOXswE0922hvKXmrCiAs6KkQM5JbYlsqJ0kmpSqsNDw115OjmFto5wcw8+FhYEuGVlxcucpzB3zK4Zf9wsaR5zGA7M7OPW8xr3a+LhkPmAcN9e0qfn25GR2xwSCv/MLEvkBW4bIqYG3irvhSKyVTHdFh9/m7EDRF6etJjzdgrY4FZKAeP0JWB3x46uEtP7CdamkI5jXPyPFksWYoSNHjuAf//gHioqKcOnSJVx//fX4+OOP0b59e8dPBscM2cX+f0sSmSVTtuU4fpy5B0VLQ7DscEucFQ3M9reN3I28dmXIHVofXR5uhXDtd1yGgXyP65+RH3HM0F/Onj2Lrl27IicnB0VFRUhISMCff/6J+vXrB7powYH9/+YC2EpmPeHptaSm9VXn0Dt5B/J669F7TAsk3ZQBoEZSVC7DQP7A1a0pCEm+ZWjy5Mn49ddfsWrVKrfPwZYhO4I5/5mrAtBK5kzC09ybTiLvPicTnhrrwe4k8jWubk1+wJahvyxevBh9+vTBXXfdhRUrVqBp06YYM2YMHn74YZvPqaqqQlVVlenniooKfxRVntj/f40fWsmqKqqwavZ2FH1dUSPh6bWBwcaEp7m5KvQen4FGLVu5/iLBkFuLpI/juCiISD4Y2rdvH9577z1MmjQJzz77LH777TdMmDABarUa999/v9Xn5OfnY9q0aX4uqUx162Zo7bDV/w8Ycj916eLfcgWCj2bJ/Ln8ILSzD6BohTHh6U2mfTUTnuben4CbhmV6nvCUyF8YeFOQkHw3WUREBG6++WasXr3atG3ChAlYv3491qxZY/U51lqGNBoNu8lssZX/rCZnBhDLvXvGS6sdGxOeagsrUbTTMuFpk5BjyE3bgzx/JTwlIpIpdpP9pUmTJsjKyjLb1rJlSyxcuNDmc9RqNdRqta+LZkmuwYCtgbc1ORpALJEZWB5x1EpmY3q69YSn19b9MSY8ze1UjrwHjQlPG/u6NkRE5CTJB0Ndu3bF7t27zbb98ccfSElJCVCJbJB7MDBoENCvn2Fhu1OnLPcLYQgGJk40jBOoGeQFyzpFLsySOX/0vFMJT3MHqNFjbEvUSw5gwlMiIrJL8t1k69evR5cuXTBt2jQMGTIEv/32Gx5++GF88MEHuPfee506h8+b2YIlaaE73UTBuE6RlcBWaDQ4ePcz+Hp7axSticUv51qjGuGm/TUTnuY+pEHmHddJK+EpEZEMMVFrDd9//z2mTJmCPXv2IDU1FZMmTbI7m6w2n76ZwRQMuDPNPliziut0OP91Ebb/dyNWbm2Ad44OwmGRbHZIi/ADpoSn3R/LQp2EOgEqLBFRcOKYoRr69euHfv36BboY1gXTooXuTLMPojxF+mo9Ns3bBe3nJ6D9LQ5rzudBj2vfuyhUokfCNuR1v4Q+o5ujxe3NIZuEp1In1/F2RBQUZBEMSVoQBQNuDSCW+TpFJ3eewtJ3dkOrFfjxQAZOiiwA1wbsZ6n3Irf1YeQOjkG3R1shsv4tgStssJL7eDsikj0GQ56SeTBgxp1l9t2cgRUojhKexqACPZvsQG6PK+jz2HVI6doCQIvAFTjYBcvgeyKSNVmMGfKUX8YMBVPSQleX2be1TpFEBpC7kvC084NZiKgbEaCSKkwwjbcjIp/gAGov8ttsMkCSwYBbXB3DIaE8RdYTnl5jmfCUa/4ERLAOvicir+EAajkJxmzhri6z76s8RU4GZZYJT9uZ9tVMeJp7bzxuGdESYZEKSC8idcE03o6IZI3BkLcwaaH38xTZGVhb1bOv7xOekm8F03g7IpI1dpPJiZKmH9sYWGv8aRjm4ktcWxMpBDp0itmOPLOEpyF+LDC5LBjH2zmipGuYyAvYTUbmlDT9WKcDHn8cQgjUXsNZBUAPFV7FZKxSdUfvFvtqJDxt4/9y8sbmPndmL8qZkq5hIplhy5AcBEu6DwfBgzHh6bZXvsNdvz7h8HTi5+VQ9XBiAK4vOHNjY7DkHAkNvveZYLmGifyMs8m8SNbBULBMP7YRPFx64RUs3dcCRd8YE54mYygKUAAX04L4kzM3NoCtAK4I5sAxWK5hogBgNxkZBEO6DxvBg/7wEagfHo7PsACLYAgQIlCFpjEVwHknzhuIgbV/deFZHeMihOHG9sgjwOnTlvu5kKBt3h58LyXBcA0TBTmOMJU6uU8/1umgHzse1hogQ/4aDj0T4zC+9XL8MHU9zhyvxutnHzL8payqPWLoLyqVoRslEKtaO3NjsxYIGfcBwMSJhqCKlEHu1zCRAjAYkjoZTj/WV+ux4fMdeKlnCcbHzEHIsaMWA6GNQiCQhDK8/U4I7nihgyHzu3FgLWAZEAV6YK2nN6yarQCkDDK8homUhsGQ1Blzf0mxlaSGU7tPY97YXzH8ul/QOOI0OozIwvM/Z+PUpWjnTlA7yDAuZNm0qfn25OTAdjN564bFVgDlkMk1TKRkHDMkdRKdfmxMeKqdexpFGy0TntbFefRssh3DWp0AfnLihNaCDCkuZOkoMa2z2AqgHBK9honoGs4m8xZfz4aRwPRjY8JT7dIQLLWR8DS3bRny7qmR8DQYF9ZzlIsuLg44cyZ46kveIYFrmEhuOLXei/ySqNUf06j9PP24ZsJT7e+NseVSptl+Y8LT3J469BmXbjvhaTAmsrV3YwOCr77kHcG8hACRDzAY8iKfvpneWkxNIr8kLROemr9fN0fvQF77EzUSnjrZ0xqMfxXb+8yCsb5ERH7GYMiLfPZmemsxtQAu019VUYVVs7dD+3UFikwJT69pqDqFPim7kZcH9Bp7PRJaNXL/xVwJ+HwVHPoz6JRIgEtEJFcMhrzIZ29mSQmQ40Q6iOJi24upBWCZ/j+XH4R29gFoV0Rh+YlWqEQd0z5jwtPcDmeQNyJACU99FRwyN5Q8MagkUiyuQC0Hni6m5sxqxhMnGmZUefDLv/JUJUre3Q5tYSW0O5thz9VUACmm/U1CjiE3bQ9y+4ai14Qs3yc8ddS9ZC049HT1Zl+dl3yLASwR+QFbhjzhacuQN1qWrBB6gd3a/Sj64BC0v9TFitOtUYVI0/4wXMWt9bcht1M5ckc1QZvB10MVYmtZRC+zd3MbMMA3OZyYG0qemNyUSPHYMiQHjtacMd5kbS2m5sVl+s8fPY/lM3dA+20VftyVgmb6Q2iCMlShCa4iHM1CDyPv+n3IHaBGj7EtUS/5Rude25sctc5MneqbHE7MDSU/fmo1JSICGAx5xtPF1DxYpl/oBf5XuAfaT45CuyYWv5xrhavoiDtRiBW4Fxpcu/lfjW+MsNkzoRr8dycr9hdvjtVw5ub29tvOncvV1ZuZG0p+GMASkR8xGPKUMW2Eta4fR9OoXWxZOnewHMtm7ID2+2po/2yBo/rrAVxvOnxM6GzM1D1mcZrwM8eBIXfZ71aoHficOgU88YT3xmp4kuC0tj17XHtt5oaSHwawRORHHDPkLe62othZkFAA2Df+LRRsbwPtb3FYez4LuhrxaxQq0SNhG3Jvu4TchzVo8WB398bFWBvHY+scgHtjNQoKgGHDHB/XoAFw9qz9Y+LjgePHnW+lCsZVsIOdj8bTEZG8+GvMEIQClJeXCwCivLw80EWxbuFCIZKThTDcqoUAxKnQBDESn9TcJAAhWkbsFZPaF4ul+RvEpbOXrp2juFhYHGztUVxs+doqlXPPBQzHajRCVFe7VkdnyzdypHPHTZvm+nusUlnW1bht4ULXzke+VV1tuCZsfTfd/R4Skaz46/7NrPUBpruiw5pT6ZiaOgePRn6CYfgC2ShGgu4o5mAU6uI8BjZZi9nDVuLAL4exoyoNb2zIRq/J7RFZ/9oMMbe6FeyN47Gl5lgNVzibubtnT+fON2OGofzOMnZnNm1qvj05mbOSpMg4Hg+w/M4wuSkReRnHDAXAsa0noH37jxoJT28w2982cjeebrsKuUPro8tDWYio28nxSd0ZF+NoHI89ro7VcHaweVycc+c7c8b1wbODBhlmH3EBP3nwZDweEZELGAz5wdXKq1jz8Q4UzTtbI+Fpgmm/ZcLTDAAZrr2IO9P8PRl86s5gY2dubjrdtazvjrhT/tBQjjGREwawROQHDIZ8pHTdUWjf3Yuin4wJT9ua7bdMeNrFsxd0Z5q/OwGNo7WTHHF0cwsNNQRLL7zg+Fyc/aUMDGCJyMc4m8xL/Jrw1B5XsqU7mmVVm79W/tXpgMRE21PtOfuLiEgRmKjVi3z1Zu4rOYSiWfsdJjzNvT8B7e/1Y8JTV6b525rab42toMoXCguBv1tZJJKpGIiIFIPBkBd5682sPFWJFbO2o2hhzYSn19RMeNpzXEvEpTXwtOjO8XSlaFutSW+8ATRqFLixGq60chERUdBhMORF7r6ZxoSn2g9LUbSqjtWEp11jtyOv8zn/Jzw18lZWb2+m3vAmqZaLiIh8jsGQF7nyZtZMeKr9IxUHqjVm+5uFHkZu+j7kDTQmPPXhipiOMKs3EREFMQZDXmTvzRR6gW2L9qDo45oJTyNM+yNQhe5x25B763nkPaxB5h3X+b/1xxrj4Gd30m8QERHJgL+CIUVOrT93sBw/vb0DRd9ZT3iaFnYQeS0PIHdQNLLHZKFOQvvAFdYWZvUmIiLyCtkFQ/n5+Xj22Wfx+OOP46233nLpua8NWIXijZq/Ep52Nm2PQiVyGm1HXvdK5D7aHC1uTwGQ4t2CexuzehMREXmFrIKh9evX44MPPkCbNm3cev5LJd0AGJrZWkb8idzWpci7KwbdHm2FyPodvFhSP3An/YYScMA1ERG5SDbB0IULF3Dvvffiww8/xEsvveTWOfomrkf/28ORO+Y6pHRNA5Dm3UL6kzvpN4Kdt2bWERGRosgma/3YsWPRt29f9HQiq3lVVRUqKirMHgAw748OGD33NqR0TTZ/gk4HlJQABQWGf13Jhh4ozOptzjizrvY4qiNHDNsLCwNTLiIikjxZBENffvklNm3ahPz8fKeOz8/PR2xsrOmh0WhsH1xYaJiVlZMDDBtm+Ld5c3ncPI2JT5s2Nd+enKysafU6naFFyFoLmXHbxInyCHKJiMjvJD+1vrS0FDfffDOWLl2Ktm0NyU6zs7PRrl07mwOoq6qqUFVVZfq5oqICGo3GcmpesKzTo/RxMiUlhiDWkeJizqwjIpIRTq3/y8aNG3HixAm0b39tertOp8PKlSsxc+ZMVFVVIbTWjV+tVkOtVts/saPWBJXK0JowYID0AwulZ/XmzDoiIvKA5IOh22+/Hf/73//Mto0aNQqZmZn4xz/+YREIOY3r9AQPzqwjIiIPSD4YiomJQevWrc221alTB/Hx8RbbXcLWhODBmXVEROQBWQyg9gm2JgQPzqwjIiIPSH4AtTdYHYBlzO3lqDWBub3kw9o6QxqNIRCSw0B4IiIywwHUvmZsTRg82BD41AyI2JogT4MGGQa8K3lmHRERuUy5wRBwbZ0ea6sWszVBnpQ+s46IiFym7GAIYGsCERGRwjEYAtiaQEREpGDKnU1GREREBAZDREREpHDsJpMipecaIyIi8iMGQ1Jjba2c5GTDMgCc3UZEROR1yusm0+kMWc4LCgz/6nSBLtE1hYWGdY9q50w7csSwvbAwMOUiIiIKYsoKhhYvNqw6nZMDDBtm+Ld5c2kEGTqdoUXI2mrYxm0TJ0oreCMiIgoCygqGhg+XbqvLqlWWZatJCKC01HAcEREReY2ygiFrpNLqUlbm3eOIiIjIKRxADZi3uvhz8cWas8aOH3fuOU2a+LZMRERECsNgqCZ/trpYmzUWGmq7dUqlMswq69bNP+UjIiJSCAZDNfmr1cU4a6z2YGl7gRBgSB7L9YaIiIi8imOGAEOwodH4p9XF3qwxo9oBT3IysGAB1xkiIiLyAeW1DKlU5oGIv1tdHM0aAwwB03/+AyQmcgVqIiIiH1NWy9B//ws0bWq+zd+tLs6OS0pMBO65xzCgm4EQERGRzyirZehvfzMEGIHM++XsuCTOGiMiIvILZQVDgCHw8ef0+dq6dTO0Rh05Yn3cEGeNERER+ZWyusmkIDTUkHQVuDZeyYizxoiIiPyOwVAgDBpkGKcU6PFLREREpMBuMqkYNAgYMCCw45eIiIiIwVBABXr8EhEREbGbjIiIiJSNwRAREREpGoMhIiIiUjQGQ0RERKRoDIaIiIhI0RgMERERkaIxGCIiIiJFYzBEREREisZgiIiIiBSNwRAREREpGoMhIiIiUjQGQ0RERKRoDIaIiIhI0RgMERERkaJJPhjKz89Hhw4dEBMTg4SEBAwcOBC7d+8OdLGIiIgoSEg+GFqxYgXGjh2LtWvXYtmyZaiurkbv3r1x8eLFQBeNiIiIgoBKCCECXQhXnDx5EgkJCVixYgVuu+02p55TUVGB2NhYlJeXo169ej4uIREREXmDv+7fYT47s4+Ul5cDAOLi4mweU1VVhaqqKtPPFRUVPi8XERERyZPku8lqEkJg0qRJuPXWW9G6dWubx+Xn5yM2Ntb00Gg0fiwlERERyYmsusnGjh2LH374Ab/88guSk5NtHmetZUij0bCbjIiISEbYTVbL+PHjsXjxYqxcudJuIAQAarUaarXaTyUjIiIiOZN8MCSEwPjx47Fo0SKUlJQgNTU10EUiIiKiICL5YGjs2LGYN28evv32W8TExODYsWMAgNjYWERFRQW4dERERCR3kh8zpFKprG7/9NNPMXLkSKfOwan1RERE8sMxQ3+ReKxGREREMierqfVERERE3sZgiIiIiBSNwRAREREpGoMhIiIiUjQGQ0RERKRoDIaIiIhI0RgMERERkaIxGCIiIiJFYzBEREREisZgiIiIiBSNwRAREREpGoMhIiIiUjQGQ0RERKRoDIaIiIhI0RgMERERkaIxGCIiIiJFYzBEREREisZgiIiIiBSNwRAREREpGoMhIiIiUjQGQ0RERKRoDIaIiIhI0RgMERERkaIxGCIiIiJFYzBEREREisZgiIiIiBSNwRAREREpGoMhIiIiUjQGQ0RERKRoDIaIiIhI0RgMERERkaIxGCIiIiJFYzBEREREisZgiIiIiBSNwRAREREpGoMhIiIiUjQGQ0RERKRoDIaIiIhI0WQTDM2aNQupqamIjIxE+/btsWrVqkAXiYiIiIKALIKhr776ChMnTsRzzz2HzZs3o1u3bsjLy8OhQ4cCXTQiIiKSOZUQQgS6EI507NgRN910E9577z3TtpYtW2LgwIHIz893+PyKigrExsaivLwc9erV82VRiYiIyEv8df+WfMvQlStXsHHjRvTu3dtse+/evbF69eoAlYqIiIiCRVigC+DIqVOnoNPpkJiYaLY9MTERx44ds/qcqqoqVFVVmX4uLy8HYIgwiYiISB6M921fd2JJPhgyUqlUZj8LISy2GeXn52PatGkW2zUajU/KRkRERL5z+vRpxMbG+uz8kg+GGjZsiNDQUItWoBMnTli0FhlNmTIFkyZNMv187tw5pKSk4NChQz59M6WmoqICGo0GpaWlihorxXqz3krAerPeSlBeXo5mzZohLi7Op68j+WAoIiIC7du3x7Jly3DnnXeati9btgwDBgyw+hy1Wg21Wm2xPTY2VlFfIqN69eqx3grCeisL660sSq13SIhvhzhLPhgCgEmTJmH48OG4+eab0blzZ3zwwQc4dOgQHn300UAXjYiIiGROFsHQ3XffjdOnT+PFF19EWVkZWrdujSVLliAlJSXQRSMiIiKZk0UwBABjxozBmDFj3HquWq3GCy+8YLXrLJix3qy3ErDerLcSsN6+rbcsFl0kIiIi8hXJL7pIRERE5EsMhoiIiEjRGAwRERGRojEYIiIiIkWTZTA0a9YspKamIjIyEu3bt8eqVavsHr9ixQq0b98ekZGRuO666zB79myLYxYuXIisrCyo1WpkZWVh0aJFviq+21ypd2FhIXr16oVGjRqhXr166Ny5M3788UezY+bMmQOVSmXxuHz5sq+r4hJX6l1SUmK1Trt27TI7Ltg+75EjR1qtd6tWrUzHyOHzXrlyJfr374+kpCSoVCp88803Dp8TDNe3q/UOluvb1XoHy/Xtar2D5frOz89Hhw4dEBMTg4SEBAwcOBC7d+92+Dx/XOOyC4a++uorTJw4Ec899xw2b96Mbt26IS8vD4cOHbJ6/P79+3HHHXegW7du2Lx5M5599llMmDABCxcuNB2zZs0a3H333Rg+fDh+//13DB8+HEOGDMG6dev8VS2HXK33ypUr0atXLyxZsgQbN25ETk4O+vfvj82bN5sdV69ePZSVlZk9IiMj/VElp7hab6Pdu3eb1Sk9Pd20Lxg/7xkzZpjVt7S0FHFxcbjrrrvMjpP6533x4kW0bdsWM2fOdOr4YLm+Xa13sFzfrtbbSO7Xt6v1Dpbre8WKFRg7dizWrl2LZcuWobq6Gr1798bFixdtPsdv17iQmVtuuUU8+uijZtsyMzPF5MmTrR7/zDPPiMzMTLNto0ePFp06dTL9PGTIEJGbm2t2TJ8+fcTQoUO9VGrPuVpva7KyssS0adNMP3/66aciNjbWW0X0CVfrXVxcLACIs2fP2jynEj7vRYsWCZVKJQ4cOGDaJofPuyYAYtGiRXaPCZbruyZn6m2NHK/vmpypd7Bc3zW583kHw/UthBAnTpwQAMSKFStsHuOva1xWLUNXrlzBxo0b0bt3b7PtvXv3xurVq60+Z82aNRbH9+nTBxs2bMDVq1ftHmPrnP7mTr1r0+v1OH/+vEWyuwsXLiAlJQXJycno16+fxV+WgeRJvW+88UY0adIEt99+O4qLi832KeHz/vjjj9GzZ0+LVdql/Hm7Ixiub2+Q4/XtCTlf394QLNd3eXk5ANhNwuqva1xWwdCpU6eg0+ksstUnJiZaZLU3OnbsmNXjq6urcerUKbvH2Dqnv7lT79reeOMNXLx4EUOGDDFty8zMxJw5c7B48WIUFBQgMjISXbt2xZ49e7xafne5U+8mTZrggw8+wMKFC1FYWIiMjAzcfvvtWLlypemYYP+8y8rKUFRUhIceeshsu9Q/b3cEw/XtDXK8vt0RDNe3p4Ll+hZCYNKkSbj11lvRunVrm8f56xqXTTqOmlQqldnPQgiLbY6Or73d1XMGgrtlLCgowNSpU/Htt98iISHBtL1Tp07o1KmT6eeuXbvipptuwjvvvIO3337bewX3kCv1zsjIQEZGhunnzp07o7S0FK+//jpuu+02t84ZKO6Wcc6cOahfvz4GDhxotl0un7erguX6dpfcr29XBNP17a5gub7HjRuHrVu34pdffnF4rD+ucVm1DDVs2BChoaEW0d6JEycsokKjxo0bWz0+LCwM8fHxdo+xdU5/c6feRl999RUefPBBfP311+jZs6fdY0NCQtChQwfJ/CXhSb1r6tSpk1mdgvnzFkLgk08+wfDhwxEREWH3WKl93u4IhuvbE3K+vr1Fbte3J4Ll+h4/fjwWL16M4uJiJCcn2z3WX9e4rIKhiIgItG/fHsuWLTPbvmzZMnTp0sXqczp37mxx/NKlS3HzzTcjPDzc7jG2zulv7tQbMPzFOHLkSMybNw99+/Z1+DpCCGzZsgVNmjTxuMze4G69a9u8ebNZnYL18wYMszX27t2LBx980OHrSO3zdkcwXN/ukvv17S1yu749IffrWwiBcePGobCwEMuXL0dqaqrD5/jtGnd6qLVEfPnllyI8PFx8/PHHYseOHWLixImiTp06plH1kydPFsOHDzcdv2/fPhEdHS2eeOIJsWPHDvHxxx+L8PBwsWDBAtMxv/76qwgNDRWvvPKK2Llzp3jllVdEWFiYWLt2rd/rZ4ur9Z43b54ICwsT7777rigrKzM9zp07Zzpm6tSpQqvVij///FNs3rxZjBo1SoSFhYl169b5vX62uFrv//znP2LRokXijz/+ENu2bROTJ08WAMTChQtNxwTj52103333iY4dO1o9pxw+7/Pnz4vNmzeLzZs3CwDizTffFJs3bxYHDx4UQgTv9e1qvYPl+na13sFyfbtabyO5X9+PPfaYiI2NFSUlJWbf28rKStMxgbrGZRcMCSHEu+++K1JSUkRERIS46aabzKbljRgxQnTv3t3s+JKSEnHjjTeKiIgI0bx5c/Hee+9ZnHP+/PkiIyNDhIeHi8zMTLOLSypcqXf37t0FAIvHiBEjTMdMnDhRNGvWTERERIhGjRqJ3r17i9WrV/uxRs5xpd6vvvqqSEtLE5GRkaJBgwbi1ltvFT/88IPFOYPt8xZCiHPnzomoqCjxwQcfWD2fHD5v49RpW9/bYL2+Xa13sFzfrtY7WK5vd77nwXB9W6szAPHpp5+ajgnUNa76q4BEREREiiSrMUNERERE3sZgiIiIiBSNwRAREREpGoMhIiIiUjQGQ0RERKRoDIaIiIhI0RgMERERkaIxGCIiIiJFYzBEREREisZgiIiIiBSNwRARyVJBQQEiIyNx5MgR07aHHnoIbdq0QXl5eQBLRkRyw9xkRCRLQgi0a9cO3bp1w8yZMzFt2jR89NFHWLt2LZo2bRro4hGRjIQFugBERO5QqVR4+eWXMXjwYCQlJWHGjBlYtWoVAyEichlbhohI1m666SZs374dS5cuRffu3QNdHCKSIY4ZIiLZ+vHHH7Fr1y7odDokJiYGujhEJFNsGSIiWdq0aROys7Px7rvv4ssvv0R0dDTmz58f6GIRkQxxzBARyc6BAwfQt29fTJ48GcOHD0dWVhY6dOiAjRs3on379oEuHhHJDFuGiEhWzpw5g65du+K2227D+++/b9o+YMAAVFVVQavVBrB0RCRHDIaIiIhI0TiAmoiIiBSNwRAREREpGoMhIiIiUjQGQ0RERKRoDIaIiIhI0RgMERERkaIxGCIiIiJFYzBEREREisZgiIiIiBSNwRAREREpGoMhIiIiUjQGQ0RERKRo/w8sMWcMUM7SsAAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "
    "
       ]
@@ -1896,9 +1896,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[3.96783837]\n",
-      " [3.23305112]]\n",
-      "[3.95982273] [3.21682143]\n"
+      "[[4.17086577]\n",
+      " [2.92317667]]\n",
+      "[4.14538257] [2.90670236]\n"
      ]
     }
    ],
@@ -2002,15 +2002,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[3.91619855]\n",
-      " [3.20101684]]\n",
-      "[[3.85813693]\n",
-      " [3.24679418]]\n"
+      "[[4.11027723]\n",
+      " [2.92805329]]\n",
+      "[[4.04931542]\n",
+      " [2.97504526]]\n"
      ]
     },
     {
      "data": {
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\n",
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\n",
       "text/plain": [
        "
    "
       ]
@@ -2389,20 +2389,20 @@
      "output_type": "stream",
      "text": [
       "Own inversion\n",
-      "[[3.70224083]\n",
-      " [3.16389131]]\n",
-      "Eigenvalues of Hessian Matrix:[0.29022057 4.66510547]\n",
+      "[[3.22532324]\n",
+      " [3.44210664]]\n",
+      "Eigenvalues of Hessian Matrix:[0.30012384 4.62464344]\n",
       "theta from own gd\n",
-      "[[3.70224083]\n",
-      " [3.16389131]]\n",
+      "[[3.22532324]\n",
+      " [3.44210664]]\n",
       "theta from own sdg\n",
-      "[[3.67541155]\n",
-      " [3.1532465 ]]\n"
+      "[[3.17736035]\n",
+      " [3.48289037]]\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
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index 29309e4fe..85e9ff71d 100644
--- a/doc/LectureNotes/_build/jupyter_execute/exercisesweek41.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/exercisesweek41.ipynb
@@ -116,20 +116,20 @@
      "output_type": "stream",
      "text": [
       "Own inversion\n",
-      "[[3.89481038]\n",
-      " [3.13155259]]\n",
-      "Eigenvalues of Hessian Matrix:[0.28194659 4.81122914]\n",
+      "[[3.97739698]\n",
+      " [2.9189726 ]]\n",
+      "Eigenvalues of Hessian Matrix:[0.27987128 4.51549827]\n",
       "theta from own gd\n",
-      "[[3.89481038]\n",
-      " [3.13155259]]\n",
+      "[[3.97739698]\n",
+      " [2.9189726 ]]\n",
       "theta from own sdg\n",
-      "[[3.88291866]\n",
-      " [3.19123037]]\n"
+      "[[3.9577228 ]\n",
+      " [2.91473433]]\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "
    "
       ]
@@ -374,9 +374,9 @@
      "output_type": "stream",
      "text": [
       "Own inversion\n",
-      "[[4.23636536]\n",
-      " [2.77184871]]\n",
-      "Eigenvalues of Hessian Matrix:[0.30125775 4.66878535]\n"
+      "[[4.32133765]\n",
+      " [2.59905073]]\n",
+      "Eigenvalues of Hessian Matrix:[0.29426584 4.28858038]\n"
      ]
     },
     {
@@ -384,13 +384,13 @@
      "output_type": "stream",
      "text": [
       "theta from own gd\n",
-      "[[4.23636536]\n",
-      " [2.77184871]]\n"
+      "[[4.32133765]\n",
+      " [2.59905073]]\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "
    "
       ]
@@ -481,73 +481,73 @@
       "Own inversion\n",
       "[[4.]\n",
       " [3.]]\n",
-      "Eigenvalues of Hessian Matrix:[0.35058127 4.29679459]\n",
-      "0 [-8.31895514] [-8.15055258]\n",
-      "1 [-0.75472506] [0.63957747]\n",
-      "2 [-0.69314603] [0.58739348]\n",
-      "3 [-0.63659131] [0.53946725]\n",
-      "4 [-0.58465096] [0.49545139]\n",
-      "5 [-0.5369485] [0.45502684]\n",
-      "6 [-0.49313815] [0.41790059]\n",
-      "7 [-0.45290234] [0.38380352]\n",
-      "8 [-0.41594943] [0.35248847]\n",
-      "9 [-0.38201155] [0.32372846]\n",
-      "10 [-0.35084272] [0.29731502]\n",
-      "11 [-0.32221699] [0.27305669]\n",
-      "12 [-0.29592687] [0.25077762]\n",
-      "13 [-0.2717818] [0.23031634]\n",
-      "14 [-0.24960675] [0.21152452]\n",
-      "15 [-0.229241] [0.19426595]\n",
-      "16 [-0.21053692] [0.17841553]\n",
-      "17 [-0.19335893] [0.16385836]\n",
-      "18 [-0.17758251] [0.15048894]\n",
-      "19 [-0.16309331] [0.13821034]\n",
-      "20 [-0.14978631] [0.12693357]\n",
-      "21 [-0.13756504] [0.11657689]\n",
-      "22 [-0.12634093] [0.10706523]\n",
-      "23 [-0.1160326] [0.09832963]\n",
-      "24 [-0.10656534] [0.09030678]\n",
-      "25 [-0.09787053] [0.08293853]\n",
-      "26 [-0.08988514] [0.07617146]\n",
-      "27 [-0.08255129] [0.06995653]\n",
-      "28 [-0.07581582] [0.06424868]\n",
-      "29 [-0.06962991] [0.05900655]\n",
+      "Eigenvalues of Hessian Matrix:[0.30311767 4.03556032]\n",
+      "0 [-14.28624958] [-15.54071847]\n",
+      "1 [-0.04900086] [0.04473913]\n",
+      "2 [-0.04532032] [0.0413787]\n",
+      "3 [-0.04191624] [0.03827068]\n",
+      "4 [-0.03876784] [0.0353961]\n",
+      "5 [-0.03585592] [0.03273744]\n",
+      "6 [-0.03316272] [0.03027848]\n",
+      "7 [-0.03067182] [0.02800421]\n",
+      "8 [-0.02836801] [0.02590077]\n",
+      "9 [-0.02623724] [0.02395532]\n",
+      "10 [-0.02426651] [0.022156]\n",
+      "11 [-0.02244382] [0.02049182]\n",
+      "12 [-0.02075802] [0.01895265]\n",
+      "13 [-0.01919885] [0.01752908]\n",
+      "14 [-0.0177568] [0.01621244]\n",
+      "15 [-0.01642305] [0.0149947]\n",
+      "16 [-0.01518949] [0.01386842]\n",
+      "17 [-0.01404858] [0.01282674]\n",
+      "18 [-0.01299337] [0.0118633]\n",
+      "19 [-0.01201742] [0.01097223]\n",
+      "20 [-0.01111477] [0.01014809]\n",
+      "21 [-0.01027992] [0.00938585]\n",
+      "22 [-0.00950778] [0.00868086]\n",
+      "23 [-0.00879363] [0.00802883]\n",
+      "24 [-0.00813313] [0.00742577]\n",
+      "25 [-0.00752224] [0.00686801]\n",
+      "26 [-0.00695723] [0.00635214]\n",
+      "27 [-0.00643466] [0.00587502]\n",
+      "28 [-0.00595134] [0.00543374]\n",
+      "29 [-0.00550433] [0.0050256]\n",
       "theta from own gd\n",
-      "[[3.81759234]\n",
-      " [3.15457792]]\n",
-      "0 [-0.06394871] [0.05419212]\n",
-      "1 [-0.05873105] [0.04977051]\n",
-      "2 [-0.0523738] [0.04438319]\n",
-      "3 [-0.04619338] [0.03914571]\n",
-      "4 [-0.04057027] [0.03438051]\n",
-      "5 [-0.03557316] [0.0301458]\n",
-      "6 [-0.03117156] [0.02641575]\n",
-      "7 [-0.02730775] [0.02314144]\n",
-      "8 [-0.02392053] [0.020271]\n",
-      "9 [-0.02095266] [0.01775594]\n",
-      "10 [-0.01835274] [0.01555268]\n",
-      "11 [-0.01607534] [0.01362274]\n",
-      "12 [-0.01408051] [0.01193226]\n",
-      "13 [-0.01233322] [0.01045155]\n",
-      "14 [-0.01080274] [0.00915458]\n",
-      "15 [-0.00946219] [0.00801855]\n",
-      "16 [-0.00828799] [0.0070235]\n",
-      "17 [-0.0072595] [0.00615193]\n",
-      "18 [-0.00635865] [0.00538851]\n",
-      "19 [-0.00556958] [0.00471983]\n",
-      "20 [-0.00487843] [0.00413413]\n",
-      "21 [-0.00427304] [0.00362111]\n",
-      "22 [-0.00374279] [0.00317175]\n",
-      "23 [-0.00327833] [0.00277816]\n",
-      "24 [-0.00287151] [0.00243341]\n",
-      "25 [-0.00251517] [0.00213144]\n",
-      "26 [-0.00220306] [0.00186694]\n",
-      "27 [-0.00192967] [0.00163526]\n",
-      "28 [-0.00169021] [0.00143234]\n",
-      "29 [-0.00148047] [0.00125459]\n",
+      "[[3.98320492]\n",
+      " [3.01533437]]\n",
+      "0 [-0.00509089] [0.00464812]\n",
+      "1 [-0.0047085] [0.00429899]\n",
+      "2 [-0.00424012] [0.00387135]\n",
+      "3 [-0.00378113] [0.00345227]\n",
+      "4 [-0.00335942] [0.00306724]\n",
+      "5 [-0.00298058] [0.00272135]\n",
+      "6 [-0.00264305] [0.00241318]\n",
+      "7 [-0.00234327] [0.00213947]\n",
+      "8 [-0.00207732] [0.00189665]\n",
+      "9 [-0.00184151] [0.00168135]\n",
+      "10 [-0.00163245] [0.00149047]\n",
+      "11 [-0.00144711] [0.00132125]\n",
+      "12 [-0.00128282] [0.00117125]\n",
+      "13 [-0.00113717] [0.00103827]\n",
+      "14 [-0.00100807] [0.00092039]\n",
+      "15 [-0.00089362] [0.0008159]\n",
+      "16 [-0.00079216] [0.00072326]\n",
+      "17 [-0.00070222] [0.00064115]\n",
+      "18 [-0.0006225] [0.00056836]\n",
+      "19 [-0.00055182] [0.00050383]\n",
+      "20 [-0.00048917] [0.00044663]\n",
+      "21 [-0.00043363] [0.00039592]\n",
+      "22 [-0.0003844] [0.00035097]\n",
+      "23 [-0.00034076] [0.00031112]\n",
+      "24 [-0.00030207] [0.0002758]\n",
+      "25 [-0.00026778] [0.00024449]\n",
+      "26 [-0.00023737] [0.00021673]\n",
+      "27 [-0.00021042] [0.00019212]\n",
+      "28 [-0.00018653] [0.00017031]\n",
+      "29 [-0.00016536] [0.00015097]\n",
       "theta from own gd wth momentum\n",
-      "[[3.99630114]\n",
-      " [3.00313452]]\n"
+      "[[3.99951642]\n",
+      " [3.00044152]]\n"
      ]
     }
    ],
@@ -631,17 +631,17 @@
      "output_type": "stream",
      "text": [
       "Own inversion\n",
-      "[[4.08185019]\n",
-      " [2.82781715]]\n",
-      "Eigenvalues of Hessian Matrix:[0.32244056 3.90871918]\n",
-      "0 [-14.19393543] [-14.29174301]\n",
-      "1 [-6.35255737e-15] [-1.10851799e-14]\n",
-      "2 [-5.89805982e-17] [-2.66490332e-16]\n",
-      "3 [9.81853487e-16] [1.10741066e-15]\n",
-      "4 [-5.89805982e-17] [-2.66490332e-16]\n",
+      "[[3.66959644]\n",
+      " [3.26513904]]\n",
+      "Eigenvalues of Hessian Matrix:[0.33285444 4.11450263]\n",
+      "0 [-12.48534921] [-14.7906583]\n",
+      "1 [-1.09712586e-14] [-5.19623863e-15]\n",
+      "2 [-1.27068495e-16] [-2.9424428e-16]\n",
+      "3 [5.91540705e-16] [7.2837521e-16]\n",
+      "4 [-1.27068495e-16] [-2.9424428e-16]\n",
       "beta from own Newton code\n",
-      "[[4.08185019]\n",
-      " [2.82781715]]\n"
+      "[[3.66959644]\n",
+      " [3.26513904]]\n"
      ]
     }
    ],
@@ -711,30 +711,24 @@
      "output_type": "stream",
      "text": [
       "Own inversion\n",
-      "[[3.41716708]\n",
-      " [3.50046106]]\n",
-      "Eigenvalues of Hessian Matrix:[0.24252405 4.30774404]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "[[3.68184997]\n",
+      " [3.32507975]]\n",
+      "Eigenvalues of Hessian Matrix:[0.26370919 4.62518501]\n",
       "theta from own gd\n",
-      "[[3.41716708]\n",
-      " [3.50046106]]\n"
+      "[[3.68184997]\n",
+      " [3.32507975]]\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "
    "
       ]
      },
      "metadata": {
       "filenames": {
-       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/exercisesweek41_22_2.png"
+       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/exercisesweek41_22_1.png"
       }
      },
      "output_type": "display_data"
@@ -744,8 +738,8 @@
      "output_type": "stream",
      "text": [
       "theta from own sdg\n",
-      "[[3.38135654]\n",
-      " [3.49216685]]\n"
+      "[[3.68809785]\n",
+      " [3.32032017]]\n"
      ]
     }
    ],
@@ -849,15 +843,15 @@
      "output_type": "stream",
      "text": [
       "Own inversion\n",
-      "[[4.12427537]\n",
-      " [2.85355539]]\n",
-      "Eigenvalues of Hessian Matrix:[0.33486875 3.91080327]\n",
+      "[[3.55555773]\n",
+      " [3.41891092]]\n",
+      "Eigenvalues of Hessian Matrix:[0.30326262 4.34133193]\n",
       "theta from own gd\n",
-      "[[4.12417157]\n",
-      " [2.85365229]]\n",
+      "[[3.55511609]\n",
+      " [3.41928689]]\n",
       "theta from own sdg with momentum\n",
-      "[[4.25050227]\n",
-      " [2.8249367 ]]\n"
+      "[[3.58207928]\n",
+      " [3.37895549]]\n"
      ]
     }
    ],
@@ -981,9 +975,9 @@
      "output_type": "stream",
      "text": [
       "theta from own AdaGrad\n",
-      "[[1.99999773]\n",
-      " [3.0000164 ]\n",
-      " [3.99998703]]\n"
+      "[[2.00039962]\n",
+      " [2.99772199]\n",
+      " [4.00233436]]\n"
      ]
     }
    ],
@@ -1098,9 +1092,9 @@
      "output_type": "stream",
      "text": [
       "theta from own RMSprop\n",
-      "[[2.01023308]\n",
-      " [2.95235306]\n",
-      " [4.04597076]]\n"
+      "[[1.99858411]\n",
+      " [2.9981377 ]\n",
+      " [3.99861427]]\n"
      ]
     }
    ],
@@ -1211,9 +1205,9 @@
      "output_type": "stream",
      "text": [
       "theta from own ADAM\n",
-      "[[1.99998193]\n",
-      " [3.0000747 ]\n",
-      " [3.99992263]]\n"
+      "[[2.00002678]\n",
+      " [2.99985103]\n",
+      " [4.00014662]]\n"
      ]
     }
    ],
@@ -1353,7 +1347,7 @@
     {
      "data": {
       "text/plain": [
-       "[]"
+       "[]"
       ]
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      "execution_count": 11,
@@ -1442,7 +1436,7 @@
     {
      "data": {
       "text/plain": [
-       ""
+       ""
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      "execution_count": 12,
diff --git a/doc/LectureNotes/_build/jupyter_execute/exercisesweek41_16_2.png b/doc/LectureNotes/_build/jupyter_execute/exercisesweek41_16_2.png
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diff --git a/doc/LectureNotes/_build/jupyter_execute/exercisesweek41_22_1.png b/doc/LectureNotes/_build/jupyter_execute/exercisesweek41_22_1.png
index ec5572cbb..6e7ac98cb 100644
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diff --git a/doc/LectureNotes/_build/jupyter_execute/exercisesweek41_5_1.png b/doc/LectureNotes/_build/jupyter_execute/exercisesweek41_5_1.png
index 9a45572de..dc0c79e9e 100644
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diff --git a/doc/LectureNotes/_build/jupyter_execute/exercisesweek43.ipynb b/doc/LectureNotes/_build/jupyter_execute/exercisesweek43.ipynb
index f0fcf3cea..1157076ae 100644
--- a/doc/LectureNotes/_build/jupyter_execute/exercisesweek43.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/exercisesweek43.ipynb
@@ -506,7 +506,13 @@
       "Learning rate  =  0.0001\n",
       "Lambda =  0.0001\n",
       "Accuracy score on data set:  0.5\n",
-      "\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "Learning rate  =  0.0001\n",
       "Lambda =  0.001\n",
       "Accuracy score on data set:  0.5\n",
@@ -704,7 +710,7 @@
      },
      "metadata": {
       "filenames": {
-       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/exercisesweek43_26_2.png"
+       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/exercisesweek43_26_3.png"
       }
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      "output_type": "display_data"
diff --git a/doc/LectureNotes/_build/jupyter_execute/exercisesweek43_26_3.png b/doc/LectureNotes/_build/jupyter_execute/exercisesweek43_26_3.png
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diff --git a/doc/LectureNotes/_build/jupyter_execute/linalg.ipynb b/doc/LectureNotes/_build/jupyter_execute/linalg.ipynb
index 065efc24a..bcf40d45a 100644
--- a/doc/LectureNotes/_build/jupyter_execute/linalg.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/linalg.ipynb
@@ -225,8 +225,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[-1.0526992  -0.18065292  0.78521833  0.80074264  0.59327016 -1.16492688\n",
-      "  0.85276246 -0.04581197 -0.65825344 -0.87931006]\n"
+      "[-8.18243276e-01 -1.33525471e+00 -8.98409646e-01 -7.24434901e-01\n",
+      "  2.27832584e+00 -8.78192446e-01 -1.35539164e-03 -7.36097055e-01\n",
+      " -1.06125720e+00  2.86376300e+00]\n"
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@@ -662,26 +663,36 @@
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-      "  0.61480907 0.29282684 0.37667238 0.91086026]]\n"
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+      "  1.90602444e-01 4.79877535e-01 6.10615323e-01 7.78031111e-02\n",
+      "  2.39856058e-01 3.20554718e-01]\n",
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+      "  5.54378734e-02 8.68351374e-01]]\n"
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@@ -800,13 +811,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.016972818397989375\n",
-      "3.9050595316983907\n",
-      "0.11007935789924998\n",
-      "[[ 1.21860973  3.69519297  6.31082439]\n",
-      " [ 3.69519297 12.18993003 19.00431775]\n",
-      " [ 6.31082439 19.00431775 65.3283771 ]]\n",
-      "[72.14421971  0.08339896  6.5092982 ]\n"
+      "0.07291818479810824\n",
+      "4.313183599076104\n",
+      "-0.05687021620384533\n",
+      "[[ 0.87810129  2.59703606  2.52470105]\n",
+      " [ 2.59703606  8.59883217  6.83603568]\n",
+      " [ 2.52470105  6.83603568 13.87354403]]\n",
+      "[19.25091007  0.06413187  4.03543554]\n"
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diff --git a/doc/LectureNotes/_build/jupyter_execute/project1.ipynb b/doc/LectureNotes/_build/jupyter_execute/project1.ipynb
index 5f4b5d2b2..b150340ef 100644
--- a/doc/LectureNotes/_build/jupyter_execute/project1.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/project1.ipynb
@@ -143,7 +143,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31707/39730396.py:11: MatplotlibDeprecationWarning: Calling gca() with keyword arguments was deprecated in Matplotlib 3.4. Starting two minor releases later, gca() will take no keyword arguments. The gca() function should only be used to get the current axes, or if no axes exist, create new axes with default keyword arguments. To create a new axes with non-default arguments, use plt.axes() or plt.subplot().\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11057/39730396.py:11: MatplotlibDeprecationWarning: Calling gca() with keyword arguments was deprecated in Matplotlib 3.4. Starting two minor releases later, gca() will take no keyword arguments. The gca() function should only be used to get the current axes, or if no axes exist, create new axes with default keyword arguments. To create a new axes with non-default arguments, use plt.axes() or plt.subplot().\n",
       "  ax = fig.gca(projection='3d')\n"
      ]
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diff --git a/doc/LectureNotes/_build/jupyter_execute/statistics.ipynb b/doc/LectureNotes/_build/jupyter_execute/statistics.ipynb
index 8d2bf7f69..4bfac63d1 100644
--- a/doc/LectureNotes/_build/jupyter_execute/statistics.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/statistics.ipynb
@@ -1344,27 +1344,27 @@
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@@ -2001,15 +2001,15 @@
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-      " [ 4.03821197 12.83140314 24.97606135]]\n",
-      "[33.84671508  0.08066381  4.58427764]\n"
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+      " [ 2.57219055  7.80289543 10.44900113]]\n",
+      "[19.14871402  0.07942491  2.70252786]\n"
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@@ -2638,7 +2638,7 @@
    "outputs": [
     {
      "data": {
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\n",
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\n",
       "text/plain": [
        "
    "
       ]
@@ -2764,12 +2764,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.01229732982000352 0.93003138502386\n"
+      "-0.024318244280276506 1.0399587275832265\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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aZM2aNa4xfnp6eqSpqUk6Oztd0y5cuFCGhoYK7+oOAACgqbFNrYdDYGxsTOrr62V06VKpu+66Ss8OAAAIIH38Hh3NNGEpMa7tBQAAqkr4w084C7YAAECRwh9+AAAANIQfAABQVcIffqj2AgAAmvCHHwAAAE34ww8lPwAAQBP+8AMAAKAh/AAAgKoS/vBDtRcAANCEP/wAAABowh9+KPkBAACa8IcfAAAADeEHAABUlfCHH6q9AACAJvzhBwAAQBP+8EPJDwAA0IQ//AAAAGgIPwAAoKqEP/xQ7QUAADThDz8AAACa8IcfSn4AAIAm/OEHAABAQ/gBAABVJfzhh2ovAACgCX/4AQAA0IQ//FDyAwAANOEPPwAAABrCDwAAqCrhDz9UewEAAE34ww8AAIAm/OGHkh8AAKAJf/gBAADQEH4AAEBVIfwAAICqQvgBAABVJfzhhwbPAABAE/7wAwAAoCH8AACAqlLy8LN27dpSv+XEUO0FAAA0Ewo/119/vYiIXH755bJo0SJ59NFHZcGCBXLbbbeVZOYAAABKbULhZ+nSpSIi0tTUJN/97ndlaGhIWltbZWhoqCQzVxKU/AAAAE1R4eeZZ55x3W9ubpZXX31VLrvsMnnkkUfkhz/8YSnmDQAAoORmFfOio446SlavXi01NTXS2Ngo9fX1cuKJJ5Z63gAAAEquqPAjInLSSSfJ6OioXHvttfLqq6/KokWLJBaLyYEHHljC2SsBqr0AAICmqPDz5z//WXp7e6WlpUW6u7ulvr5eRkdHJZFIyK233iof+chH5MILLyz1vAIAAExYUeGnra1NVq5cKUcddVT6sfr6ejnzzDPlzDPPLNW8AQAAlFxRDZ43bdrkCj5TGtVeAABAU1T46erqYiwfAAAwLRUVfpYsWSIHHXSQPProo6Wen9Kj5AcAAGgm1NsLAABguuHCpgAAoKqEP/xQ7QUAADThDz8AAACa8IcfSn4AAIAm/OEHAABAU5HwY1mWLF++XOLxuCxfvlxSqVSg13V1dQWeFgAAwKQi4aetrU06OzultbVVWltbZcmSJXlfk0wmZfny5YV/GNVeAABAU/bwY1mW6340GpVEIhHoddFodLJmCwAAVImyh59EIiENDQ2uxxoaGiSZTPq+Jh6PS2tra3EfSMkPAADQFD3Cc7H82uyMjIz4Th+JRPK+7/j4uIyPj6fvj42NFTN7AAAg5KZMby+/UNTf3y/Nzc15X9/T0yP19fXpv/nz55d4DgEAQBiUPfxEIpGsUp6RkRFj6U4ikZCzzjor0Pt2d3fL6Oho+m/9+vXqCaq9AACApuzVXs3NzdLX15f1eGNjo3H6/v7+9P+WZUlPT48sWrRIYrGYa7ra2lqpra0t7cwCAIDQKXv48fbYsixLGhsb0yU/yWRSIpGIRKPRrOqujo4O6ejoKKzXFyU/AABAU5E2PwMDA9LV1SXxeFz6+vpkYGAg/VxPT4/E43HX9KlUKj3GT29vb86eYQAAALnU2HY4i0bGxsakvr5eRs89V+ruvLPSswMAAAJIH79HR6Wurm5SPmPK9PaaNOHMdgAAoEjhDz8AAACa8IcfSn4AAIAm/OEHAABAQ/gBAABVJfzhh2ovAACgCX/4AQAA0IQ//FDyAwAANOEPPwAAABrCDwAAqCrhDz9UewEAAE34ww8AAICG8AMAAKoK4QcAAFQVwg8AAKgq4Q8/NHgGAACa8IcfAAAATfjDDyU/AABAE/7wAwAAoCH8AACAqhL+8EO1FwAA0IQ//AAAAGgIPwAAoKqEP/xQ7QUAADThDz8AAACa8IcfSn4AAIAm/OEHAABAQ/gBAABVJfzhh2ovAACgCX/4AQAA0IQ//FDyAwAANOEPPwAAABrCDwAAqCrhDz9UewEAAE34ww8AAIAm/OGHkh8AAKAJf/gBAADQEH4AAEBVCX/4odoLAABowh9+AAAANOEPP5T8AAAATfjDDwAAgIbwAwAAqkr4ww/VXgAAQBP+8AMAAKAJf/ih5AcAAGjCH34AAAA0hB8AAFBVwh9+qPYCAACa8IcfAAAATfjDDyU/AABAE/7wAwAAoCH8AACAqhL+8EO1FwAA0IQ//AAAAGjCH34o+QEAAJpZlfhQy7IkHo9LNBoVy7Kkvb1dIpGIcdpkMimJREJERNasWSMrV670nRYAACCfioSftrY2GRoaEhEVhJYsWSIDAwPGaROJhHR2doqIyPLly+Wkk05KvxYAAKBQZa/2sizLdT8ajaZLdrySyaT09PSk77e2tkoymcx6j5yo9gIAAJqyh59EIiENDQ2uxxoaGiSZTGZNG4vFZOXKlen7qVQqPT0AAEAxyl7t5QQYr5GREePjra2t6f9XrVolzc3NxjY/4+PjMj4+nr4/NjY2ofkEAADhNGV6e/mFIv35eDzu2zaop6dH6uvr03/z58+fhLkEAADTXdnDTyQSySrlGRkZyduDq6urSwYHB32n6+7ultHR0fTf+vXrSzTHAAAgTMoefpqbm42PNzY2+r5m+fLl0tXVJdFoVFKplLGUqLa2Vurq6lx/IkKDZwAA4FL28BONRl33LcuSxsbGdImOtzdXPB6XWCyWDj79/f2M8wMAAIpWY9vlLxqxLEv6+vqkqalJ1qxZI93d3elA09bWJk1NTdLZ2SmWZcmCBQtcr41EIrJp06a8nzE2Nib19fUyeuKJUrd69WR8DQAAUGLp4/foaKYWp8QqEn7KgfADAMD0U47wM2V6ewEAAJRD+MNPOAu2AABAkcIffgAAADSEHwAAUFXCH36o9gIAAJrwhx8AAABN+MMPJT8AAEAT/vADAACgIfwAAICqEv7wQ7UXAADQhD/8AAAAaMIffij5AQAAmvCHHwAAAA3hBwAAVJXwhx+qvQAAgCb84QcAAEAT/vBDyQ8AANCEP/wAAABoCD8AAKCqhD/8UO0FAAA04Q8/AAAAmvCHH0p+AACAJvzhBwAAQEP4AQAAVSX84YdqLwAAoAl/+AEAANCEP/xQ8gMAADThDz8AAAAawg8AAKgq4Q8/VHsBAABN+MMPAACAJvzhh5IfAACgCX/4AQAA0BB+UBmvvy5y000i77xT6TkBAFSZWZWegUlHtdfUdPzxIq++KvL00yJ33FHpuQEAVBFKflAZr76qbh94oLLzAQCoOuEPP5T8TG2sHwBAmYU//IQZwQEAgIIRfqaLt98WGR/P3B8ZETngAJFLLqncPJVCsQFuZETk8ccJgACAghF+poN160T22Ufk8MMzj918s8hrr4n8679Wbr5KYefO4l53xBEin/mMSH9/SWcHABB+hJ/p4MEH1e3wcOaxbdsqMy9Txeuvq9v77qvsfExVeimhiMgTT4j88IeUlAGAVEP4mco7+z/+UVVbbdiQezrTd9i+fXLmqdwmun5mzizNfITJCy+I7LqryNe/nnnsb/5G5KtfFXnkEfNr3ntPlSauX1+eeQSACgp/+JnKFi5U1VZf+lLhr51KJT9DQyKf+pTI738/+Z+1das7MM0owSb8s5+J/PM/T82g/O67aju58srgr7nmGnX7gx9kP6eXHuquvFLk4otFPvnJwucRAKYZws9f/1q5g97776vbhx8WufZakb/8xTydPn/O/5Mdfp55RuRXvwo27cknq+DzqU8V/jljYyJ//nOwad9+W2TffUXOPTfzWClKfs44Q+Rb31INqB22rcYiKrZNUqnccYdIMiny//5f8NfkCoS1tebHH35Y3b75ZvDPAYBpKvzhJ1ewefxxkb32EunoKPx9X3hB5NOfFnnssaJnzeWKK0T23tv8nP4dnNATtNpr+3YV8Ap19NEip54q8uKL+acdGSn8/XUHHRRsugceENm0SeTuuzOPlbLa6+23M//ffrtINCpy6aXmaXfsKE9o9rbdCcK7TPT53GUX82t23bXwzwGAaSr84SeXK65QtytXFv7aM84Q+c1vRE44ofDXXnaZyHXXFf46kczBMGj4Ofdc1VPshReCTb9pk8jSpZn7L79c2PxNpoaG7McmWu2lB4PZszP/f+Mb6tbUm27HDpFjjxX5xCcm3vYqX4gK+v2SSZHvf1+9n/c1W7dm/vcLP3PmBPucQti2yFe+IvKd75T+vUvlt791h2kEM5Wq3YEiVPe1vUZHi3vPHTtE/vSn4l77yisi119f2Gv0qpf33xfZfffgB12nK3h3t8gvfpF72u3bVXd6veqjpqaweS2WbavP+slPVEnQccdlT2MqBTGFA9tWO2e/A73OLxi8957/a554QmTNGvX/W2+J7Ldf/s8x2bxZLe+jjvLvtaYvf2cZmSxcqG7r67OXyebNmf8nO/zYtsjf/70KqtdcI9LXpx4/7DA1LtX/+l+ZaW++WZWuvf66yLJlIg895B7OoRyOP17dfuITIh//eLDXPPaY2gecdNKkzdaUtmqVyHnnidxzj8iZZ1Z6boCihL/k5913/Z8rNvz09Ljv//SnqsQkiB07cj9vamOiH/SddkKFnnn94Q/5p7nlluLafJSi0fH27ar0YvHizAHJSz+IO0zVXhddJPKBD/g37vV7T6fkxxSYL71UpK1NPae3Dcq1feXzq1+p9k4/+5n/NHrYCbLO//CH7GWiB7maGpF33hG5+mqR55/PPJ6r2uvdd0X+7d+CVZ++8ooamuHOOzPbqohadnpj6iefVA2sTz1VZMkSNWbVBRfkf/9S0n+LQXu5bdmiSnubm9VyLIUnn1TbbCrlfvzll7Mfmwr+8R/V77W1tdJzAhQt/OHnrbf8n9PDz49/rNr+6DtEv6DyrW+57593nntH8NRTIv/1X+bX5gs/pgOcfhAppNpL/6wgDXcHBrIfC9KuJUi7m/Fx1Y4mkfB//n/+J3PfNL+m8GMKXn196oDtVF3lYirhuf12933bFrnxRpF4XAU0PfBM5AColzr50b+f6fuLiLz0Uub/mprcJT/bt6sSme98R5V2OPSSH++y7+hQweS88/LPr850QrBli7p1Lmyr07fzctB//34Nwb30dT82Vpr5OPZYkVtvFfk//yfz2IsvinzsYyKHHlqaz5hOpmKvS4RO+MPPO+/4HzT0ndf//t8iK1aI3H+/uv/UUyJ77BF8BOVHH1W377+vznCPOcbdgNaRbwefL/yYSn70ncUf/6jaMO3YUfjOfa+9sh8L0uB2VoDa0yuvFLnwQlUlYrJ1q3sex8ZU0HjjDfX9rr/efAX4XKVOfgFUp28bzjLVe1bNnu1e/lu2uIPnREp+9GXrF4r1x03b8dNPuw+QM2bkDz/JZPb76OHH+53uukvdPvywyNe+lrsdmB7oTA3hnUBkCu+F9qx77jn3b/jdd9VYRqtXB3u9Hs6CViPrYdlZf6+9psJkrhOtIPT5dkoXN2wQOe207EDuuPFG1cuyVEGs0jZuDMdlezDlhT/8iOQfRFDnnMkvWaL+L/RHqO8c99lH5Ec/cj9fqvCj76z1g8bhh4u0t6tqh1w796eeErntNndw2nPP7M/2C466ICU/Thup8XHzgWZ83P3d29tVO5b99hP5+c9VI3FnpOtcn60viyC90EzhRw9h++2XPY0+n872UkzDZz0o+IVM/XGn1ETnhHXdT37ivu8NP/X17ucvucT9mlwH0ltuEfnbvzU/t2mTet5hWv7OY6btvJDw85//qS5x4rR1EhG54QY1inVzc7D30Odv82b1WxgaEjnkEP+qSD0YOr/1k09W1Yjnnx98/k30/dQ++2T+/8Uv1ImDyaWXqmEmpvtlbhwrVoTjsj2Y8qon/IyPi1x1lapfF/EvWp07V92adsQ7d+Zvd+E9CH75y+77N9+c+/Vbt6od7KOPZs769YNeIpE9H6YDbzKZvXPXffKTKuDp1VCmZWI64HoFKfnR39sUAMfH3QcWvQpu7Vr/99VLOdavdx808lUximQHA5HMpTNE1PADeqB9773s8HPffSJ1daparBCmtlxe+uOmIHrAAe77N9/snm7nTvf8e8PPihXZB5p8odFUoimiGr8GDT9+JT/r15urEr3r0lnWr7ySeWzdOv95NtFPDs44Q2TBApHGRpH//m9138Qbfs48MzMcRNASJz/6PidIlah3vjZvVicJ+j4u3/7quedEfv3rwj7LxLbN45Tl6s34zjsqxOrfO2hbxpdeUr30bFutB+9npFIiy5e7q4S98xtk/4DQqo7w8+abqnj4mmtU/bqIf2NnZ6dsKs04+WSRD3/Y/3Ns23wQcwLGa69lqhD8bNsmsmiR6knyL/+iHtPfs7tbZHDQffAwHUh23dV98PHrveTsHLZvVw23vYot+fHujPQdnKmUY+tW/3nM1XBZDz/XXpu/Ua63xMtU8qMv761b3dOMjrp30O++K/IP/6BCYlub+7Nuu02123jiCfO86AfS999XPfO8VXV6+DQFUW/w9E6zbZtqTOvYvl1kt90y901jXG3caJ5fnemA5j2I5go/pt/Jyy+rMFdXJ/LFL2Y+46qr1Pe86CI1/6mUaoDtpX+vCy9UvehyNSb3zp+pHZKXvo3++c8i996buT9vXv7XBxXkpENn26rH3PXXZ/Zx556rSi5zhdkjjhA58UR3iDS55prM0CAmPT3qROGOOzKPbdmiStG+8AXzaz7/eVWKqFfpBS39O/RQkXPOUaV9u+3mvpSLiBqnrKtLbTt+n33wwYUvZ4RGdYSfDRtUWxidX/38e++paU0799Wrc/eGevdd8069pUXdBuldtm1bpnrn2mvVrfc9161zBx7nDEaf5113dZ9Bv/dedjWXSCa43HqreUfw0kvq4P673/nPs34AXrpU7Xj23ts9QrQeGIKU/Oi860731lsiTU1q3KQg7ZOcEi9nRGNv+Nm+3b1sveFnbMxc7eVoblalbps2qc95+WVVpXTMMapR/FNPiZx9tlqH+vbw4osq9B5zTCbspVLu93/44ez1l68adeNGd3jcvj1/qcKZZ6oRvp3pTYL0QjIddM86Sx2gTaU7+oHvzjszDaydy3Xceqsay+gLX3BvK84y0cPH7beLPPusyD/9k7p8jKl0I2gPTZ3+ud5loIcvx86dav7//d/N7+ctmXDClem3uH69ClumdWLbatvS3X23Ko3Jd8IlkhkH7N13s8NvKqVCxLXX+gdjJxjpPfZWr1bb3i9/ad6fOu2abrrJ/T1M/zvefFO9n+Oyy9StXqI+MpIZNf7pp83z+8ADKuzqPTdL6emnVTtSv1C5Y4caA8tvu3C8+qoqBd+5U83za68Fn4dSdSAIaQP06gk/+gH6f/5HnZGb3Huvajejdw0PuvLvvVfk29/2fz7I++gHJmfn6t0Rbt1qLvnRN/aZM7M3/iVL1JmwPh9O+DG1HRFRB5H77lPj7vgVFeslPzfcoA6cf/2r6sbs0L9DoSU/uaq9fvITtaPp7CyszYhzcPeGH++8eedrdNS97L0H8dWr1ffWGwUnEqpE5z/+Q4Wve+5RJRt62xq9q/Ujj6j7e+zh3qlfdZV6rS5f4ItG3fe3b89fmrdpU6Zbul9QChLkTeFn2zZ1wArSS8500H7oIVVVonO2c1N3/RdfVKURJ56Y/VwxBwc9/HhLGU3h55571Ho7/3zVdqepKRN4hoeze3M5J1em8HPeeSqY+l0L0O/kwW9sKH0/sGWLun/QQar6T99G9H1DvmWmv6deffz22/6lLE5pqmVlz5NuZETkQx9SpTa5/OY3mf/33Tf7eX2bdvYZzz6rxqDy641aqKYm1YP44INVSb3XffepXqn52okdcog6YVi8WHUWOeSQYJ//5JOqets5gS6Gbat92bx5atv94x8rf7mfEqqO8PPmm+7w88//7D+tqYrC2THk86UvZR+cvO+Tj6nO23ug2LrV/QN2Dsb6mezVV6sDrNfQkPsznOXyoQ/ln7e/+zv1g/rhD9WB3CmuDtLg2dRoW5er5CcoUzDza0PgzIO3zc/FF7uny1ftZTqIv/WW+4xP78LveP55d8mBfiDdvNlcBSmSvX3lOxh5w1GQ8COS2b78wo+3UbRp3eWqbvG7jp2Xd/5NI5U7232+ZfH1r6sgcMIJ6nsVM0qx/j29pSCmai+92u2001RQP+881SPzIx/Jnt4JDKZ9hXNQd7YN7z7JW43qeOst80FLD/Fbtqjw8de/qm3c2Wbfe08deB3e0q61a93DfNi26qDw0EOqZMOx777qckCm+RgdVaXLCxa4OzXoJx07d4rsv3/2a03039sTT4h87nOZdb11qwoljosuUu2eTjtNDVzqlNL7WbHCv+edn7POyn5Mr3nQS+69v01nvp11vnlzdsePLVtU+6ZEQgWqf/1XVbq8dWvuqsp8RkZU6f2WLWr5HH64OrkNieoIPxs2uC9dkOtg7dc9txSJN8hBZ9u27Ms4eBuYbt3q3rk5O7EgVRHeg5+zLIKEn0ceUTukr35V7fAuvFD9iIOEH31nbjpI5Sr5CcoUfvzec9kydSB0RsAWUcte3zE6jznVLiLZ1V5+JSD6COCmALFjh3u9OsX3ImpZBV2X3d35p/O+Jsh26PArWfKGPlNj41xth4I2Tva29zJVO6dS6i/f9uNc5f6xx1SD6VKHH79qL6+hIdWb0eSNN9RtkBMlfbuybfc60befa65Rv9H99xc58MBMVYu+bi3LHayd36i3Ks07CvZnP6tOhPT5OP109bjXmjXmRuGjo5mqrKGhzOOnnJJp2/W73wVvn+PdXz74YKYDxfXXu9u5rVun2j2ZTlC8NmxQr73wwmDTO/L9lp0Tn6VLRSIRVQr1gx+IHHmkefrPfc49Ivz996v2TS0tKnBecknwi0UXOt+dndmPPfVUptH/Cy8U3vGgQipyeQvLsiQej0s0GhXLsqS9vV0ikciEp/X1zDPu4n/TNaJySaXM3cAL8eKLwc+4d9vNHcK8P+ZUyj1Wi3PQD3LAfPtt90HCOXvUu3cvXOjeCeWyeXOw3l6mgRp1pSj5MS3f995TOxQ/TtsfEfPBcMsWd3snb8mP31ngs8/mnFXZudN/CIbNm4O1R7nttsJD+fbthTXyNA18KRIs/OSqGsu3fBz6KNR+fvvbwi9O/OabwXtUnXuu+j3+8pfu34632ksv+dmyRf2GCq1ac5ZZvnX00EPZlyTRfz+mZe+UKp1/virN0X+H3tJwZ/06Pcf8FDq20dBQ/tIVx9q1qg3Rl79c2OeYShXPPVft+267Lfj7vPii2rc6gU/fFn/2s8wwKKOj7h6Ufu3kduxQTQ+amtz79//4D1VS/L3vqfsXXJB//3vjjZkeifmWzUsvqcbhV1yRuRzJ/fer9kSf/KQKxLNmqVHxdUGOJxs2ZKrIX39dLat589QyKeVFpydBRcJPW1ubDP3/K9eyLFmyZIkM+OxkC5nWqL5erRS94Vuh4cc7sF0x7rpL9T5xzJ5tPthu2+beqW3dmvkxn3mm+qF897vu1zjzFqQdxo9+5L5Su7MD1HeEjz8uEoupLr/5vP9+6Up+JjpQmykwFDICs+lg6N0BjI0F2xbyXU5kxw7/nVaukp+1a1XPmksvDRYMvAotKfL2onE8/7yqBnUUGn6CVnstWpR/Gr92dtGoKtEw8bbd8rN1a6btUTLpX1Up4i5dvuSS4i6Y7AT4fOHHW7KyY0fuxtgmucLfwIDaPic6cKPXI4+oUopHHinsdUFOjJxr3/ltW5deGvzz3nxTXY9u7lwVmubNc1e5OiNy33KLqiq/+2512Y933jFfI+6cc9R8JRJqH/y1r2We0/8Xyd3Bw/Gb36jtd9as/Pv9jo5M9aTTbtPbA2+vvVQp6+67Zx7zOwFzPlfE/ftyrnH43nuqw8LixeW7NmQRyl7tZXl2RtFoVBI+jcwKmdbX0UerW70YsNBrUb3//sTDz+uvu3dou+1mbmS8bZu74ea6dZmSHb8LaDrzZhq510Q/mxsfV8vGmbelS9UPPehBcsuWwkt+TF2Kx8eDdbH2U1trDk/6OEv5SkmCVLt5S3785Os2vXmzf7f8LVv8l8X69aoH009/OvnjlOTa5i+7LHOgfv/9wsNPKfltf01N/q+55hr/YOR4/XX3xYDXrXOHAe93dnoLHndcccFHRLWx2Lmz8C7Y3/uee3vINzjr4GDuMcd+8APVVuW55wqbj3x+/WsVJvXgHESQ8OMss6DBOhenhGjz5kyJ2apVmedfekkdR5w2gmefrW4ff9z8W7j7bndj6lzLPui6d9qn5vud6fui//5vcxukv/xF5KMfde8j/QL0c8+p0vDt2/33hV/6kvrOuTpkPPmkmp8lS1SpVCqlQlp/f1l6mJW95CeRSEiDp+SloaFBksmkxGKxoqf15bRl0Xda+oF4jz3yVzFMpOTny19W9dZPPum+sOO2beYws22bO5w5O+hIxJ3Kddu3q/r0K68sfP4GBtzXwHKu+u0M9phP0B+qPp3pApZ6CVcxZswwz4vegD3fyL9BSonGxrJHSM7lb//W3fskiM2b85e6vfXW5Iaf/fbLX2Wzfr0qnl+82Ly9lOtaXX7h54MfzP06/WBm8uEPu5fxK6+4w5C3VGT9evU7zDUsRD6JhFqWJ5xQ/HuI5L+0y8knB3ufUoWf+vrMQbqYRrNBws+zz6pOHkEuaJzL73/vHh9owwZVJeTsS+rq/EupJ6vrvIkTsvIdv/TnjzzS/3e5YYPq7ffss+p44xd+nGPvD36Qe1mfe65qfP3QQ+o4s2GDup7g8LA6zujXshOZWM+0IpS95Cfls0BHDA2NC5l2fHxcxsbGXH8ikgk/eqJ1DnLHHqvGh8nnxReDD5nvdeCBmfdYsSLz+LZt7nlyujC+8447aDlnHfX1mWDidcMNqptmMbw7aqftTyHhJ1dJiBPegvRKCnLVcD9bt+YPYvlGsg0SfoKW/Iiog+8RRwSbVveXv+S/ynhDQ/ArkRfin/5J3dbW5l+eGzaoHdyOHaW7wnkudXXmx/1Kck3dnAvhDZeXX577IPz88xPrXeMYH1cHjKlgoqUohxyihgDRL59S6ImkbZuv6+f1pS9NPPiIqGul6TZsyLTP+8AHcgdTvxGlTfRmELn4DZ75xhsqnHrbHToN+x1+vQBN1q3LXNtQH6ft4IPVGGS6yy/PtFNy6K8RUSF8r71UA/+rrlLtlJYuzQ4+FTBlenv5BZ2g0/b09Eh9fX36b/78+eoJUy8mpxiwsdF9OQQ/l1ySGRemUPqI0Hq11N//vXtDdIr5WlvdOwfn7HLuXP/wU2jXy1ycz/A2pnR4f4j5SsV+9CMVFvKVUuSq6gnC2+bBsdde6gcYZADEXMvx3HPVbSHh5yMf8S+tyyXI2fZvf+s+QDrh12+9BeX0WHr//fw7SqdnUjl8/euq7YVpvB6/s3C/30up6W3eSnGpiFIqJnyX0m67qQOevi4KLU168EG1vecTpI1iMRYtyvQK3Xdfc082EdW+xem15tdWzvHoo8EuvDwy4n9dtzfeMLdj8rv2nsnRR2dvI6+8onpEOqOXf/Wrqr2T96LU3v3tjBmqOvPnP3c/nuv40NoabBDOSVD28BOJRLJKbkZGRow9uAqZtru7W0ZHR9N/652zYlO4cYq7d9kleAlHsfwuh7FyZWaja2hw/3D1BslOT6+5c4NdmX2iclV7XXmlGqtDl6/k57331A8pn699LVMSlq+6wo9e93366er2L39RZyz6mCPF+OpX1e3IyOSHnyChwjsO0EknqcH/JnoA2GsvdRsk/OQa7TyIffYxHyRef10NEqn/zuvr1fb/0Y9mHvuHf1C3fuMJFdNuIGhP0qVLC3/vSnjmGdVwd7L4XQPN4fxWCjkge0c9dn57fpzS9aB+9Su1H3v44UxTgdNOy55OD2zf+Y663Xdf1YD4pZdEjj/e/zOctqYmn/2sKj3aZRfV8NsZydxr3jzVLMNvQN433jBXee2zj/r9eDvGeL34ojohf/ZZ97Hl6afdpVtnnaUa8y9YkPv9nAE7P/tZtTz9uuo7li5V1WFnn61Gw545U1U5Bi3pm6Cyh59mn+qjxsbGCU1bW1srdXV1rj8RMY/66ihH+FmwIPvik52daqPefXe18eYastwp+ZkzpzxnsrlKfubMyV5ezzyTOwxs3VpY99LddjOPlxKEU7pkuiq0Pp5PMfT2WU4poPcMx+vDH84ffp54Ivsq7MUctLdtUw1tiw2OIiL/9/+qECWigo+p2uvggzNnvkEb2O+5p3lHPHOmeZuuq1MhZ4893I+JuKu4nG1UL1V0Qq/zeF9fsHl0mEbj9Vq61F1dXkzbK6dk2vGZz5inu/lmc/ucww93Dy7op6bG3aav1PKdtTs9yubM8a/KO/10d/XJ4Ye7nzc1IL73XpFvfUv9fvbeO/Dsiojqav/YY2q5XnON+r2ZBhXVx/dyOCfThxySe3iF1lb/TiN6YGlpyf79O5xt3tu+1elBtW6dOfh/4APq99PVpYYtMc3bmjXuUKX3/NOPR319mRPegw4yz6fDKVSYPVsNBbB2rWrj4/XMM2qcNX353nKL6njjXJfuuONyf1YJlD38RD3D7VuWJY2NjenSnGQyme7llW/aQHKVlpQj/MyZk32Q0A/ukUjuqgpn5zIVSn523TX78Z4e//FqRNT857tKuK62duIhb9687ADlfc9c82xiGucp33a4117+QW71ahVYjjmmsAbUfpydV9CxNbxjeoioAeCcbXHLFnP4mTkzUwXoNwq112mnifzN32Q/PmOGeV0786CHH2cZ6eHH+3u48Ub34G87dwbrVfSNb6jqtEcecXdX9+O0i5oIvavxF76gzny9IhE1ArG3HYWI+t3lK3VxXHBBsHGQ7r5bHZTy+ehH1VAOf/5z9snlypXux/TqZr3Dh+6++9y/r4MPzj8PZ5yhSmOOOSb7EiFe3qp602/Eu1+bM8c8EKV+cuE3MOzRR6uTHr8GvE6bGl1np/pMvZegM096r8W77soM2/Lkk+bBDPXvp3c1v+oq1WD/zjtVkw+dX8m4fuLuV/Jz5JEq6HhDa02NmtfFi9X22tKirjxw5JFqXvTtZNddg4/gXSIVafMzMDAgXV1dEo/Hpa+vzzVuT09Pj8Tj8UDTBlLp8DN3rtqJ6xthocW0IhMr+SmkW2mhJT/5bNpU2PVyZs2aePiZMyd7h+dtFLv33oWNQbHrrtln5/kOlHvtZS75OeEEdbB1einlCz9BqgzyBWNvg3g9WOicHZJtm9vS1NQU3vh/9mxz6Yhf+HF23voB0Qlr+nr0vta7znfuzJ7GtM4/9jEVRltagoWfYksmdXr18WGHmZfPxz+u5lf/zp/4hBpR9/OfV9UFetjzM2NG/uthvfqqGqfGdBX0pqbMPmvePDUOzSc+Ya7Sv/BC93ajlyhEImpkbRO9wXyhVcX5qvWCDGFRU6NGd//e91QIfvxxNb9OuxeH/jsyha5Pf1pV3Tj+/d/dy6mhwdxurbdXdfjQh+Zw1ntNTeYadf/4j+bre/m1Q9J7Qi5bprYD077iuutUKPLSw8+ee2aWje6ZZ/wbb++yi5rvyy9Xr8117csyq0j4iUaj0tvbK62trdLb2+sqyRkYGJBObQjtXNMGkutAOnt24QfzffYpbOjwOXPURqwf4PLVnZpMpOTntNP8dzpehZb8FOKnP81fBTZv3sRKuGpq1HfwrnfvWBg1Nf69KESyr/E2c6Z7xySSf3yjPfc078idIfsdftv0eeep9i+nnGJ+/ogj1FXOYzHzsPOOI4/M7qnh95l66PXrhBCkk4Bu9mz3WZ3TgPOaa3L/PvWDmlN8r4cX73biDSXz52e/v6lKUQ88+cLPrFmZaX7zG3UG7e1dY/LNb7rv6wfRN94wNwr1nkmLqIORUxJQU6OqjEztVUTcbWf8wq7D74TsvvvUwe7BB1V1yRNP5C9dnD07s268JQzHHmtexrnayOSjlxR94QuqyuSeezINdIPub88/X5UCtrRklvEZZ2QueTNjhvsEaN99VTu7oaFMlVBrq7tE44tfVMcLJyjlagg9d676jLPPVuv21lszz33sY5lBA+fNy/79GpqCiIgqzd11V3Xtr1zmzcuU6Oq8AbelRZ2MOdW+hTRpmGKmTG+vSVPqkp/Zs3MfNEXMOxL9NcWEn4mU/Lz3nv+ZgVeuru7FlPzoDjhAFcE7gzueckr2VY1vuy3Y2bcfZ5RXL9OZda716A0LItkH13zz6Rd+vOHBr0Fjfb0qWvcrofq7v1NdRoeGcl9+Zc6c7N5ukYiql//KV9QOzWmPoW9j3quni5i7tF99tf9ni6jl9JGPqHZXjz2mdup/+pPamedahvoB0dkJByn5efBB1d7h7LOD/WYKCT/6ge3441XbiSAlc96DiB4GX3vN3G7ONFKwqZTD1J7t2GPdFzbWD5bf/37+y1Y4Tj9dbT+HHqoap5racJgMDalt09vm6kMfUg1s169XQeFf/kU9fswxqnpPvyCw7uqrs0cldujL8uij1Xa7aJEKQN3d7muPFWPxYlUqsmpVdvui445TJx9r16q2O6YxzERU766f/CRYlenKlarBsqmEyKHvQ775TbW9L1uWfdmYT31KlcTp1w7M5Uc/Urdf+Yrqlee3j/zGN9S68vu+04EdUqOjo7aI2KPJpG2rQ2L23y232Pb4uP/zpr8DD7TtVCr3NHPnZv537L575rGdO7Nn+He/y36f730v8//FF9v2Aw8UNq/O37e/rT4zyLS/+IWaH9P0995r29ddV9w8iNj2H/6Q+b47d6q/q67KPL9qlXruuOOK/wx9meeb5phjcj/vvf/II+7Hnn8+92e88YZt69tfba1tNzWZN9hf/tK2TzrJth99NDN9Z6d67t57ze//0EPm9/JO95nP2Pb557sfO+MMv5+Obe+6a+5t27Ztu79f3b/zTtvescO2L7nEtq+80rYtK3s5dXX5f9YNN7invf/+zHPbt9v25Zfb9s9/nnls2bLMtFdemfl/1iz12V7vv59/e7nnnsz0r73mfu6LX3TfnzMn+zNeein/Z/z4x5n/lyxRrzvnHHX/gQds+623sl/z619nPuPee237rLNse2zMvBy9rz3lFPfzb7yRee5Xv8p+jd97BVFbq6Y99NBg0wdxyy3u+bjiCtvesMG2DztM7YN0+nf7/vfN7zdjRmHfaar7/e9te/Zs21682Hw8KdbOnbY9PKx+0xWUPn6Pjk7aZ1DyU2gpw+zZ2a/xlgiYztT1M2bT86YGoXpx7pw5wS5iqZ/t6a8N2r7FKdI2TT9zpvsCsYXSzz5ratSfvn6cqsHJvmyDo9CBIYOU/DhdoGtrVTsVfTn+9a/+o/9+7nOqbZTefdZpKH766eaePfl6Xzjmz8/utp6rkX2uy784l7Roa1P/n3OOmv7GG1U11kEHZZ+x5qoe1D8rlXKPJTJzpmooqZ/xf+MbqqTlppvcpToXX2xeHkF+3/o0+nvef7+qOtKr/0wlNH6fof9W9Gmc0o477lBtbU49VZUoeC8uqld7nXGGKnkI2h7GOyCkXu3lVLH5ndUXej2m3/1Ozd/PflbY63K56CL3dzjnHFXa8cIL2cMM6KUxfvuOcnQWKadjj1VDTdx+e2mvn1VTo7bbQi8BNQ2F/xvmCz/6hnPRRfnrxmfNcu/MDzvM3cBNRNWTz59v7qVRCD387NzpHgHZ1DBRxN1rZMEC1Z3zoovc09TWqvYiptb1+s7dW8U1Y4Z7h37AAbnbmniZ2pnoBxtnXZXqasAPP6yKg/3ovU/6+vI3PPYeeGbPVsXZ0aj6Hqeeqhou/uIXarCzXXZRVRcLF6pws9tuwdqUOJwupzU1qsrh97/PjDcikj2Egp8Pfzg7/OSqvgy64/ML1TNnuocByPWdc1VjmdTVqTYoF1/s/m17B2Azvb/DuQ6Taf70/53tMN8Vu03h7k9/Ug2T29rUWDL6Nu18z1mz3FXk3qpWZ8ylYni3Vb26ztkW/NqJOG2ynGEP8lm4UDUM1sdgKoX//E/VY2rDhtyNmvVl69eWLWzhR0SdXAW5riKMwh9+cu1Qvc99/OMqYJjGd3DoDR5FVONVbz14S4sag0HvZeXsyArp6aW343j7bfegUX5dUvX2RF/5igoAzo7wqqvUvL71lqobNl1bzG+8BxF1INHfP5FQB3t9UEY/s2ebe8noOyXn/yBdXYM4+eTcA33p4efQQzP13U7jVO+B0zv/s2apnlvDw6pNzQMPZHrWOD2iZs1S7UKckV+DWLJE3XrHCTn22OzuoSbXXec+kM6cWbqSn6Dt1fT3z1VaoX9WoTty/fdbSFC46y736Lr5wk8+pkC0++7q4NTfr/Yn+nfzC4MnnpgZ/PSOO4J9th+/S4GIZNqY3XmnahvjPXm78Ua1jArtWVtqRxyhfgNBGtjfdpsajO+cc8zPl2u0b0wb4Q8/uRK/sxNypjnhBLUz7uxUO60NG7IveTBrlvtsN2jvswcfVAfFBx8MPOuuHeZbb6nGfL/+debyHKYB2fSDgHenvGyZGp/DOZM1nbXr4cdbEjJzptqpL16sDu7OgdCvwa5u/nzz55lKfibS86OQkii9FMsZhv+tt1QPCZHsg583/OjLKpdCi6VvvVVte6aGtOedp7a5XA0Nly5VvXKcAPbFL2YPkZ/rd2EqLXvhBXWZl6Dbrx7McgUTfRkXWuKnbzumcYtM1q5Vt3o4mGj4MQ0s6Q18fmOv6Gpq1Hp69ln1G5sIUynmH/+oToachtT7768aBTsDyznmzlWlY/lKwaeSCy5Q1YJ+2zXhBx7hLzMLUvLzxhvqYON0R9xlF1VcLeLffuH229WB0jTmgkljo/uK0EHMnq2CwNq16qxGxN3VsrlZteTXd+T6QSffZRhMO2F9NFFvCYBzYCjmrNTvMh/6zspZHxdcoLrm/+EP/t2tHfvskxkF++yzVUlUUDU1qvfRyy9nApfefmDmTPcy9B7Q/L7TRM2Y4X+2+8EPqlLAIG1ZfvlL1W7ogx9UQe+3v82MnJqrlOWyy9wX6HzlFRV0b7wx8FdwbYe5RuDVt7FC2xnoPdgaGvJPf9llmfFI9HU50fAzZ466jMrYWOaEwFuyNtFrruWzdq3qYfX446ra3TSo4aGH5h8QMKwIP/AIf8lPrh2q84NoaPCvU/aeQTkHjS9/2X/48lKZNUu1KRkcVFcsNtl9d1Wl4QQGPdDku3qyfgBYt05VBeTaOebq0pvvTNWvfYqp2mvuXLUTNw37rrdVmT9fXcvKObgGGe7f69OfNo/kKpK97eyyS2Z7GB2tXH170Eb6tbWZUomaGvdQ97nmffZs91l/MUMzBC35mUjDSv3CikHChf6d9FI8vSOB/vsppCRqzz1VwHzmGRWmvScWJ52kSpbzXfCyWEcdpfYBd9yhqu4ncpmTMHKun2UaOwlVKfzhJ5cgBxFv3Xk5D3izZqkqjubm3Dvi2lpV533mme7H85X8fP/7quj7pptUkDD1flq8WJUGbd2a+7v/8IeqkatfiPQ7gJqqvRyf+5xqLKqPYquXiBx+uFo/zz2n2h/5Dfdf7JW2vcu8pkYNOrhpU+42FVOVvr3n244nUvUo4l6X5Qg/uTgHPKc0V8QdfrxjIB12mArZpusi5XPkkeaGv86JjPeac6Xm7UEJ5YorVLVYsfsChE74q71yCXIA8x4AJzP89PW5i6sn2oUxX/j52MfUYGO5BK3imjtXdUmOxdT3+NSn3AMr+g1Qliv81NRkNz7fb79MmyfnwLn33rl7pnzmM+oAt3ChyPPPB/o6ImIOnPkGuJzKCmlf8+MfqwPGJZcU91l6qeNkhZ+gVRlr1qjAql+LSf/+3vDz7LNq/nNdFBnTyy67ZJoOAFKNJT/6Ti9oDxG9e/Fkhp/29sKvUJxLvmqvybD//iqw6I2JW1tVzw2ToF2dr7tOVS2sWGF+bT677KKGoy9EmMe6yLcdz5+vxrgppvRDRLWFO+YYVRqXK0RMZBlfeqkaH+uWW3JPN2eO/0UoRbJLuWbNyp5np4G5t3QVwLQU4r27D32nn+uSADp94MDJrvYq1Rg3IuoAVil6kLnppmClWLmK65cuVQ199TZJhR44nUtp6EMG5OIMphb06tnTSTm24yeeyL4wpGm6YjU0qAH2vONYBfX22yIvvRTsd3LTTeq7ONd5AjCtVV+1l17EHfQ6VfqBYjqEn4ceUqPTfu1rE3+vYulhJ2hPl3xtFbwBqtBqwXPPVdeYCtrosbNTNYjWe8CFxVQZHO3zn1fVz6YRzifbXnsFL/2dMyecIRioUlNkDzjFFdP9tVhOt+2JOOUU/yuBl0uutjw62878X+iy1S8FEURNjfmCpX5mzqzMQbkcpkr4qa9X3cQncjFbACjQFNkDTrL/+i/3aL6FKueOOV8j5enigx8U+da31BlzqRuOvvSSyOrVmZGQUbig1wUrB8ZgAVBm1RF+9C7cixerhpx+4+aY6OFHL6mYDMccI/Lkk+ZByqYbvaG4H2e02UIcckjwwSXh9sADIsmkug4ZAFSp6gg/uo9/XHV7zXcRS12Q6/KUysCAaq+jX6A0zA48UJXMBb08ASbm1FMJPgCqXvWFn+3bg1+Py6EHnolcaTmI/fcvvvfKdGUaXBEAgElSfV3di2lTE2RsIOfSD34jHAMAgCmh+sJPMQP/6V2q/QYh7O8X+fa3VTdzAAAwZVVftVfQgQ39+IWfffYRufrqib03AACYdNVT8tPfr3p6+V3BO5/jj1cXQ6z0+DkAAGBCamx7svtuV8bY2JjU19fL6Oio1JXiCtzbt6v2QkFHKwYAAAUr+fHboPqqvYo1a9bUGRUXAAAUrXqqvQAAAITwAwAAqkxo63GcpkxjY2MVnhMAABCUc9yezCbJoQ0/GzduFBGR+fPnV3hOAABAoTZu3Cj1hVyKqgChDT8NDQ0iIrJu3bpJW3gIZmxsTObPny/r16+ftJb7CI71MXWwLqYO1sXUMTo6KgcccED6OD4ZQht+ZsxQzZnq6+vZkKeIuro61sUUwvqYOlgXUwfrYupwjuOT8t6T9s4AAABTEOEHAABUldCGn9raWrn66qultra20rNS9VgXUwvrY+pgXUwdrIupoxzrIrSXtwAAADAJbckPAACACeEHAABUFcIPAACoKqEc58eyLInH4xKNRsWyLGlvb5dIJFLp2QqtZDIpiURCRETWrFkjK1euTC/vXOuC9TS5urq6pLu7m3VRQYlEQizLkmg0KiIizc3NIsK6qATLsiSRSEhDQ4NYliWtra3p9cL6mFzJZFKWLFkiQ0NDrseLXe4lWSd2CMVisfT/w8PDdmtrawXnJvx6e3td/+vLP9e6YD1NnqGhIVtE7E2bNqUfY12U1+DgoN3e3m7btlqm0Wg0/Rzrovz0/ZRt2+l1Y9usj8k0MDCQ3h95FbvcS7FOQhd+hoeHXQvGtm07EolUaG7Cb2hoyLV8h4eHbRGxh4eHc64L1tPkGhgYsKPRaDr8sC7KT1/+tq2Ws3PLuig/73LVgynrY/J5w0+xy71U6yR0bX6cYk1dQ0ODJJPJCs1RuMViMVm5cmX6fiqVEhG1zHOtC9bT5InH49La2up6jHVRXpZlycjIiEQiEUkmk5JKpdJVLKyLymhoaJCFCxemq79aWlpEhPVRKcUu91Ktk9CFH+fg6zUyMlLeGaki+oF21apV0tzcLJFIJOe6YD1NjlQqZaz7Zl2UVzKZlIaGhnS7hBUrVkg8HhcR1kWlDAwMiIjIggULZGBgIL3fYn1URrHLvVTrJJQNnk38FhhKJ5VKSTwez2rUZpqumOeQX39/v7S3tweennUxOUZGRsSyrPSJQHt7u+yxxx5i5xhTlnUxuRKJhPT29oplWdLR0SEiIn19fb7Tsz4qo9jlXug6CV3JTyQSyUqATvEzJldXV5cMDg6ml3WudcF6Kr1EIiFnnXWW8TnWRXlFo9H0shWR9G0ymWRdVIBlWbJmzRppbm6W9vZ2GR4elv7+frEsi/VRIcUu91Ktk9CFH6crqVdjY2OZ56S6LF++XLq6uiQajUoqlZJUKpVzXbCeJkd/f7+sWLFCVqxYIZZlSU9PjySTSdZFmTnte0xYF+WXTCalqakpfT8ajUp3dzf7qQoqdrmXap2ErtrLu9OxLEsaGxtJ6pMoHo9LLBZLBx+n6sW7zPV1kes5FMe7U+jo6JCOjg7jgZh1Mbmi0ag0Njam22A5Y/3EYrGsaVkXky8Wi0lfX5+rfeLGjRtZH2Wmt0nMdawux7EjlBc2tSxL+vr6pKmpSdasWeMa6A2lZVmWLFiwwPVYJBKRTZs2pZ/3Wxesp8mRSqVkxYoV0tXVJe3t7dLR0SGxWIx1UWapVEq6urpk4cKFMjQ0lC4ZFeF3UQmJRCJd7SiiThZYH5MvkUjI4OCgLF++XDo7O6WpqSkdQotd7qVYJ6EMPwAAAH5C1+YHAAAgF8IPAACoKoQfAABQVQg/AACgqhB+AABAVSH8AACAqkL4AQAAVYXwAwAAqgrhB8C0lEwmpaWlpdKzAWAaYoRnANOWfq0gAAiK8AMAAKoK1V4App14PC4dHR2SSqUqPSsApiHCD4BpJR6PS2trq4yMjIhlWZWeHQDTENVeAKadVColCxculOHh4UrPCoBpiJIfANNOf3+/tLa2Vno2AExTlPwAmHYWLlwoq1evlv7+fmlvb6/07ACYZij5ATDtNDc3SyKRkMbGxkrPCoBpiJIfAABQVSj5AQAAVYXwAwAAqgrhBwAAVBXCDwAAqCqEHwAAUFUIPwAAoKoQfgAAQFUh/AAAgKpC+AEAAFWF8AMAAKoK4QcAAFSV/w/ZfgWH+2loygAAAABJRU5ErkJggg==\n",
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diff --git a/doc/LectureNotes/_build/jupyter_execute/statistics_188_1.png b/doc/LectureNotes/_build/jupyter_execute/statistics_188_1.png
index 08cbabfcd..ac8fce949 100644
Binary files a/doc/LectureNotes/_build/jupyter_execute/statistics_188_1.png and b/doc/LectureNotes/_build/jupyter_execute/statistics_188_1.png differ
diff --git a/doc/LectureNotes/_build/jupyter_execute/week34.ipynb b/doc/LectureNotes/_build/jupyter_execute/week34.ipynb
index 2ab49b241..fc53b3d05 100644
--- a/doc/LectureNotes/_build/jupyter_execute/week34.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/week34.ipynb
@@ -985,8 +985,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[ 0.27919014 -0.36550376 -0.86282204  1.31714002 -1.99484719 -0.63837812\n",
-      "  1.45062284 -0.33079132 -0.58182803 -0.7207467 ]\n"
+      "[ 0.98502634 -1.56822376 -0.45668633  1.26063304 -1.43801947 -0.72264336\n",
+      "  0.12428533 -0.64472123 -0.99439119  0.84257054]\n"
      ]
     }
    ],
@@ -1424,26 +1424,26 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[0.23516186 0.40009482 0.40666305 0.26318493 0.37335014 0.7865355\n",
-      "  0.11587186 0.81840893 0.38046294 0.17138811]\n",
-      " [0.70506522 0.56475572 0.02507163 0.57935482 0.33537181 0.32382849\n",
-      "  0.37266855 0.00348543 0.11400145 0.84991754]\n",
-      " [0.27152452 0.88901776 0.95166414 0.82292185 0.09524714 0.92630576\n",
-      "  0.29374695 0.55867377 0.10959669 0.71647328]\n",
-      " [0.15913825 0.89604286 0.80447153 0.59412285 0.30990916 0.94642209\n",
-      "  0.20494446 0.7215423  0.03049638 0.55649207]\n",
-      " [0.70899024 0.80389541 0.60883945 0.7803213  0.43466245 0.33078483\n",
-      "  0.86852099 0.19772911 0.93022647 0.45253585]\n",
-      " [0.5810785  0.94823368 0.73379189 0.09408163 0.14549142 0.8353591\n",
-      "  0.46754435 0.29732036 0.7698352  0.39200159]\n",
-      " [0.77265782 0.37376184 0.43647835 0.38782352 0.97449977 0.02276062\n",
-      "  0.36802977 0.43941514 0.99006712 0.98316168]\n",
-      " [0.89897156 0.04956816 0.52067151 0.52180619 0.21275991 0.86420934\n",
-      "  0.50653545 0.49057373 0.77067609 0.16043757]\n",
-      " [0.22616902 0.70408916 0.43902948 0.68992377 0.5608253  0.84132082\n",
-      "  0.95661705 0.7082333  0.33600213 0.44116407]\n",
-      " [0.96459246 0.32141575 0.95679388 0.44595818 0.1875353  0.47700752\n",
-      "  0.03321947 0.55865092 0.96543101 0.63227278]]\n"
+      "[[0.36681298 0.62199022 0.32023229 0.08231145 0.09917246 0.29025302\n",
+      "  0.85115237 0.79516409 0.833774   0.85910255]\n",
+      " [0.08990571 0.56425249 0.34440086 0.37540613 0.30085673 0.93901621\n",
+      "  0.00790262 0.92604308 0.7742213  0.58486384]\n",
+      " [0.57811941 0.84268865 0.11339075 0.57329374 0.78094722 0.46156624\n",
+      "  0.2545724  0.1095957  0.6559956  0.22291364]\n",
+      " [0.12049203 0.50172141 0.38493367 0.62633359 0.13880371 0.37092452\n",
+      "  0.21913628 0.78478186 0.12625715 0.89142357]\n",
+      " [0.69018454 0.02200532 0.63281889 0.28622606 0.84900747 0.44440345\n",
+      "  0.5302517  0.15957051 0.10154612 0.7025846 ]\n",
+      " [0.26518597 0.48577692 0.68736603 0.10401756 0.88473534 0.31949465\n",
+      "  0.00434364 0.20240089 0.46285399 0.64432571]\n",
+      " [0.39229856 0.66289428 0.2591811  0.68871199 0.37021881 0.32041353\n",
+      "  0.93049763 0.30690504 0.63212587 0.56397327]\n",
+      " [0.31212802 0.82402448 0.94610136 0.19407473 0.28535441 0.99933329\n",
+      "  0.55331574 0.96439516 0.4809676  0.21947455]\n",
+      " [0.66489687 0.7613959  0.33285905 0.20726939 0.74587018 0.97375628\n",
+      "  0.62642303 0.95762994 0.22169909 0.82717721]\n",
+      " [0.8470287  0.16761991 0.74042291 0.42143986 0.70262543 0.8964059\n",
+      "  0.69629255 0.05916189 0.89940861 0.19010909]]\n"
      ]
     }
    ],
@@ -1557,13 +1557,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "-0.022210866177877393\n",
-      "3.7588118737641243\n",
-      "0.5615739502773949\n",
-      "[[ 1.30150056  3.81953844  7.0377961 ]\n",
-      " [ 3.81953844 12.2361161  19.72546953]\n",
-      " [ 7.0377961  19.72546953 73.31248389]]\n",
-      "[79.90960269  0.08394792  6.85654993]\n"
+      "-0.1804736801658276\n",
+      "3.577421319924605\n",
+      "-0.13894606338836166\n",
+      "[[ 0.93900613  3.09173024  2.71615146]\n",
+      " [ 3.09173024 11.22689573  9.13261905]\n",
+      " [ 2.71615146  9.13261905 12.06931309]]\n",
+      "[21.60398689  0.07438088  2.55684718]\n"
      ]
     }
    ],
@@ -1916,7 +1916,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31718/1326197715.py:6: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11068/1326197715.py:6: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
       "  data_pandas=data_pandas.append(pd.DataFrame(new_hobbit, index=['Pippin']))\n"
      ]
     },
diff --git a/doc/LectureNotes/_build/jupyter_execute/week35.ipynb b/doc/LectureNotes/_build/jupyter_execute/week35.ipynb
index c5399826b..0cb2574ec 100644
--- a/doc/LectureNotes/_build/jupyter_execute/week35.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/week35.ipynb
@@ -1519,7 +1519,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.995840825550726\n"
+      "0.9955273625597437\n"
      ]
     }
    ],
@@ -1550,7 +1550,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.007607459165915922\n"
+      "0.008900933315885705\n"
      ]
     }
    ],
@@ -1585,23 +1585,23 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[0.00552246 0.00115669 0.02017377 0.01896127 0.01389847 0.01591021\n",
-      " 0.02816083 0.0100949  0.02757522 0.00182747 0.00584432 0.02081274\n",
-      " 0.01365363 0.0191717  0.01068907 0.02763182 0.02950229 0.02485679\n",
-      " 0.01456159 0.00644939 0.0297291  0.08272096 0.01335857 0.00825399\n",
-      " 0.04478101 0.05834444 0.04198166 0.034985   0.00562524 0.01524072\n",
-      " 0.01529708 0.02083512 0.01161357 0.01708691 0.02279888 0.02912421\n",
-      " 0.00076617 0.02329285 0.00626773 0.01054509 0.00460405 0.01476097\n",
-      " 0.0036718  0.00569405 0.07804489 0.03894873 0.02103178 0.00726135\n",
-      " 0.00353575 0.00857028 0.00923278 0.01616709 0.02881357 0.00550379\n",
-      " 0.02942218 0.00946636 0.03982972 0.0149713  0.04103307 0.05526765\n",
-      " 0.00463639 0.00254359 0.00915433 0.02588522 0.00090992 0.00739382\n",
-      " 0.02075115 0.024632   0.00115506 0.01963203 0.00086063 0.01580414\n",
-      " 0.01059601 0.03376827 0.02745507 0.02109939 0.05977068 0.04662395\n",
-      " 0.00283853 0.03903968 0.0001225  0.02385515 0.02089297 0.04214702\n",
-      " 0.01289962 0.00798188 0.04746791 0.04822955 0.02066371 0.01045774\n",
-      " 0.01164198 0.03633213 0.00183398 0.0105301  0.00880924 0.015244\n",
-      " 0.01596986 0.01176096 0.01448147 0.00610607]\n"
+      "[0.00643899 0.04246989 0.0607062  0.02997344 0.0011878  0.00123457\n",
+      " 0.00324986 0.03285652 0.01028728 0.01571866 0.03940381 0.0814985\n",
+      " 0.038844   0.01828593 0.03967758 0.00303693 0.02227466 0.02702328\n",
+      " 0.00026861 0.00883798 0.02876697 0.00472251 0.03141454 0.02911162\n",
+      " 0.02387339 0.00585113 0.00110716 0.00587564 0.00693821 0.00604105\n",
+      " 0.00804985 0.02058094 0.01151984 0.01782721 0.0286851  0.08874631\n",
+      " 0.01678538 0.0065912  0.03611471 0.02706508 0.00313125 0.04977093\n",
+      " 0.00415289 0.02760079 0.00518122 0.00628874 0.00739489 0.01619456\n",
+      " 0.00996972 0.02210753 0.02030107 0.02100763 0.04699527 0.01512934\n",
+      " 0.00717079 0.01784714 0.01095703 0.01281486 0.0231703  0.04482932\n",
+      " 0.00287871 0.0565419  0.04028659 0.03102525 0.01617722 0.0271761\n",
+      " 0.01736502 0.03394827 0.00328494 0.05750876 0.0059888  0.01915888\n",
+      " 0.01423609 0.01024227 0.03660869 0.01012951 0.00534938 0.03375068\n",
+      " 0.02699539 0.04083439 0.04965227 0.00565625 0.02250553 0.00893027\n",
+      " 0.02244755 0.00741987 0.00189101 0.02042476 0.02036545 0.06362348\n",
+      " 0.03145163 0.02987833 0.07393685 0.0033575  0.02791218 0.00214832\n",
+      " 0.0111154  0.01344581 0.00368581 0.01436601]\n"
      ]
     }
    ],
@@ -1655,15 +1655,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[ 2.04899609 -0.34193915  5.64527549 -0.69997503  0.31290684]\n",
+      "[ 2.09851217 -1.48209629 10.27096183 -6.79998516  2.87206824]\n",
       "Training R2\n",
-      "0.9952222065466447\n",
+      "0.9957273060382023\n",
       "Training MSE\n",
-      "0.008897354602673473\n",
+      "0.010053880703541525\n",
       "Test R2\n",
-      "0.9915165982451293\n",
+      "0.9888005551376943\n",
       "Test MSE\n",
-      "0.009442796383765939\n"
+      "0.008043926731954223\n"
      ]
     }
    ],
diff --git a/doc/LectureNotes/_build/jupyter_execute/week37.ipynb b/doc/LectureNotes/_build/jupyter_execute/week37.ipynb
index 2b4851ed5..9b86b5406 100644
--- a/doc/LectureNotes/_build/jupyter_execute/week37.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/week37.ipynb
@@ -2023,7 +2023,7 @@
      "text": [
       "Bootstrap Statistics :\n",
       "original           bias      std. error\n",
-      " 100.115  15.1213        100.117        0.151517\n"
+      " 99.9889  15.1336        99.9892        0.150749\n"
      ]
     }
    ],
@@ -2089,7 +2089,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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YEZdsOopXujnuM346VYdNm0wnEfE/Km5EpFgffADRQZn0rrbRdBSv1KPqZuLsR7RSuIgBKm5EpFiLF8OgmK+0AngZBQUUcGPsCt59F/LVhCJupeJGRM6yYwds3w7XaCBxudwU+zmpqbBGzSjiVipuROQsixdDeDj0qbbBdBSvdkXkjyQkwEcfmU4i4l9U3IjIWRYtgquucl71I2UXYLMYPNhZLOqqKRH3UXEjIkUcOADr18OQIaaT+IYhQ2D3bti61XQSEf+h4kZEiliyBAIDYcAA00l8Q/fuEBnp7L0REfdQcSMiRSxa5PwPOUqLWrtEcDD076/iRsSdVNyISKFjx+CLL3RKytWGDIHNm2HvXtNJRPyDihsRKfTJJ5CXB4MHm07iW/r1A7tdV02JuIuKGxEptGgRtG0LtWubTuJbIiOhVy+dmhJxFxU3IgLA6dPOnhudkqoYgwfDypVw5IjpJCK+T8WNiADw+edw4gRcc43pJL7p6qudyzAsXWo6iYjvM1rcTJkyhcsvv5yIiAhiY2MZMmQIO3bsuODzVq5cSZs2bQgNDeWiiy5i5syZbkgr4tsWL4ZGjaBpU9NJfFNCArRrp1NTIu5gtLhZuXIlY8aMYd26dSQnJ5OXl0ffvn05efLkOZ+ze/du+vfvT5cuXdi8eTP/+Mc/uPfee1mwYIEbk4v4lvx85/w2Q4aAzWY6je8aMgQ+/RROnTKdRMS3BZl882XLlhW5/+abbxIbG8vGjRvp2rVrsc+ZOXMmderUYerUqQA0bdqUDRs28Nxzz3HttdeetX9OTg45OTmF97OysgBwOBw4HA4XHUnZnHl/0zm8mdqwfM6025o1+Rw6ZGfQoDwcjj+sExAcbCiZ93DY7UX+PHuH3z+bAwbApEl2Pv00jwEDtB7DGfo5Lj9/aMPSHJvNsjxnxZOff/6ZRo0asXXrVlq0aFHsPl27dqV169a89NJLhdsWLVrEDTfcQHZ2NvY//YJ59NFHeeyxx856nfnz5xMeHu7aAxDxUm++2ZxVq2oxe/anBGgkXoUaM6YnTZseYezYLaajiHiV7Oxshg8fTmZmJpGRkefd12jPzR9ZlsX48ePp3LnzOQsbgLS0NOLi4opsi4uLIy8vj/T0dOLj44s8NmnSJMaPH194Pysri9q1a9O3b98LNk5FczgcJCcn06dPn7OKMikZtWH5OBwOli9P5rvvLuK66ywGDuxfdIeBA80E8yIOu53kkSPpM2sW9uK+WSYmFrk7bFgAb71VmSuvTCAw0E0hPZx+jsvPH9rwzJmXkvCY4mbs2LF89913rFmz5oL72v40KOBM59OftwOEhIQQEhJy1na73e4xHwBPyuKt1IZlt2dPBLt3BzB0KNjtf/rfNlergpeU3eHAXlx7/elzee218PzzsGGDnc6d3RTOS+jnuPx8uQ1Lc1we0QF9zz33sGTJElasWEGtWrXOu2+NGjVIS0srsu3QoUMEBQURHR1dkTFFfNL69fFERFj06GE6iX9o1w7i4nTVlEhFMlrcWJbF2LFjWbhwIV988QX169e/4HM6dOhAcnJykW3Lly+nbdu2PlutilSk9evj6dfPopgOTqkAAQHOCf0WLwbPGfEo4luMFjdjxoxh7ty5zJ8/n4iICNLS0khLS+PUH66TnDRpErfeemvh/VGjRrFnzx7Gjx/P9u3beeONN5g9ezYTJkwwcQgiXm3PHti1qypXX11gOopfGTIEfvkFfvjBdBIR32S0uJkxYwaZmZl0796d+Pj4wtt7771XuE9qaip7/7CUbv369UlKSiIlJYVLL72UJ554gmnTphV7GbiInN+SJQEEBeVz1VXqQnCnnj2hcmWdmhKpKEYHFJfkKvQ5c+acta1bt25s2rSpAhKJ+JclS2y0bHmYyMgo01H8SkiIc6XwxYvh4YdNpxHxPR4xoFhE3C8jA1avttGuXarpKH5pyBDYsAH27TOdRMT3qLgR8VMff+wc0HrFFWkX3llcrn9/CApyLnshIq6l4kbETy1eDO3bW1SrlnPBfcX1qlaFHj007kakIqi4EfFDJ086F3AcPFgDiU0aPBhSUuDYMdNJRHyLihsRP7R8OZw+DYMG6RJwk66+GvLyICnJdBIR36LiRsQPJSZC06bQqJHpJP6tdm1o21anpkRcTcWNiJ8pKIClS2HAANNJBJxXTX3yibMnTURcw2MWzhQR99i0CQ4e1ILfbnOBRbsGn6jHIyfeJCUFrrrKPZFEfJ16bkT8zNKlUKUKdOxoOokANK/0K3XqOP9dRMQ1VNyI+JnERLjyStA6s57BZnOeIkxM1EKaIq6i4kbEj6SlOWfF1SkpzzJgAPz6K2zfbjqJiG9QcSPiRz75xNlToLEdnqVnTwgL06kpEVdRcSPiR5YuhXbtoHp100nkj8LCnAWOihsR11BxI+IncnOdk/fpEnDPNGAArFmj2YpFXEHFjYifWL0ajh/XeBtPNWAA5Oc7C1ARKR8VNyJ+YulSqFkTWrUynUSKU6cOtGihU1MirqDiRsRPJCZC//7OAcXimQYMcA76LtCSXyLlouJGxA/s3Om86ZSUZxswAA4fhm++MZ1ExLtp+QURX/SnKf+X7ruWENtd9HpuMLz4v0WMVqwwEEzOp0MHqFbt96vaRKRsVNyI+IHEjA50r7qFSoF/WJ2xRw8IDobRo51dOrm55gIKAEFBztmjly6Fxx83nUbEe+m0lIiPO54XxqrMlgyMXms6ipTAgAHOxU1TU00nEfFeKm5EfFzy0bY4LDsDoteZjiIlcNVVzkHfSUmmk4h4LxU3Ij5uaUZ7mob/Sv2wNNNRpARiYqB9e+fVbSJSNipuRHxYgWVjaUZ7BqrXxqsMGADJyZCTYzqJiHdScSPiwzYdb8RBR5ROSXmZgQPh5ElYtcp0EhHvpOJGxIctPdKeKoEn6Bi5zXQUKYWWLaFWLc1WLFJWKm5EfFhiRgeujPoGe0C+6ShSCjabczZpFTciZaPiRsRHpeVUY8PxJroE3EsNGAA//ww//WQ6iYj30SR+Ij7qkyPtsFHAVVFfm44iF/KnGaUBeuWHEmL7iKX9Z3Fx7Q+dGzWrtEiJqOdGxEclZnSgXeR2qgdnmo4iZVAp8DTdq25haUZ701FEvI6KGxEflFsQRPLRNjol5eUGRK9jVWZLjueFmY4i4lVU3Ij4oNWZLTmeX4kBUboE3JsNiF6Hw7KTfLSt6SgiXkXFjYgPWprRnprBh2lV+RfTUaQcLgpLpUn4Hp2aEiklFTciPigxoz0Dotdhs5lOIuU1IGodSUfaUWDpH1OkpFTciPiYnTth56nampXYRwyIXkdabjSbTzQyHUXEa6i4EfExS5dCiC2XXtU2mY4iLtC5ylYiA0+QqFNTIiWm4kbExyQlQfeqW6gUeNp0FHEBe0A+faM2aNyNSCmouBHxISdOwMqV0D96veko4kIDotbxzfGmHDxoOomId1BxI+JDvvgCcnOhvy4B9ylnitVlywwHEfESKm5EfMjSpdCoETQMP2A6irhQbPAxLo/YTlKS6SQi3qFMa0vNmTOHG264gfDwcFfnEZEysizneJvrrgO2mE4jrtY/aj1TF9Qmr9tgggIKzr2j1p8SKVvPzaRJk6hRowZ//etf+eqrr1ydSUTKYNs2+O036N/fdBKpCP2j15OZX5m1Wc1NRxHxeGUqbn777Tfmzp3L0aNH6dGjB02aNOE///kPaWlprs4nIiWUlATh4dC1q+kkUhHaRuyguv2orpoSKYEyFTeBgYFcffXVLFy4kH379nHXXXcxb9486tSpw9VXX81HH31EQcF5uk1FxOWSkqB3bwgJMZ1EKkKAzaJf1NckHWlnOoqIxyv3gOLY2Fg6depEhw4dCAgIYOvWrdx+++00aNCAlJQUF0QUkQs5dgy+/FKnpHxd/+j1bD3ZgH2nq5uOIuLRylzcHDx4kOeee47mzZvTvXt3srKySExMZPfu3Rw4cIChQ4dy2223uTKriJzD8uWQnw/9+plOIhWpb7VvCCCfT9R7I3JeZSpuBg0aRO3atZkzZw4jR45k//79vPPOO/Tu3RuAsLAw/v73v7Nv3z6XhhWR4iUlwSWXQJ06ppNIRapmP0HHKt+TlKHiRuR8ynQpeGxsLCtXrqRDhw7n3Cc+Pp7du3eXOZiIlExBAXzyCdxxh+kk4g4Dotbx7z23kFNgJyTAYTqOiEcqU89Nt27duOyyy87anpuby1tvvQWAzWajbt265UsnIhe0aRMcOqTxNv6if/R6ThaEsepYS9NRRDxWmYqbO+64g8zMzLO2Hz9+nDv09VHErZKSoEoVOE9HqviQSyrtombwYV01JXIeZSpuLMvCZrOdtf23336jSpUq5Q4lIiW3dCn07Qt2u+kk4g42m7P3RuNuRM6tVGNuWrdujc1mw2az0atXL4KCfn96fn4+u3fv5qqrrnJ5SBEp3qFD8M03MHq06STiTgOi1zErdSA/ZydoHTGRYpSquBkyZAgAW7Zs4corr6Ry5cqFjwUHB1OvXj2uvfZalwYUkXP79FPnmlL6TuFfelXdiN3mIOlIe+4NX2g6jojHKVVxM3nyZADq1avHjTfeSGhoaIWEEpGSSUqCtm0hLs50EnGnykGn6Vb1W5Iy2nFvLRU3In9WpjE3t912mwobEcPy8pw9N7pKyj/1j1pPyrFLOZmv38Uif1bi4iYqKor09HQAqlWrRlRU1DlvJbVq1SoGDRpEQkICNpuNxYsXn3f/lJSUwjE/f7z9+OOPJX5PEV+xfj0cParixl/1j15PjhXMiqOXmo4i4nFKfFrqxRdfJCIiovDvxV0tVVonT56kVatW3HHHHaUaq7Njxw4iIyML71evrnVWxP8sXQoxMc7TUuJ/Lg7bR4PQ/SQdac/AmHWm44h4lBIXN39cJ+r22293yZv369ePfmVYDCc2NpaqVau6JIOIt0pKcq4lFRhoOomYcOaS8I/SO2JZzvsi4lTi4iYrK6vEL/rHXpWK0Lp1a06fPk2zZs145JFH6NGjxzn3zcnJIScnp/D+meNwOBw4HGanLj/z/qZzeDN/bcP9++Hbb+1MmJCHw2GdvUNwcIlex/G/yXEcmiSnzEy2Yd+4jby8fyjfOhrRvPKe/wXyvp8Ff/05diV/aMPSHJvNsqxifjOeLSAg4IKnos5M7pefn1/iAIVBbDYWLVpUeLl5cXbs2MGqVato06YNOTk5vP3228ycOZOUlBS6du1a7HMeffRRHnvssbO2z58/n/Dw8FLnFPEEycl1mDHjUv7v/z4hIsJ3f5nJ+eXkBHDLLf246aYdXHPNz6bjiFSo7Oxshg8fTmZm5gU7UUpc3KxcubLEAbp161bifQuDlKC4Kc6gQYOw2WwsWbKk2MeL67mpXbs26enpFd7DdCEOh4Pk5GT69OmDXd+cy8Rf2/D66wM5fBhSUs7xRWLgwBK9jsNuJ3nkSPrMmoXdh7/xVSTTbThk02OczA8j+fKJzg2JiW7PUF7++nPsSv7QhllZWcTExJSouCnxaamyFCzu0L59e+bOnXvOx0NCQggJCTlru91u95gPgCdl8Vb+1Ia5ufD55zBpEtjt57jgMTe3VK9pdziwl/I5UpSpNhxYbS33/nwP2dl2qgSd9Op1OPzp57ii+HIblua4SlzcfPfdd7Ro0YKAgAC+++678+7bsqX7VqvdvHkz8fHxbns/EdNWr4YTJ3QJuDj1i1pPnnU/yUfacF3sKtNxRDxCiYubSy+9lLS0NGJjY7n00kux2WwUd0arNGNuTpw4wc8//36eePfu3WzZsoWoqCjq1KnDpEmT2L9/P2+99RYAU6dOpV69ejRv3pzc3Fzmzp3LggULWLBgQUkPQ8TrJSVBQgK0amU6iXiCemEHaRb+K0lH2qu4EfmfEhc3u3fvLpxPZvfu3S558w0bNhS50mn8+PGA87LzOXPmkJqayt69ewsfz83NZcKECezfv5+wsDCaN2/O0qVL6a+vsOJHkpKcvTa69FfO6B+9jrkH+1Bg2co27byIjylxcVO3bt1i/14e3bt3L7b354w5c+YUuT9x4kQmTpzokvcW8Ua7dsGPP8JTT5lOIp5kQNQ6nts3jC0nGnKZ6TAiHqBUC2f+0Y4dO3j55ZfZvn07NpuNJk2acM8999C4cWNX5hORP0hKco4X7dXLdBLxJJ2qbCMi8CRJGe1U3IhQxuLmww8/5KabbqJt27Z06NABgHXr1tGiRQvmz5/P9ddf79KQIuKUmAjdKm8gcvADpqOIB7EH5NO32gaWHmnPI6bDiHiAMhU3EydOZNKkSTz++ONFtk+ePJkHH3xQxY1IBThxAlasgGfqaB0hOVv/6PXcuWMC6enONcdE/FmZxp6lpaVx6623nrV9xIgRpKWllTuUiJzts8+c09cMjF5rOop4oH5R67EIYNky00lEzCtTcdO9e3dWr1591vY1a9bQpUuXcocSkbMlJkKTJtAg7IDpKOKB4kOO0DbiRz7+2HQSEfNKfFrqj8sbXH311Tz44INs3LiR9u3bA84xNx988EGx6ziJSPkUFMDSpTBiBLDBdBrxVIOi1/L8siY4HF49UbFIuZW4uCluzafp06czffr0ItvGjBnDqFGjyh1MRH63cSOkpcGgQai4kXMaGL2Wyb/ewerV0LOn6TQi5pT4tFRBQUGJbmVZEVxEzi8xEapWhY4dTScRT9a68k4SEtCpKfF7msxSxAskJkK/fhBU5pmpxB/YbM4F4T/+GM4zP6qIzyvzr8qTJ0+ycuVK9u7dS+6fVsK99957yx1MxC/9YTmSM/bnxLBp0wf8/dS/ocfnBkKJNxk0CF57DXbscA5AF/FHZSpuNm/eTP/+/cnOzubkyZNERUWRnp5OeHg4sbGxKm5EXCgpox2B5HNV1Nemo4gX6NkTQkN/v7pOxB+Vqbi5//77GTRoEDNmzKBq1aqsW7cOu93OiBEjuO+++1ydUcSvfZzRgU5VthFlP246iniB8AE96F3pST7+dyUmLB1X/E4rVrg1k4i7lWnMzZYtW/j73/9OYGAggYGB5OTkULt2bZ555hn+8Y9/uDqjiN86lR/MZ0fbaOI+KZWB0ev4MrMFRxwRpqOIGFGm4sZut2Oz2QCIi4tj7969AFSpUqXw7yJSfiuOteZUQaiKGymVgdFrySeQZUeuMB1FxIgyFTetW7dmwwbnZBs9evTgX//6F/PmzWPcuHFccsklLg0o4s8SMzpwUeh+moTrS4OUXM2QdFpX/onEjA6mo4gYUabi5qmnniI+Ph6AJ554gujoaP72t79x6NAhXnvtNZcGFPFXlgWJGe0ZGL2O/3WUipTYoOi1fHLkChwFgaajiLhdmQYUt23btvDv1atXJykpyWWBRMTpu5MN2JcTx6Dor0xHES80MHotj++5ja+yWtCt6rem44i4Vbkm8Tt06BCrV69mzZo1HD582FWZRARnr03lwGy6Vv3OdBTxQm0ifqJGcAYfp+vUlPifMhU3WVlZ3HLLLdSsWZNu3brRtWtXEhISGDFiBJmZma7OKOKXEjM6cGW1bwgOyDMdRbxQgM1iQNQ6jbsRv1Sm4ubOO+9k/fr1JCYmcuzYMTIzM0lMTGTDhg2MHDnS1RlF/M6h3Kqsz2qqq6SkXAbFrGXHqTrszK5pOoqIW5WpuFm6dClvvPEGV155JZGRkURERHDllVcya9Ysli5d6uqMIn4nKaMdAP2j1xtOIt6sd7WNhNhy1XsjfqdMxU10dDRVqlQ5a3uVKlWoVq1auUOJ+LvEjA60i9xObPAx01HEi1UKPE3Papv4WMWN+JkyFTePPPII48ePJzU1tXBbWloaDzzwAP/85z9dFk7EH+UWBPHp0ct1SkpcYmD0OlZntuSYo5LpKCJuU+JLwVu3bl04KzHAzp07qVu3LnXq1AFg7969hISEcPjwYe6++27XJxXxE6uOteREfjgDo9eZjiI+YGD0WsbsHMenR6/gxlitKSX+ocTFzZAhQyowhoickZjRgVohh2hZ6RfTUcQH1Al1fpYSM9qruBG/UeLiZvLkyRWZQ0Rwzkr8cUYHBkav1azE4jKDor9ixoGrySsIICigwHQckQpXrkn8Nm7cyNy5c5k3bx6bN292VSYRv/Vjdh12na7JII23ERcaGL2WI3lVWJfVzHQUEbco0/ILhw4dYtiwYaSkpFC1alUsyyIzM5MePXrw7rvvUr16dVfnFPELiRkdCAs4TY+q+rIgrnNF5I9Utx/l44yOdK66zXQckQpXpp6be+65h6ysLL7//nuOHDnC0aNH2bZtG1lZWdx7772uzijiNxIzOtC72kbCAnNNRxEfEmCzGBCt2YrFf5SpuFm2bBkzZsygadOmhduaNWvGf//7Xz755BOXhRPxJ0eOwJeZLXSVlFSIQdFr+SG7HrtOxZuOIlLhylTcFBQUYLfbz9put9spKNBgNZGyWLYM8glkgIobqQB9qm0gWLMVi58oU3HTs2dP7rvvPg4cOFC4bf/+/dx///306tXLZeFE/MnixdCm8g5qhqSbjiI+KCLoFN2rfqvZisUvlKm4eeWVVzh+/Dj16tWjQYMGNGzYkPr163P8+HFefvllV2cU8XmnT8Mnn8A11VebjiI+bGD0WlYea0VWlukkIhWrTFdL1a5dm02bNpGcnMyPP/6IZVk0a9aM3r17uzqfiF/47DM4cQKGNlVxIxVnUPRX3PvzvSxbBjfcYDqNSMUpdXGTl5dHaGgoW7ZsoU+fPvTp06cicon4lYULoXFjaFppr+ko4sPqhR2kdeWfWLToYhU34tNKfVoqKCiIunXrkp+fXxF5RPxOXh4sWQLXXGM6ifiDoTGrSUx0ngoV8VVlXhV80qRJHDlyxNV5RPzOmjWQkQFDh5pOIv5gaPXVnDgBn39uOolIxSnTmJtp06bx888/k5CQQN26dalUqVKRxzdt2uSScCL+YOFCqFUL2rY1nUT8QdPwPVx8sfNzN2CA6TQiFaNMxc2QIUOw2WxYluXqPCJ+xbKcl4APGYIWyhS3sNmcvYSzZsGrr0JQmf4XEPFspfpYZ2dn88ADD7B48WIcDge9evXi5ZdfJiYmpqLyifi0jRth3z6dkhL3GjoUnn4aVq+GHj1MpxFxvVKNuZk8eTJz5sxhwIAB3HTTTXz22Wf87W9/q6hsIj5v4UKIjoYuXUwnEX/Stq3zVOjChaaTiFSMUvXcLFy4kNmzZzNs2DAAbr75Zjp16kR+fj6BgYEVElDEZxTzFXnR13MYFPkDQX2eMRBI/NWZU1MLFsBLL0FAmS4tEfFcpfpI79u3jy5/+Ip5xRVXEBQUVGQZBhEpme0n6/Bjdl2GxmjiPnG/oUNh/3745hvTSURcr1TFTX5+PsHBwUW2BQUFkZeX59JQIv5gUXoXKgWcok+1DaajiB/q3BmqV4dFi0wnEXG9Up2WsiyL22+/nZCQkMJtp0+fZtSoUUUuB1+oE7kiF7QovTP9otcTGugwHUX8UGAgDB7sPDU1ZYqu1hPfUqri5rbbbjtr24gRI1wWRsRf7DtdnQ3HmzC+1gemo4gfGzoUXn8dvv8eWrQwnUbEdUpV3Lz55psVlUPEryxO74zd5qB/9HrTUcSP9ewJERHOq6ZU3Igv0Rh5EQMWpnehV7VNVAk6aTqK+LGQEBg4UJeEi+9RcSPiZum5kaw61lJXSYlHGDoUvv0WfvnFdBIR19HE2yJu9nFGRyxsXB3zleko4q/+MOfSVXmhhAYsZlHfN5hQ5/3f91mxwkAwEddQz42Imy1M70KnKtuICz5qOooIlYNOc2W1b1iYrmmyxXeouBFxo+N5YSQfaatTUuJRhlZfzdqsFhzIiTYdRcQlVNyIuNGyI1eQYwVzjYob8SADo9cSZMvjo/ROpqOIuISKGxE3WpjehUsr76Re2EHTUUQKRdmP06PqZp2aEp+h4kbETXIK7CzNaK9TUuKRhsasZsXR1hxxRJiOIlJuRoubVatWMWjQIBISErDZbCxevPiCz1m5ciVt2rQhNDSUiy66iJkzZ1Z8UBEX+OJoa47nV9IpKfFIg2O+pAAbH2d0NB1FpNyMFjcnT56kVatWvPLKKyXaf/fu3fTv358uXbqwefNm/vGPf3DvvfeyYMGCCk4qUn6L0rvQMOw3mlf61XQUkbPEhxyhQ+QPLDysU1Pi/YzOc9OvXz/69etX4v1nzpxJnTp1mDp1KgBNmzZlw4YNPPfcc1x77bUVlFKk/PLzYXF6J26v8akWKBSPNbT6Kh7edScn8kKpbDqMSDl41SR+a9eupW/fvkW2XXnllcyePRuHw4Hdbj/rOTk5OeTk5BTez8rKAsDhcOBwmF2N+cz7m87hzbylDdessXHYUY2r49fiCA42HaeQ438/M45ifnakZHypDQfGr2fCL6P5OKsT17nxZ8pbfo49mT+0YWmOzauKm7S0NOLi4opsi4uLIy8vj/T0dOLj4896zpQpU3jsscfO2r58+XLCw8MrLGtpJCcnm47g9Ty9DV9/vQVRUQkcfqgnSQE9Tcc5S/LIkaYjeD1facP6+48xo8rthCcluf29Pf3n2Bv4chtmZ2eXeF+vKm4AbH/q07csq9jtZ0yaNInx48cX3s/KyqJ27dr07duXyMjIigtaAg6Hg+TkZPr06VNsr5NcmDe0YV4e3H13ELdELGTgzNdMxynCYbeTPHIkfWbNwu7D3/gqkq+14eaAYzz/1fX0XNqf0FD3vKc3/Bx7On9owzNnXkrCq4qbGjVqkJaWVmTboUOHCAoKIjq6+Jk1Q0JCCAkJOWu73W73mA+AJ2XxVp7chikpcPAgjLhsOfbcXNNximV3ODw2m7fwlTa8PiqFx365jRUr4Oqr3fvenvxz7C18uQ1Lc1xeNc9Nhw4dzupyW758OW3btvXZf0zxfvPnQ6NG0CbiJ9NRRC6oeaVfaVFpF++8YzqJSNkZLW5OnDjBli1b2LJlC+C81HvLli3s3bsXcJ5SuvXWWwv3HzVqFHv27GH8+PFs376dN954g9mzZzNhwgQT8UUu6PRpWLgQhg9HV0mJ17g59jM++giOHzedRKRsjBY3GzZsoHXr1rRu3RqA8ePH07p1a/71r38BkJqaWljoANSvX5+kpCRSUlK49NJLeeKJJ5g2bZouAxePlZQEWVlw002mk4iU3E1xX3DqFHz0kekkImVjdMxN9+7dCwcEF2fOnDlnbevWrRubNm2qwFQirjN/Plx2GTRubDqJSMnVDT1I584wbx6MGGE6jUjpedWYGxFvkpkJiYnOU1Ii3ubmmyE5GQ4dMp1EpPRU3IhUkEWLIDcXbrzRdBKR0rvuOuc4sfffN51EpPRU3IhUkHfegW7doFYt00lESi8mBq66ynlqSsTbqLgRqQAHD8Jnn2kgsXi34cNh3TrYtct0EpHSUXEjUgHefx8CA0EX8ok3u/pqqFTJOTBexJuouBGpAPPnO7v0zzFxtohXqFQJrrnGeWrqPBe2ingcFTciLrZrl7MrX1dJiS8YPhx+/BH+N9eqiFfwqrWlRLzBu+86v/EOGmQ6iUg59OgBQO+CQKrbP2De1ctp3XBm0X1WrDAQTOTC1HMj4kKW5ezCHzzYWeCIeDt7QD43VE/hnUM9ybf0X4Z4B31SRVxo61b44QedkhLfcnPcZxzIrc6qYy1NRxEpERU3Ii40f75zEHHfvqaTiLhO+8gfqB96gHkHe5uOIlIiKm5EXKSgwDlx3/XXg91uOo2I69hsMDz2cz483I2cAn24xfOpuBFxkbVrYe9eTdwnvunmuM/IzK9MUkY701FELkjFjYiLzJ/vXGqhc2fTSURcr2mlvVxaeSfzD/UyHUXkglTciLiAw+GclfimmyBAP1Xio26O/YyP0zuSmadLAcWz6dewiAt89hmkp+sqKfFtw2K/INcKYuHhLqajiJyXJvETcYH5dyynafjFtBp3B9hMpxGpGLVC0+lW9VvmH+rFHfHLTMcROSf13IiUU3Y2LE7vzE2xX2BTYSM+7ubYz/jiaGtSc6JMRxE5JxU3IuWUmAgn8sO5KfZz01FEKty11VcRZMvnvUM9TEcROScVNyLl9MYb0D7yexqGHzAdRaTCVbOfoH/0euYd0oR+4rlU3IiUw+7dsHw53BWfaDqKiNsMj/2cDcebsGOH6SQixVNxI1IOr78OkZFwY6xWRxb/MTB6LdWCspg923QSkeKpuBEpI4fDeUpqxAgID8wxHUfEbcICc7mtxqe8+Sbk6KMvHkjFjUgZffwxpKXBXXeZTiLifnfFJ5KeDgsXmk4icjYVNyJl9Npr0L49tGxpOomI+zWttJdu3eDVV00nETmbihuRMigcSKxeG/Fjo0bBypWwfbvpJCJFqbgRKYPXX4eICLjhBtNJRMy55hqIiXH2Yop4EhU3IqV0ZiDxLbdAJa0fKH4sJAT+8heYMwdOnTKdRuR3Km5ESikx0TmQeORI00lEzBs5Eo4dgw8+MJ1E5HdaOFPkQnoUnWb+1W//Q7uIyrQaN8ZQIBHP0bAh9OkDM2fCrbeaTiPipOJGpBR2n6rB8qNtmd34WdNRRMz7X+F/9+GuXPf9Y3x3+V9pWXlX0X1WaIJLcT+dlhIphdmp/YkIzOaG2BTTUUQ8xtXRX1IjOINXDwwyHUUEUHEjUmKOgkBmp/VnRNxnVAo8bTqOiMewB+Tz1xpJvH2wDyfyQk3HEVFxI1JSiRkdSMuN5q4ELZIp8mcjE5ZyIj+Mdw/1NB1FRMWNSEm9ljqQdhE/0KryL6ajiHicuqEH6R+1npkHrjYdRUTFjUhJ/Hoqjk+PXK5eG5HzuDvhYzaeaMyGrItNRxE/p+JGpAReTx1ARGA2N8bqyg+Rc+kfvZ7aIQd5NVUDi8UsFTciF+AoCOSNtH4aSCxyAYG2Au6MT+Kdg73IzNP03WKOihuRC1ia0Z7U3BidkhIpgb/GJ3G6IJh5B3ubjiJ+TMWNyAW8mjpIA4lFSqhmSDqDYr5i5oFBWJbpNOKvVNyInMevv6KBxCKlNCrhY7aebMC6rGamo4ifUnEjch6vvooGEouUUp9qG6gfekAzFosxKm5EziEzE6ZPh7sSEjWQWKQUAmwWd8Un8t7hHhw9ajqN+CMVNyLn8OqrcPo03F/rQ9NRRLzOHfHLKLBszJxpOon4IxU3IsU4fRpefBFuuw0SQjJMxxHxOnHBR7mjxjJefBGys02nEX+j4kakGG+9BQcPwgMPmE4i4r0m1nmXjAx44w3TScTfqLgR+ZO8PHjmGbjuOmjUyHQaEe91UVgqN90Ezz4LDofpNOJPVNyI/MmCBfDLL/Dgg6aTiHi/hx6CvXth/nzTScSfqLgR+QPLgqefhj59oE0b02lEvF+LFjB4MEyZAvn5ptOIv1BxI/IHy5fDli3Ob5si4hqTJsGOHbB4sekk4i9U3Ij8wdNPw+WXQ48eppOI+I527aBnT3jqKbQkg7iFihuR/1m3DlJSnL02NpvpNCK+5R//gE2bnL2jIhVNxY3I//znP9C4MQwZYjqJiO/p2ROuuMLZeyNS0VTciADbtzvHAzz4IATop0LE5Ww2Z+/NqlXw5Zem04ivCzIdQMQT/Oc/ULMm3Hyz6SQiPuYPA9gGWTaah89mytVpJLb8x+/76FyVuJi+o4rf27sX5s2Dv/8dgoNNpxHxXQE2i4fqzGfpkQ5sOd7AdBzxYcaLm+nTp1O/fn1CQ0Np06YNq1evPue+KSkp2Gy2s24//vijGxOLr3nhBYiIgJEjTScR8X3DYr+gXmgqT+8dbjqK+DCjxc17773HuHHjePjhh9m8eTNdunShX79+7N2797zP27FjB6mpqYW3RpojX8ooPR1mzYJ77oHKlU2nEfF9QQEFPFj7Hd4/3J2fsmuZjiM+yuiYmxdeeIG//vWv3HnnnQBMnTqVTz/9lBkzZjBlypRzPi82NpaqVauW6D1ycnLIyckpvJ+VlQWAw+HAYXixkzPvbzqHNytvG770UgAQwKhReede+8aHz1U57PYif0rpqQ1L7+Y6X/DYntt4ev9wXm0+Vb8LXcAf2rA0x2azLDNTKuXm5hIeHs4HH3zANddcU7j9vvvuY8uWLaxcufKs56SkpNCjRw/q1avH6dOnadasGY888gg9zjPj2qOPPspjjz121vb58+cTHh7umoMRr3TqVCB33dWXbt32ceed20zHEfErixc3YO7cZsycmUxMzGnTccQLZGdnM3z4cDIzM4mMjDzvvsZ6btLT08nPzycuLq7I9ri4ONLS0op9Tnx8PK+99hpt2rQhJyeHt99+m169epGSkkLXrl2Lfc6kSZMYP3584f2srCxq165N3759L9g4Fc3hcJCcnEyfPn2w61tfmZSnDV98MYBTJyymZk6mzvTDFZTQsznsdpJHjqTPrFnYffgbX0VSG5ZNl7wwPuJtvnv8JFO2X6XfheXkD/+fnDnzUhLGLwW3/WkqWMuyztp2RuPGjWncuHHh/Q4dOrBv3z6ee+65cxY3ISEhhISEnLXdbrd7zAfAk7J4q9K2YUaGcyG/v9ZYQoPA/ZBbgeG8gN3hwJ7r541QTmrD0okil3tqLuTZfTfy4LFAQL8LXcGX27A0x2VsQHFMTAyBgYFn9dIcOnTorN6c82nfvj07d+50dTzxcY89BgUF8Hj9N01HEfFb99ZaSJAtn6eeMn7hrvgYY5+o4OBg2rRpQ3JycpHtycnJdOzYscSvs3nzZuLj410dT3zY9u0wfTo8/DDEBh8zHUfEb0Xbs/hn3beZOTOAvXsjTMcRH2L0tNT48eO55ZZbaNu2LR06dOC1115j7969jBo1CnCOl9m/fz9vvfUW4Lyaql69ejRv3pzc3Fzmzp3LggULWLBggcnDEC8zYQLUrQv33Qd8YjqNiH+7t9ZCXgu4m9mzW3D33abTiK8wWtzceOONZGRk8Pjjj5OamkqLFi1ISkqibt26AKSmphaZ8yY3N5cJEyawf/9+wsLCaN68OUuXLqV///6mDkE8WTFX0S0/0pak757lw+aTCblqlYFQIvJHIQEOnnkmn6FDY1m6NI8/XDwrUmbGBxSPHj2a0aNHF/vYnDlzityfOHEiEydOdEMq8UV5BQGM/3k0Xat8y9AYFTYinmLAAItWrQ4xcWJ1+veHYq4BESkVjeISvzErdSA/ZNflhYbTOccFeSJigM0Gf/3rNnbvhmnTTKcRX6DiRvzCMUcl/vXrHdwat5w2ET+ZjiMif1KnznFGjSrgiSfg4EHTacTbqbgRv/Dk3hFk54fw1EWvm44iIufwz38WYLc7r2QUKQ8VN+LzfjmVwEu/XctDdd4hISTDdBwROYeoKHj8cXjjDdi0yXQa8WYqbsTnTfzlbuKCj/L32u+bjiIiF3D33dCsmXOqBjMrH4ovUHEjPi3laCsWpnfl6YtmER6Yc+EniIhRQUEwdSqsWQPv6/uIlJGKG/FZ+VYA438ZzRUR27kp9nPTcUSkhHr3hsGD4YEHIDvbdBrxRipuxGe9ldaXzScuZmrDVwiwqX9bxJs895zzqqnnnjOdRLyRihvxSSdOwD9238mw2M/pUOUH03FEpJQaNoRx4+Dpp2HfPtNpxNuouBGf9NhjcCyvMk9fNMt0FBEpo4cfhshIePBB00nE26i4EZ+TkgLPPw+P1ptD3VDNBibirSIjYcoUeOcdWLnSdBrxJsbXlhJxpWPH4NZboWtXmIAutRDxCgMHwujRzj9zc4s8dJtl480qL3LLLa349luoVs1QRvEqKm7Ep4weDVlZ8NZbEHhbgek4IlJOATaLuU2fouU3sxnV+Bvebfb4udeGW7HCrdnEc+m0lPiM+fOd3dczZkCdOqbTiIir1Ak9xGuNn+f9wz2Yk3aV6TjiBVTciE/Yswf+9je4+Wa46SbTaUTE1W6ITeGOGp9wz8572Zld03Qc8XAqbsTr5efDX/4SSNWq8MorptOISEWZ1nAaCSHpDN/+CLkFGlUh56ZPh3inHj2cfwYHszjuRdashhWXjqPqNd+azSUiFaZy0GnmN/03HTb/l3/tvoOnG2iqBymeem7Eq23Oasj8+U2YUP99ulVVYSPi69pG/sST9WfzzL5hfHG0tek44qFU3IjXys4P4ZbvHqJu3SwmN3jbdBwRcZMJtd+jR9Ut3LJ9EhmOSNNxxAOpuBGv9cAvo9h7Opb7799IcECe6Tgi4iYBNou3mk4hpyCYO3dMwNLScfInKm7EKy3NaM/0A0N45uLXqF37hOk4IuJmNUPSmd3kWRand+G11EGm44iHUXEjXufQIfjLjw/QL2odd9dONB1HRAwZHPMloxI+4v6fR7P9pCa3kt+puBGvcvo03HADFBDAG42fOfdMpSLiF55vMIN6oWnc9MM/OX3adBrxFCpuxGvk58Mtt8D69bC4xT+pEXLUdCQRMSw8MId3mv2bH7PrcMcdUKBVVwQVN+IlLAvuuw8WLoR334VOVbaZjiQiHqJV5V+Y1+xJ3n8f7r0XDTAWFTfiHZ5+Gv77X+e6UYMHm04jIp7m2uqrmDnT+Xvi8cdNpxHTNEOxeLw334R//AMefRTuust0GhHxVCNHwuHD8PDDUL06jB5tOpGYouJGPNrSpc5fWHfdBf/6l+k0IuLpJk1yFjhjx0JUFAwbZjqRmKDiRjzW+vVw/fUwcKCzq1lXRonIefXogQ143rKRHvsQtw7vQbUpD3Nl1De/77NihbF44j4acyMeaccOGDAALrsM3nkHglSGi0gJBdgs3mj8DH2rbWDotsdYl9nUdCRxM/2XIe51ZjXv8zgwbwVXXglxcbBkCYSFuSGXiPgUe0A+7zd/jL7fPsuArVNY3fo+mlXaYzqWuIl6bsSjZOZVol8/yMuDZcuc58xFRMoiPDCHjy/5BzVD0un77bPsOR1nOpK4iYob8RgHcqLpteV59u6FTz+F2rVNJxIRb1fNfoJPW04kOMBB32+fIS3NdCJxB52WEo+w5XgDBm17Csuy8UXjkTQf+7PpSCLiI+JDjrC85QN03fIS7do5T3e3amU6lVQk9dyIcR+nd6Dz5peJtR9jfZvRtI5QYSMirtUw/ADrLxtNdDR06gSLF5tOJBVJxY0YY1nw4r7rGLzt3/SJ2sCq1vdRMyTddCwR8VG1Qw+zejVcdRVccw089ZSWavBVKm7ECEdBIKN3jmP8L2OYUPt9FjSfTKVALekrIhWrUiV4/33npKAPPwwjRsCpU6ZTiatpzI243TFHJW74YTIrjrVm1sXPcmdCkulIIuJHAgLgscegWTO4/Xb4+Wfnaar4eNPJxFXUcyNutetUPB03v8I3x5vwacuJKmxExJgbb4TVq+G33+Dyy2HTJtOJxFVU3IjbpKRA+03/JbfAzrrLxtCz2mbTkUTEz7VtC998AwkJ0LkzfPih6UTiCjotJRXu6FF48EGYNQu6VdnDh80nExOcZTqWiPijYmZJTwBWhgbzl4iJXH99L0aPhiefhKpV3Z5OXEQ9N1JhLAveew+aNnX+OX06fHHpeBU2IuJxwgJzmd/030xrOI233oImTZzr2ulqKu+k4kZcp0ePwtueDsMYGLOOYcOgU/4qfmh+PX97vwcBNv2mEBHPZLPBPbUW8eOP0LUrDB8OffvCzp2mk0lp6bSUlEwJFrwEyCsIYNr+a/nn7juoFnSCxS0eYXDMlxUcTkTEdWrWdF4u/sknMGYMtGgBkybBQw9BaKjpdFIS6rkRl9l0vBHtNs1gwi+j+Gv8J/xwxe0qbETEa/XrB9u2wQMPOCf8u+QSSE42nUpKQsWNlNvO7JrcvWM8l2+cQZ4VyNrLxjKt0ctEBmWbjiYiUi7h4fDvf8O330KtWs7TVDfdBLt3m04m56PiRspsfVZTrt32GI2/fovF6Z34z0WvsaHN3bSL3G46moiISzVtCl98AW+9BZ9/Dg0bwvXXw9q1ppNJcTTmRkqlwLKxNKM9z+67kdWZrbg4bC+vXvwCt8QtJzTQYTqeiEiFsdngllvg2mvhrUtf4MWl19Hxwzq0j/yev9d6nyExawgKKPj9CStWmAvr51TcSInkFNiZd7A3z+27ge3Z9egYuY1FzR/h6pivdAWUiPiV8HAYVfNj7kpIZGlGe1747Xqu/+Ex6oWmcl/NBfwl/hOdljdMxY2ck2XBd9/BggXw+rr5pObGMDh6DbMaP0+nKttMxxMRMSrAZjEoZi2DYtay6XgjXvztOh7YNYrJv97OyPil3L4Nmjd39viIe6m4kSIsyzkV+YIFsHChc0G5KlXghuh1jK/1Pk0q7TMdUUSkYpVw6os/uixiJ283ncLTF83i5d+u4dXUQTx/CTQM+41rYtZwTcxq2kVuP7unW6euKoSKGyE/H7766veCZt8+iImBIUPg5ZehZ08IvvJ50zFFRDxezZB0nm4wi0frzeGLY5exKL0zc9Ku5Nl9w6gRnMHg6C+5pvoaelTdTHBAnum4PkvFjR/Kz4cffnCO8l+7FpYtg7Q0iI+HoUOdg+W6dIEgfTpERMokNNBB/+j19I9ez8yLX2RtZjMWpXdhUXpnXk29msjAEwyIXk/Pxs/SPvIHmlbaS6CtoPgXU+9Oqem/Lz9w7BisX+/snVm71vn3rCwIDIRWrWB40Ptc23oV7SN/IOB7C743nVhExHcE2groXHUbnatu47kGM9h68iIWpXfmo/ROvHeoOwUEEhF4kssjdtA+8gfaR/5Au8jtxAYfMx3da6m48SHZ2c41UH76CXbscN42bYLt251jaaKjoUMH5wrdHTvC5ZdDpUpAjxmmo4uI+AWbDVpW3kXLyruYXO8tTuSFsuF4Y9ZlNWNdVjNmp/bnqb0jAKgfeoB2kdu55Cm4+GJo1Mg5v06lSoYPwgsYL26mT5/Os88+S2pqKs2bN2fq1Kl06dLlnPuvXLmS8ePH8/3335OQkMDEiRMZNWqUGxObk58Phw7BgQOQmgq775/Gjuza7DhVm5+ya7E3p0bhvlFBmTQO30fnSruZ2Ph7OkR+T6Ow37CdAJL/dxMREaMqB52me7Vv6V7tW8D5RXRvThzrspqxPqsp67Oa8ulzcPTo78+pWdNZ6JwpeBo1cg4rOHIkhPx8sNsNHYwHMVrcvPfee4wbN47p06fTqVMnXn31Vfr168cPP/xAnTp1ztp/9+7d9O/fn5EjRzJ37ly+/PJLRo8eTfXq1bn22msNHEH5nDrl/DBu3w4nTjhPHx075vwQH352DgdyY0jNjeJAjvPPg7nVKCCw8PnBtlE0CtvPxeH7GB73OY3D9nFx+G80Dt9HtD3L2HGJiEjZ2GxQN/QgdUMPcmPs/8barFhBRoazV/5M7/zOnfD11zBvHpw8CWAHruLOOy1iY53FTo0azj/P/D0mxnn1a9Wqv9+qVIGwMN+7XN1ocfPCCy/w17/+lTvvvBOAqVOn8umnnzJjxgymTJly1v4zZ86kTp06TJ06FYCmTZuyYcMGnnvuOY8obt5803kqKDvbeTt5svi/Z2U5i5icHOeH8c+CgiAmYBAJwRkkhKTTNmIH8cEZJIRkFPkzLvjouQegiYiIb+jRg2igw/9uhaqC1RbScqP49fn3SEzcQK1abTl8OIjUVGcP//ffw2efOS8ayc0t/uXt9t+LncqVnZMUhoc7i54zf//jLeT/XiM4II+QAAfBNgfBAXln/Vn/g2e4+OIKbpfzMFbc5ObmsnHjRh566KEi2/v27ctXX31V7HPWrl1L3759i2y78sormT17Ng6HA3sxfXE5OTnk5OQU3s/MzATgyJEjOByuXS5g8eJAvlt+kNDAHMIDcgkPPE1YQC7hgTnEBJ4mPDCHsIAcKoefpmrkcSqF5LD3qvZ0XrmEKDKpaj9JlaAThAfmlqiKPgb4+/JgDiA7O5sMwB7g321RFmq/8lMblp/asHyCw45R/9EBtLj9dnrMmYw97w+XmFcCGoLVALLzg8nMq0xmXiWy8sL/9/dwshzhZOZXJvN0JbJPhnKqIJhT+SFk54dw1AomOz+EU/khnC5wbssp6IejIIhcy06uFVxspr9NPcoTT7j2y/fx48cBsKwSzIpvGbJ//34LsL788ssi25988knr4osvLvY5jRo1sp588ski27788ksLsA4cOFDscyZPnmwBuummm2666aabD9z27dt3wRrD+IBi25+6KCzLOmvbhfYvbvsZkyZNYvz48YX3CwoKOHLkCNHR0ed9H3fIysqidu3a7Nu3j8jISKNZvJXasHzUfuWnNiw/tWH5+UMbWpbF8ePHSUhIuOC+xoqbmJgYAgMDSUtLK7L90KFDxMXFFfucGjVqFLt/UFAQ0dHRxT4nJCSEkJCQItuqVq1a9uAVIDIy0mc/jO6iNiwftV/5qQ3LT21Yfr7ehlWqVCnRfsZObgYHB9OmTRuSk4tek5ycnEzHjh2LfU6HDh3O2n/58uW0bdu22PE2IiIi4n+MjtwaP348r7/+Om+88Qbbt2/n/vvvZ+/evYXz1kyaNIlbb721cP9Ro0axZ88exo8fz/bt23njjTeYPXs2EyZMMHUIIiIi4mGMjrm58cYbycjI4PHHHyc1NZUWLVqQlJRE3bp1AUhNTWXv3r2F+9evX5+kpCTuv/9+/vvf/5KQkMC0adM84jLwsggJCWHy5MlnnTaTklMblo/ar/zUhuWnNiw/tWFRNssqyTVVIiIiIt5BEwqIiIiIT1FxIyIiIj5FxY2IiIj4FBU3IiIi4lNU3LjI8ePHGTduHHXr1iUsLIyOHTvyzTffFD5+8OBBbr/9dhISEggPD+eqq65i586dF3zdY8eOMWbMGOLj4wkNDaVp06YkJSVV5KEYU1FtOHXqVBo3bkxYWBi1a9fm/vvv5/Tp0xV5KG6xatUqBg0aREJCAjabjcWLFxd53LIsHn30URISEggLC6N79+58//33RfbJycnhnnvuISYmhkqVKnH11Vfz22+/XfC9p0+fTv369QkNDaVNmzasXr3alYfmNqbacMqUKVx++eVEREQQGxvLkCFD2LFjh6sPzy1Mfg7PmDJlCjabjXHjxrngiNzPZBvu37+fESNGEB0dTXh4OJdeeikbN2505eEZoeLGRe68806Sk5N5++232bp1K3379qV3797s378fy7IYMmQIu3bt4qOPPmLz5s3UrVuX3r17c9K5Vn2xcnNz6dOnD7/++isffvghO3bsYNasWdSsWdONR+Y+FdGG8+bN46GHHmLy5Mls376d2bNn89577zFp0iQ3HlnFOHnyJK1ateKVV14p9vFnnnmGF154gVdeeYVvvvmGGjVq0KdPn8LF5wDGjRvHokWLePfdd1mzZg0nTpxg4MCB5Ofnn/N933vvPcaNG8fDDz/M5s2b6dKlC/369SsybYO3MNWGK1euZMyYMaxbt47k5GTy8vLo27fveT/LnspUG57xzTff8Nprr9GyZUuXHZO7mWrDo0eP0qlTJ+x2O5988gk//PADzz//vMfN4l8mF1x9Si4oOzvbCgwMtBITE4tsb9WqlfXwww9bO3bssABr27ZthY/l5eVZUVFR1qxZs875ujNmzLAuuugiKzc3t8Kye4qKasMxY8ZYPXv2LLJt/PjxVufOnV17AIYB1qJFiwrvFxQUWDVq1LCefvrpwm2nT5+2qlSpYs2cOdOyLMs6duyYZbfbrXfffbdwn/3791sBAQHWsmXLzvleV1xxhTVq1Kgi25o0aWI99NBDLjoaM9zZhn926NAhC7BWrlxZ/gMxyN1tePz4catRo0ZWcnKy1a1bN+u+++5z6fGY4M42fPDBB33ud+EZ6rlxgby8PPLz8wkNDS2yPSwsjDVr1pCTkwNQ5PHAwECCg4NZs2bNOV93yZIldOjQgTFjxhAXF0eLFi146qmnSvRtxttUVBt27tyZjRs38vXXXwOwa9cukpKSGDBgQAUchefYvXs3aWlp9O3bt3BbSEgI3bp146uvvgJg48aNOByOIvskJCTQokWLwn3+LDc3l40bNxZ5DkDfvn3P+RxvVVFtWJzMzEwAoqKiXJTeM1R0G44ZM4YBAwbQu3fvijkAD1CRbbhkyRLatm3L9ddfT2xsLK1bt2bWrFkVdzBupOLGBSIiIujQoQNPPPEEBw4cID8/n7lz57J+/XpSU1Np0qQJdevWZdKkSRw9epTc3Fyefvpp0tLSSE1NPefr7tq1iw8//JD8/HySkpJ45JFHeP7553nyySfdeHTuUVFtOGzYMJ544gk6d+6M3W6nQYMG9OjRg4ceesiNR+d+ZxaY/fMitHFxcYWPpaWlERwcTLVq1c65z5+lp6eTn59/3tf1FRXVhn9mWRbjx4+nc+fOtGjRwgXJPUdFtuG7777Lpk2bmDJliotTe5aKbMNdu3YxY8YMGjVqxKeffsqoUaO49957eeutt1x8FO6n4sZF3n77bSzLombNmoSEhDBt2jSGDx9OYGAgdrudBQsW8NNPPxEVFUV4eDgpKSn069ePwMDAc75mQUEBsbGxvPbaa7Rp04Zhw4bx8MMPM2PGDDcemftURBumpKTw5JNPMn36dDZt2sTChQtJTEzkiSeecOORmWOz2YrctyzrrG1/VpJ9yvK63qqi2vCMsWPH8t133/HOO++UOaOnc3Ub7tu3j/vuu4+5c+ee1dvrqyric1hQUMBll13GU089RevWrbn77rsZOXKkT/wfo+LGRRo0aMDKlSs5ceIE+/bt4+uvv8bhcFC/fn0A2rRpw5YtWzh27BipqaksW7aMjIyMwseLEx8fz8UXX1zkP++mTZuSlpZGbm5uhR+Tu1VEG/7zn//klltu4c477+SSSy7hmmuu4amnnmLKlCkUFBS469DcrkaNGgBnfWs7dOhQ4TfAGjVqkJuby9GjR8+5z5/FxMQQGBh43tf1FRXVhn90zz33sGTJElasWEGtWrVclNxzVFQbbty4kUOHDtGmTRuCgoIICgpi5cqVTJs2jaCgIJ86dV+Rn8P4+HiaNWtWZFvTpk298uKAP1Nx42KVKlUiPj6eo0eP8umnnzJ48OAij1epUoXq1auzc+dONmzYcNbjf9SpUyd+/vnnIv8J//TTT8THxxMcHFxhx2CaK9swOzubgICiH/PAwEAsy8Ly4WXV6tevT40aNUhOTi7clpuby8qVK+nYsSPgLBbtdnuRfVJTU9m2bVvhPn8WHBxMmzZtijwHIDk5+ZzP8VYV1Ybg/EY9duxYFi5cyBdffHHeAt2bVVQb9urVi61bt7Jly5bCW9u2bbn55pvZsmXLeXtzvU1Ffg47dep01hQEP/30U+Hi1V7NwCBmn7Rs2TLrk08+sXbt2mUtX77catWqlXXFFVcUXun0/vvvWytWrLB++eUXa/HixVbdunWtoUOHFnmNW265pcgVJ3v37rUqV65sjR071tqxY4eVmJhoxcbGWv/+97/demzuUhFtOHnyZCsiIsJ65513Cl+3QYMG1g033ODWY6sIx48ftzZv3mxt3rzZAqwXXnjB2rx5s7Vnzx7Lsizr6aeftqpUqWItXLjQ2rp1q3XTTTdZ8fHxVlZWVuFrjBo1yqpVq5b12WefWZs2bbJ69uxptWrVysrLyyvcp2fPntbLL79ceP/dd9+17Ha7NXv2bOuHH36wxo0bZ1WqVMn69ddf3XfwLmKqDf/2t79ZVapUsVJSUqzU1NTCW3Z2tvsO3kVMteGfefPVUqba8Ouvv7aCgoKsJ5980tq5c6c1b948Kzw83Jo7d677Dr6CqLhxkffee8+66KKLrODgYKtGjRrWmDFjrGPHjhU+/tJLL1m1atWy7Ha7VadOHeuRRx6xcnJyirxGt27drNtuu63Itq+++spq166dFRISYl100UXWk08+WeTD6ksqog0dDof16KOPWg0aNLBCQ0Ot2rVrW6NHj7aOHj3qpqOqOCtWrLCAs25njr+goMCaPHmyVaNGDSskJMTq2rWrtXXr1iKvcerUKWvs2LFWVFSUFRYWZg0cONDau3dvkX3q1q1rTZ48uci2//73v1bdunWt4OBg67LLLvPaS5hNtWFx7wlYb775ZgUfseuZ/Bz+kTcXNybb8OOPP7ZatGhhhYSEWE2aNLFee+21ijxUt7FZlg/3zYuIiIjf0ZgbERER8SkqbkRERMSnqLgRERERn6LiRkRERHyKihsRERHxKSpuRERExKeouBERERGfouJGREREfIqKGxEREfEpKm5ERETEp6i4EREREZ+i4kZEvN7hw4epUaMGTz31VOG29evXExwczPLlyw0mExETtHCmiPiEpKQkhgwZwldffUWTJk1o3bo1AwYMYOrUqaajiYibqbgREZ8xZswYPvvsMy6//HK+/fZbvvnmG0JDQ03HEhE3U3EjIj7j1KlTtGjRgn379rFhwwZatmxpOpKIGKAxNyLiM3bt2sWBAwcoKChgz549puOIiCHquRERn5Cbm8sVV1zBpZdeSpMmTXjhhRfYunUrcXFxpqOJiJupuBERn/DAAw/w4Ycf8u2331K5cmV69OhBREQEiYmJpqOJiJvptJSIeL2UlBSmTp3K22+/TWRkJAEBAbz99tusWbOGGTNmmI4nIm6mnhsRERHxKeq5EREREZ+i4kZERER8ioobERER8SkqbkRERMSnqLgRERERn6LiRkRERHyKihsRERHxKSpuRERExKeouBERERGfouJGREREfIqKGxEREfEp/w9fsr1QAOF+XQAAAABJRU5ErkJggg==\n",
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\n",
       "text/plain": [
        "
    "
       ]
@@ -2473,7 +2473,13 @@
       "Error: 0.026605727637184558\n",
       "Bias^2: 0.010018312644139219\n",
       "Var: 0.016587414993045335\n",
-      "0.026605727637184558 >= 0.010018312644139219 + 0.016587414993045335 = 0.026605727637184554\n",
+      "0.026605727637184558 >= 0.010018312644139219 + 0.016587414993045335 = 0.026605727637184554\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "Polynomial degree: 10\n",
       "Error: 0.021592704588021178\n",
       "Bias^2: 0.010516485576646504\n",
@@ -2483,24 +2489,12 @@
       "Error: 0.07160048164232538\n",
       "Bias^2: 0.014436800088896381\n",
       "Var: 0.05716368155342902\n",
-      "0.07160048164232538 >= 0.014436800088896381 + 0.05716368155342902 = 0.0716004816423254\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "0.07160048164232538 >= 0.014436800088896381 + 0.05716368155342902 = 0.0716004816423254\n",
       "Polynomial degree: 12\n",
       "Error: 0.11547777218876518\n",
       "Bias^2: 0.016285782696017142\n",
       "Var: 0.09919198949274803\n",
-      "0.11547777218876518 >= 0.016285782696017142 + 0.09919198949274803 = 0.11547777218876518\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "0.11547777218876518 >= 0.016285782696017142 + 0.09919198949274803 = 0.11547777218876518\n",
       "Polynomial degree: 13\n",
       "Error: 0.2284246870217162\n",
       "Bias^2: 0.01975416527168255\n",
@@ -2517,7 +2511,7 @@
      },
      "metadata": {
       "filenames": {
-       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week37_162_4.png"
+       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week37_162_3.png"
       }
      },
      "output_type": "display_data"
@@ -3066,9 +3060,9 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31736/626635268.py:73: RuntimeWarning: divide by zero encountered in log10\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11090/626635268.py:73: RuntimeWarning: divide by zero encountered in log10\n",
       "  plt.plot(polynomial, np.log10(trainingerror), label='Training Error')\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31736/626635268.py:74: RuntimeWarning: divide by zero encountered in log10\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11090/626635268.py:74: RuntimeWarning: divide by zero encountered in log10\n",
       "  plt.plot(polynomial, np.log10(testerror), label='Test Error')\n"
      ]
     },
@@ -3203,7 +3197,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31736/3817475779.py:63: RuntimeWarning: divide by zero encountered in log10\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11090/3817475779.py:63: RuntimeWarning: divide by zero encountered in log10\n",
       "  plt.plot(polynomial, np.log10(estimated_mse_sklearn), label='Test Error')\n"
      ]
     },
diff --git a/doc/LectureNotes/_build/jupyter_execute/week37_144_0.png b/doc/LectureNotes/_build/jupyter_execute/week37_144_0.png
index 736939b6c..4d8d3f41c 100644
Binary files a/doc/LectureNotes/_build/jupyter_execute/week37_144_0.png and b/doc/LectureNotes/_build/jupyter_execute/week37_144_0.png differ
diff --git a/doc/LectureNotes/_build/jupyter_execute/week38.ipynb b/doc/LectureNotes/_build/jupyter_execute/week38.ipynb
index b2849e31e..b8c454dd8 100644
--- a/doc/LectureNotes/_build/jupyter_execute/week38.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/week38.ipynb
@@ -1679,7 +1679,7 @@
      "output_type": "stream",
      "text": [
       "RandomizedSearchCV(estimator=Ridge(), n_iter=100,\n",
-      "                   param_distributions={'alpha': })\n",
+      "                   param_distributions={'alpha': })\n",
       "Best estimated lambda-value: 0.9849967686928113\n",
       "MSE score: 1.0853136633465326\n",
       "R2 score: -0.0002382102844775691\n"
diff --git a/doc/LectureNotes/_build/jupyter_execute/week39.ipynb b/doc/LectureNotes/_build/jupyter_execute/week39.ipynb
index 77072dd0d..5fdb1fa92 100644
--- a/doc/LectureNotes/_build/jupyter_execute/week39.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/week39.ipynb
@@ -1160,14 +1160,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31749/3838917029.py:18: MatplotlibDeprecationWarning: Calling gca() with keyword arguments was deprecated in Matplotlib 3.4. Starting two minor releases later, gca() will take no keyword arguments. The gca() function should only be used to get the current axes, or if no axes exist, create new axes with default keyword arguments. To create a new axes with non-default arguments, use plt.axes() or plt.subplot().\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11106/3838917029.py:18: MatplotlibDeprecationWarning: Calling gca() with keyword arguments was deprecated in Matplotlib 3.4. Starting two minor releases later, gca() will take no keyword arguments. The gca() function should only be used to get the current axes, or if no axes exist, create new axes with default keyword arguments. To create a new axes with non-default arguments, use plt.axes() or plt.subplot().\n",
       "  ax = fig.gca(projection=\"3d\")\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       ""
+       ""
       ]
      },
      "execution_count": 1,
@@ -1337,7 +1337,7 @@
     {
      "data": {
       "text/plain": [
-       "[]"
+       "[]"
       ]
      },
      "execution_count": 5,
diff --git a/doc/LectureNotes/_build/jupyter_execute/week40.ipynb b/doc/LectureNotes/_build/jupyter_execute/week40.ipynb
index da63ddffb..65b0f85cf 100644
--- a/doc/LectureNotes/_build/jupyter_execute/week40.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/week40.ipynb
@@ -485,20 +485,20 @@
      "output_type": "stream",
      "text": [
       "Own inversion\n",
-      "[[4.42484459]\n",
-      " [2.65626992]]\n",
-      "Eigenvalues of Hessian Matrix:[0.30361418 4.06484621]\n",
+      "[[3.88391015]\n",
+      " [3.15024162]]\n",
+      "Eigenvalues of Hessian Matrix:[0.29734306 4.63081005]\n",
       "theta from own gd\n",
-      "[[4.42484459]\n",
-      " [2.65626992]]\n",
+      "[[3.88391015]\n",
+      " [3.15024162]]\n",
       "theta from own sdg\n",
-      "[[4.53049637]\n",
-      " [2.68581655]]\n"
+      "[[3.92822216]\n",
+      " [3.17648722]]\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "
    "
       ]
diff --git a/doc/LectureNotes/_build/jupyter_execute/week40_25_1.png b/doc/LectureNotes/_build/jupyter_execute/week40_25_1.png
index 1adb4c03c..cb9eb54d5 100644
Binary files a/doc/LectureNotes/_build/jupyter_execute/week40_25_1.png and b/doc/LectureNotes/_build/jupyter_execute/week40_25_1.png differ
diff --git a/doc/LectureNotes/_build/jupyter_execute/week41.ipynb b/doc/LectureNotes/_build/jupyter_execute/week41.ipynb
index cf7d8ff7d..0eb2283e7 100644
--- a/doc/LectureNotes/_build/jupyter_execute/week41.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/week41.ipynb
@@ -3368,7 +3368,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -3956,7 +3956,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -3974,7 +3974,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -3992,7 +3992,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -4010,7 +4010,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -4028,7 +4028,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -4046,7 +4046,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -4064,7 +4064,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -4082,11 +4082,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -4104,11 +4104,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -4126,11 +4126,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -4148,11 +4148,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -4170,11 +4170,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -4192,7 +4192,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -4210,11 +4210,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -4232,11 +4232,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -4254,11 +4254,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -4276,11 +4276,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -4298,11 +4298,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -4320,11 +4320,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -4342,11 +4342,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -4364,11 +4364,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -4429,15 +4429,15 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -4980,7 +4980,13 @@
       "Learning rate  =  0.01\n",
       "Lambda =  1.0\n",
       "Accuracy score on test set:  0.9722222222222222\n",
-      "\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "Learning rate  =  0.01\n",
       "Lambda =  10.0\n",
       "Accuracy score on test set:  0.9527777777777777\n",
@@ -5072,7 +5078,13 @@
       "Learning rate  =  1.0\n",
       "Lambda =  1.0\n",
       "Accuracy score on test set:  0.08888888888888889\n",
-      "\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "Learning rate  =  1.0\n",
       "Lambda =  10.0\n",
       "Accuracy score on test set:  0.09444444444444444\n",
@@ -5098,7 +5110,13 @@
       "Learning rate  =  10.0\n",
       "Lambda =  0.01\n",
       "Accuracy score on test set:  0.1388888888888889\n",
-      "\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "Learning rate  =  10.0\n",
       "Lambda =  0.1\n",
       "Accuracy score on test set:  0.11388888888888889\n",
@@ -5627,7 +5645,13 @@
       "Learning rate  =  1.0\n",
       "Lambda =  10.0\n",
       "Accuracy score on data set:  0.5\n",
-      "\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "Learning rate  =  10.0\n",
       "Lambda =  1e-05\n",
       "Accuracy score on data set:  0.5\n",
@@ -5693,7 +5717,7 @@
      },
      "metadata": {
       "filenames": {
-       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week41_211_2.png"
+       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week41_211_3.png"
       }
      },
      "output_type": "display_data"
diff --git a/doc/LectureNotes/_build/jupyter_execute/week41_211_3.png b/doc/LectureNotes/_build/jupyter_execute/week41_211_3.png
new file mode 100644
index 000000000..d6998a4a8
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diff --git a/doc/LectureNotes/_build/jupyter_execute/week42.ipynb b/doc/LectureNotes/_build/jupyter_execute/week42.ipynb
index 743a68302..0bc7cbf2b 100644
--- a/doc/LectureNotes/_build/jupyter_execute/week42.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/week42.ipynb
@@ -3,9 +3,7 @@
   {
    "cell_type": "markdown",
    "id": "50ce4eae",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "\n",
@@ -15,9 +13,7 @@
   {
    "cell_type": "markdown",
    "id": "f46bd6b4",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "# Week 42 Constructing a Neural Network code with introduction to Tensor flow\n",
     "**Morten Hjorth-Jensen**, Department of Physics, University of Oslo and Department of Physics and Astronomy and Facility for Rare Isotope Beams, Michigan State University\n",
@@ -28,9 +24,7 @@
   {
    "cell_type": "markdown",
    "id": "8c0fa4d7",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Plan for week 42\n",
     "\n",
@@ -70,9 +64,7 @@
   {
    "cell_type": "markdown",
    "id": "89b6b637",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Lecture Thursday October 19"
    ]
@@ -80,9 +72,7 @@
   {
    "cell_type": "markdown",
    "id": "3a32ad82",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Review of the back propagation algorithm\n",
     "\n",
@@ -95,9 +85,7 @@
   {
    "cell_type": "markdown",
    "id": "4f9291ee",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Setting up the Back propagation algorithm\n",
     "\n",
@@ -118,9 +106,7 @@
   {
    "cell_type": "markdown",
    "id": "7753981f",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "\\delta_j^L = f'(z_j^L)\\frac{\\partial {\\cal C}}{\\partial (a_j^L)}.\n",
@@ -130,9 +116,7 @@
   {
    "cell_type": "markdown",
    "id": "8b093c71",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Then we compute the back propagate error for each $l=L-1,L-2,\\dots,2$ as"
    ]
@@ -140,9 +124,7 @@
   {
    "cell_type": "markdown",
    "id": "96ca25bd",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "\\delta_j^l = \\sum_k \\delta_k^{l+1}w_{kj}^{l+1}f'(z_j^l).\n",
@@ -152,9 +134,7 @@
   {
    "cell_type": "markdown",
    "id": "a156d8bd",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Finally, we update the weights and the biases using gradient descent for each $l=L-1,L-2,\\dots,2$ and update the weights and biases according to the rules"
    ]
@@ -162,9 +142,7 @@
   {
    "cell_type": "markdown",
    "id": "f35c8afe",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "w_{jk}^l\\leftarrow  = w_{jk}^l- \\eta \\delta_j^la_k^{l-1},\n",
@@ -174,9 +152,7 @@
   {
    "cell_type": "markdown",
    "id": "ffa6d322",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "b_j^l \\leftarrow b_j^l-\\eta \\frac{\\partial {\\cal C}}{\\partial b_j^l}=b_j^l-\\eta \\delta_j^l,\n",
@@ -186,9 +162,7 @@
   {
    "cell_type": "markdown",
    "id": "7b6e59f6",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The parameter $\\eta$ is the learning parameter discussed in connection with the gradient descent methods.\n",
     "Here it is convenient to use stochastic gradient descent (see the examples below) with mini-batches with an outer loop that steps through multiple epochs of training."
@@ -197,9 +171,7 @@
   {
    "cell_type": "markdown",
    "id": "e93ff00c",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Setting up a Multi-layer perceptron model for classification\n",
     "\n",
@@ -225,9 +197,7 @@
   {
    "cell_type": "markdown",
    "id": "3c437395",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "P(y = 0 \\mid \\boldsymbol{x}, \\boldsymbol{\\theta}) = \\frac{1}{1 + \\exp{(- \\boldsymbol{x}})} ,\n",
@@ -237,9 +207,7 @@
   {
    "cell_type": "markdown",
    "id": "3d7b1140",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "and"
    ]
@@ -247,9 +215,7 @@
   {
    "cell_type": "markdown",
    "id": "e3df5aec",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "P(y = 1 \\mid \\boldsymbol{x}, \\boldsymbol{\\theta}) = 1 - P(y = 0 \\mid \\boldsymbol{x}, \\boldsymbol{\\theta}) ,\n",
@@ -259,9 +225,7 @@
   {
    "cell_type": "markdown",
    "id": "63345646",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "where $y \\in \\{0, 1\\}$  and $\\boldsymbol{\\theta}$ represents the weights and biases\n",
     "of our network."
@@ -270,9 +234,7 @@
   {
    "cell_type": "markdown",
    "id": "6ac465b3",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Defining the cost function\n",
     "\n",
@@ -282,9 +244,7 @@
   {
    "cell_type": "markdown",
    "id": "cf06b4a0",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "\\mathcal{C}(\\boldsymbol{\\theta}) = - \\ln P(\\mathcal{D} \\mid \\boldsymbol{\\theta}) = - \\sum_{i=1}^n\n",
@@ -295,9 +255,7 @@
   {
    "cell_type": "markdown",
    "id": "719f761f",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "This last equality means that we can interpret our *cost* function as a sum over the *loss* function\n",
     "for each point in the dataset $\\mathcal{L}_i(\\boldsymbol{\\theta})$.  \n",
@@ -320,9 +278,7 @@
   {
    "cell_type": "markdown",
    "id": "342de1d9",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "P(y_{ic} = 1 \\mid \\boldsymbol{x}_i, \\boldsymbol{\\theta}) = \\frac{\\exp{((\\boldsymbol{a}_i^{hidden})^T \\boldsymbol{w}_c)}}\n",
@@ -333,9 +289,7 @@
   {
    "cell_type": "markdown",
    "id": "211a69ba",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "which reduces to the logistic function in the binary case.  \n",
     "The likelihood of this $C$-class classifier\n",
@@ -345,9 +299,7 @@
   {
    "cell_type": "markdown",
    "id": "5f0cd5a2",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "P(\\mathcal{D} \\mid \\boldsymbol{\\theta}) = \\prod_{i=1}^n \\prod_{c=0}^{C-1} [P(y_{ic} = 1)]^{y_{ic}} .\n",
@@ -357,9 +309,7 @@
   {
    "cell_type": "markdown",
    "id": "fe018e32",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Again we take the negative log-likelihood to define our cost function:"
    ]
@@ -367,9 +317,7 @@
   {
    "cell_type": "markdown",
    "id": "9d48faca",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "\\mathcal{C}(\\boldsymbol{\\theta}) = - \\log{P(\\mathcal{D} \\mid \\boldsymbol{\\theta})}.\n",
@@ -379,9 +327,7 @@
   {
    "cell_type": "markdown",
    "id": "897c8b0c",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "See the logistic regression lectures for a full definition of the cost function.\n",
     "\n",
@@ -391,9 +337,7 @@
   {
    "cell_type": "markdown",
    "id": "68347a7f",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Example: binary classification problem\n",
     "\n",
@@ -403,9 +347,7 @@
   {
    "cell_type": "markdown",
    "id": "8425d868",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "\\mathcal{C}(\\boldsymbol{\\beta}) = - \\sum_{i=1}^n \\left(y_i\\log{p(y_i \\vert x_i,\\boldsymbol{\\beta})}+(1-y_i)\\log{1-p(y_i \\vert x_i,\\boldsymbol{\\beta})}\\right),\n",
@@ -415,9 +357,7 @@
   {
    "cell_type": "markdown",
    "id": "9108d4ac",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "where we had defined the logistic (sigmoid) function"
    ]
@@ -425,9 +365,7 @@
   {
    "cell_type": "markdown",
    "id": "77e0ec3b",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "p(y_i =1\\vert x_i,\\boldsymbol{\\beta})=\\frac{\\exp{(\\beta_0+\\beta_1 x_i)}}{1+\\exp{(\\beta_0+\\beta_1 x_i)}},\n",
@@ -437,9 +375,7 @@
   {
    "cell_type": "markdown",
    "id": "64ed867c",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "and"
    ]
@@ -447,9 +383,7 @@
   {
    "cell_type": "markdown",
    "id": "51819578",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "p(y_i =0\\vert x_i,\\boldsymbol{\\beta})=1-p(y_i =1\\vert x_i,\\boldsymbol{\\beta}).\n",
@@ -459,9 +393,7 @@
   {
    "cell_type": "markdown",
    "id": "db6532a5",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The parameters $\\boldsymbol{\\beta}$ were defined using a minimization method like gradient descent or Newton-Raphson's method. \n",
     "\n",
@@ -472,9 +404,7 @@
   {
    "cell_type": "markdown",
    "id": "24e5e213",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "a_i^l = y_i = \\frac{\\exp{(z_i^l)}}{1+\\exp{(z_i^l)}},\n",
@@ -484,9 +414,7 @@
   {
    "cell_type": "markdown",
    "id": "d398c961",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "with"
    ]
@@ -494,9 +422,7 @@
   {
    "cell_type": "markdown",
    "id": "236d161c",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "z_i^l = \\sum_{j}w_{ij}^l a_j^{l-1}+b_i^l,\n",
@@ -506,9 +432,7 @@
   {
    "cell_type": "markdown",
    "id": "25e3004d",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "where the superscript $l-1$ indicates that these are the outputs from layer $l-1$.\n",
     "Our cost function at the final layer $l=L$ is now"
@@ -517,9 +441,7 @@
   {
    "cell_type": "markdown",
    "id": "9440c725",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "\\mathcal{C}(\\boldsymbol{W}) = - \\sum_{i=1}^n \\left(t_i\\log{a_i^L}+(1-t_i)\\log{(1-a_i^L)}\\right),\n",
@@ -529,9 +451,7 @@
   {
    "cell_type": "markdown",
    "id": "782f5282",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "where we have defined the targets $t_i$. The derivatives of the cost function with respect to the output $a_i^L$ are then easily calculated and we get"
    ]
@@ -539,9 +459,7 @@
   {
    "cell_type": "markdown",
    "id": "0e8498a5",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "\\frac{\\partial \\mathcal{C}(\\boldsymbol{W})}{\\partial a_i^L} = \\frac{a_i^L-t_i}{a_i^L(1-a_i^L)}.\n",
@@ -551,9 +469,7 @@
   {
    "cell_type": "markdown",
    "id": "68398b35",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "In case we use another activation function than the logistic one, we need to evaluate other derivatives."
    ]
@@ -561,9 +477,7 @@
   {
    "cell_type": "markdown",
    "id": "19887152",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## The Softmax function\n",
     "In case we employ the more general case given by the Softmax equation, we need to evaluate the derivative of the activation function with respect to the activation $z_i^l$, that is we need"
@@ -572,9 +486,7 @@
   {
    "cell_type": "markdown",
    "id": "80e8dc5d",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "\\frac{\\partial f(z_i^l)}{\\partial w_{jk}^l} =\n",
@@ -585,9 +497,7 @@
   {
    "cell_type": "markdown",
    "id": "68d33776",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "For the Softmax function we have"
    ]
@@ -595,9 +505,7 @@
   {
    "cell_type": "markdown",
    "id": "3c86943c",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "f(z_i^l) = \\frac{\\exp{(z_i^l)}}{\\sum_{m=1}^K\\exp{(z_m^l)}}.\n",
@@ -607,9 +515,7 @@
   {
    "cell_type": "markdown",
    "id": "efe53876",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Its derivative with respect to $z_j^l$ gives"
    ]
@@ -617,9 +523,7 @@
   {
    "cell_type": "markdown",
    "id": "fce5b9b2",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "\\frac{\\partial f(z_i^l)}{\\partial z_j^l}= f(z_i^l)\\left(\\delta_{ij}-f(z_j^l)\\right),\n",
@@ -629,9 +533,7 @@
   {
    "cell_type": "markdown",
    "id": "97210471",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "which in case of the simply binary model reduces to  having $i=j$."
    ]
@@ -639,9 +541,7 @@
   {
    "cell_type": "markdown",
    "id": "4f515591",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Developing a code for doing neural networks with back propagation\n",
     "\n",
@@ -663,9 +563,7 @@
   {
    "cell_type": "markdown",
    "id": "ec34f212",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Collect and pre-process data\n",
     "\n",
@@ -713,10 +611,7 @@
    "cell_type": "code",
    "execution_count": 1,
    "id": "e389e60e",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -792,9 +687,7 @@
   {
    "cell_type": "markdown",
    "id": "9a264b82",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Train and test datasets\n",
     "\n",
@@ -813,10 +706,7 @@
    "cell_type": "code",
    "execution_count": 2,
    "id": "8750ea41",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -860,9 +750,7 @@
   {
    "cell_type": "markdown",
    "id": "d3897eca",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Define model and architecture\n",
     "\n",
@@ -904,9 +792,7 @@
   {
    "cell_type": "markdown",
    "id": "ad593a03",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Layers\n",
     "\n",
@@ -944,9 +830,7 @@
   {
    "cell_type": "markdown",
    "id": "e37b3844",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Weights and biases\n",
     "\n",
@@ -965,10 +849,7 @@
    "cell_type": "code",
    "execution_count": 3,
    "id": "3d909fc7",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# building our neural network\n",
@@ -991,9 +872,7 @@
   {
    "cell_type": "markdown",
    "id": "b89c2d9f",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Feed-forward pass\n",
     "\n",
@@ -1019,9 +898,7 @@
   {
    "cell_type": "markdown",
    "id": "435c0ced",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Matrix multiplications\n",
     "\n",
@@ -1056,10 +933,7 @@
    "cell_type": "code",
    "execution_count": 4,
    "id": "3037d7ab",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -1119,9 +993,7 @@
   {
    "cell_type": "markdown",
    "id": "61c33a3f",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Choose cost function and optimizer\n",
     "\n",
@@ -1150,9 +1022,7 @@
   {
    "cell_type": "markdown",
    "id": "665f44ff",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Optimizing the cost function\n",
     "\n",
@@ -1188,9 +1058,7 @@
   {
    "cell_type": "markdown",
    "id": "2d0168d0",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Regularization\n",
     "\n",
@@ -1222,9 +1090,7 @@
   {
    "cell_type": "markdown",
    "id": "9c0a8db3",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Matrix  multiplication\n",
     "\n",
@@ -1263,10 +1129,7 @@
    "cell_type": "code",
    "execution_count": 5,
    "id": "0bf3739e",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -1279,7 +1142,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -1365,9 +1228,7 @@
   {
    "cell_type": "markdown",
    "id": "33e198f3",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Improving performance\n",
     "\n",
@@ -1386,9 +1247,7 @@
   {
    "cell_type": "markdown",
    "id": "932f6c5e",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Full object-oriented implementation\n",
     "\n",
@@ -1400,10 +1259,7 @@
    "cell_type": "code",
    "execution_count": 6,
    "id": "91e351de",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "class NeuralNetwork:\n",
@@ -1510,9 +1366,7 @@
   {
    "cell_type": "markdown",
    "id": "e8c2feb6",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Evaluate model performance on test data\n",
     "\n",
@@ -1529,10 +1383,7 @@
    "cell_type": "code",
    "execution_count": 7,
    "id": "1534af1b",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -1564,9 +1415,7 @@
   {
    "cell_type": "markdown",
    "id": "85627e28",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Adjust hyperparameters\n",
     "\n",
@@ -1578,10 +1427,7 @@
    "cell_type": "code",
    "execution_count": 8,
    "id": "19382903",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -1867,7 +1713,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -1885,7 +1731,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -1903,7 +1749,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -1921,7 +1767,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -1939,7 +1785,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -1957,7 +1803,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -1975,7 +1821,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -1993,11 +1839,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -2015,11 +1861,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -2037,11 +1883,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -2059,11 +1905,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -2081,11 +1927,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -2103,7 +1949,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -2121,11 +1967,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -2143,11 +1989,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -2165,11 +2011,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -2187,11 +2033,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -2209,11 +2055,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -2231,11 +2077,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -2253,11 +2099,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -2275,11 +2121,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -2320,9 +2166,7 @@
   {
    "cell_type": "markdown",
    "id": "12bc42df",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Visualization"
    ]
@@ -2331,24 +2175,21 @@
    "cell_type": "code",
    "execution_count": 9,
    "id": "ec0dc239",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31871/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -2420,9 +2261,7 @@
   {
    "cell_type": "markdown",
    "id": "4dd39506",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## scikit-learn implementation\n",
     "\n",
@@ -2443,10 +2282,7 @@
    "cell_type": "code",
    "execution_count": 10,
    "id": "d9dbb807",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stderr",
@@ -2951,10 +2787,6 @@
       "Learning rate  =  1.0\n",
       "Lambda =  1e-05\n",
       "Accuracy score on test set:  0.08611111111111111\n",
-      "\n",
-      "Learning rate  =  1.0\n",
-      "Lambda =  0.0001\n",
-      "Accuracy score on test set:  0.10555555555555556\n",
       "\n"
      ]
     },
@@ -2963,9 +2795,19 @@
      "output_type": "stream",
      "text": [
       "Learning rate  =  1.0\n",
-      "Lambda =  0.001\n",
+      "Lambda =  0.0001\n",
       "Accuracy score on test set:  0.10555555555555556\n",
       "\n",
+      "Learning rate  =  1.0\n",
+      "Lambda =  0.001\n",
+      "Accuracy score on test set:  0.10555555555555556\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "Learning rate  =  1.0\n",
       "Lambda =  0.01\n",
       "Accuracy score on test set:  0.17777777777777778\n",
@@ -2973,20 +2815,10 @@
       "Learning rate  =  1.0\n",
       "Lambda =  0.1\n",
       "Accuracy score on test set:  0.08333333333333333\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "\n",
       "Learning rate  =  1.0\n",
       "Lambda =  1.0\n",
       "Accuracy score on test set:  0.08888888888888889\n",
-      "\n",
-      "Learning rate  =  1.0\n",
-      "Lambda =  10.0\n",
-      "Accuracy score on test set:  0.09444444444444444\n",
       "\n"
      ]
     },
@@ -2994,10 +2826,20 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "Learning rate  =  1.0\n",
+      "Lambda =  10.0\n",
+      "Accuracy score on test set:  0.09444444444444444\n",
+      "\n",
       "Learning rate  =  10.0\n",
       "Lambda =  1e-05\n",
       "Accuracy score on test set:  0.17222222222222222\n",
-      "\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "Learning rate  =  10.0\n",
       "Lambda =  0.0001\n",
       "Accuracy score on test set:  0.11666666666666667\n",
@@ -3059,9 +2901,7 @@
   {
    "cell_type": "markdown",
    "id": "214af3ab",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Visualization"
    ]
@@ -3070,10 +2910,7 @@
    "cell_type": "code",
    "execution_count": 11,
    "id": "d57415ac",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -3144,9 +2981,7 @@
   {
    "cell_type": "markdown",
    "id": "fe7af77c",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Testing our code for the XOR, OR and AND gates\n",
     "\n",
@@ -3171,9 +3006,7 @@
   {
    "cell_type": "markdown",
    "id": "7e12b1cf",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## The AND and XOR Gates\n",
     "\n",
@@ -3209,9 +3042,7 @@
   {
    "cell_type": "markdown",
    "id": "4b5002b4",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Representing the Data Sets\n",
     "\n",
@@ -3221,9 +3052,7 @@
   {
    "cell_type": "markdown",
    "id": "a44df1a3",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "\\boldsymbol{X}=\\begin{bmatrix} 0 & 0 \\\\\n",
@@ -3236,9 +3065,7 @@
   {
    "cell_type": "markdown",
    "id": "acdb4e08",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "while the vector of outputs is $\\boldsymbol{y}^T=[0,1,1,0]$ for the XOR gate, $\\boldsymbol{y}^T=[0,0,0,1]$ for the AND gate and $\\boldsymbol{y}^T=[0,1,1,1]$ for the OR gate."
    ]
@@ -3246,9 +3073,7 @@
   {
    "cell_type": "markdown",
    "id": "0567fd0f",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Setting up the Neural Network\n",
     "\n",
@@ -3259,10 +3084,7 @@
    "cell_type": "code",
    "execution_count": 12,
    "id": "412401df",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -3347,9 +3169,7 @@
   {
    "cell_type": "markdown",
    "id": "53c52dab",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Not an impressive result, but this was our first forward pass with randomly assigned weights. Let us now add the full network with the back-propagation algorithm discussed above."
    ]
@@ -3357,9 +3177,7 @@
   {
    "cell_type": "markdown",
    "id": "7d192a02",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## The Code using Scikit-Learn"
    ]
@@ -3368,10 +3186,7 @@
    "cell_type": "code",
    "execution_count": 13,
    "id": "766d5af6",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -3560,16 +3375,21 @@
       "Learning rate  =  10.0\n",
       "Lambda =  0.01\n",
       "Accuracy score on data set:  0.5\n",
+      "\n",
+      "Learning rate  =  10.0\n",
+      "Lambda =  0.1\n",
+      "Accuracy score on data set:  0.5\n",
+      "\n",
+      "Learning rate  =  10.0\n",
+      "Lambda =  1.0\n",
+      "Accuracy score on data set:  0.5\n",
+      "\n",
+      "Learning rate  =  10.0\n",
+      "Lambda =  10.0\n",
+      "Accuracy score on data set:  0.5\n",
       "\n"
      ]
     },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  = "
-     ]
-    },
     {
      "name": "stderr",
      "output_type": "stream",
@@ -3596,24 +3416,6 @@
       "  warnings.warn(\n"
      ]
     },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " 10.0\n",
-      "Lambda =  0.1\n",
-      "Accuracy score on data set:  0.5\n",
-      "\n",
-      "Learning rate  =  10.0\n",
-      "Lambda =  1.0\n",
-      "Accuracy score on data set:  0.5\n",
-      "\n",
-      "Learning rate  =  10.0\n",
-      "Lambda =  10.0\n",
-      "Accuracy score on data set:  0.5\n",
-      "\n"
-     ]
-    },
     {
      "data": {
       "image/png": 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7qILNptmrt5a4T9O6tSRJsW9+qryCguL1nLyCErcv78ixsciv8cix8cixsciv8cgxfBUFiAUG391e+xNS9fy81ZKkTXHHVKlCBQ24s43mr9uh3PxCt32ahYfo5C/p2nEwwexwfRI5Nhb5NR45Nh45Nhb5NR45hq/iHBCTVa5UUTdHhOubXYdc1r/edVD+Druim9Qpcb9m4bW0PyHVjBB9Hjk2Fvk1Hjk2Hjk2Fvk1Hjm2RqFspfLha+iAmCy8ZjXZK1fS8ZQzLusnUtMkSfVDamjzvuNu+zUNr6UjSac0b8yDalY3RGezcvX55jjN+myTCoqKzAjdZ5BjY5Ff45Fj45FjY5Ff45Fj+DLLC5CCggJ99dVX2r59u37++Wfl5eWpSpUqCg0NVUxMjHr06KFKlSwP02sCq/hJkjJz8lzWs3597l/F7rZPcGBV1azmL6fTqWnLNijxTIbaNqunAXfEqHaNAI2bu9r4wH0IOTYW+TUeOTYeOTYW+TUeOYYvs/Rf9sePH1dsbKySk5MVFRWlkJAQVatWTbm5udq3b5+WLFmi6dOn64MPPtD1119vZaheY7P92iZzlnwnm5K+fDiXk6shby/RseTTSj6TKUnaefCk8gsKNbxXB81etVVHkk4bFbLPIcfGIr/GI8fGI8fGIr/GI8fWKHL63rhTaWTpOSATJ05UeHi41q9frwULFuitt97S66+/rrfeeksLFizQt99+q7CwML344otWhulVZ7NzJUn+DtdvJqr++jwzJ9dtn9z8Qm2NP178y+KC9XuOSJIiwmsaEarPIsfGIr/GI8fGI8fGIr/GI8fwZZYWIDt27NDYsWMVGBhY4utBQUEaM2aMtm27+PWsfU1CapoKCotUN6S6y3rdWuef/5R4ym2f+rVrqHenFm6/ZByVzzew0jJzDInVV5FjY5Ff45Fj45FjY5Ff45Fj+DJLC5CgoCClpKRccpuff/5ZDofDpIiMl1dQqF2HEnR7qyYu692jI5SRlaO9R5Pc9gmpHqDn/thN3aMjXNbviGmqzOxc7TuebGjMvoYcG4v8Go8cG48cG4v8Go8cW8Pqq11xFSwv6NOnj5555hmNHDlS7dq1U1hYmOx2u/Ly8pScnKytW7dqypQp6tOnj5Vhet0Hq7Zq1sjeen3Q3Vrx3V61bBSmP/WI0bTl65WbXyh/h12NwoJ1IjVdaZnZ2nEgQdv2n9DoPp3l8Kuso0mn1al5Qz10W7TeWvqtzma5t1nLO3JsLPJrPHJsPHJsLPJrPHIMX2VzOi9y9pIJnE6n3nnnHc2dO1dZWVlur/v7++vhhx/WX/7yF1WocO3NmtZDp17ze3hL15aNNaTnLapfu4ZS0s/pk39/r/nrdkqSbo4I1/ujH9D4D9fo881xkqQAh12De96i21o2Vs1q/kpITdeCb3Zq2cY9Vv4YpRo5Nhb5NR45Nh45Nhb5NV5ZzPHOWaOsDuGidh6vZ3UIJWpdz/2Sy6WZpQXIBfn5+dq3b5+Sk5OVnZ0th8Oh0NBQRUZGym53v4zc1SpNBQgAAADcleYCZNvxBlaHUKI29Y5aHcIVKRU32KhcubJatGhhdRgAAAAADGbpSegAAAAAypdS0QEBAAAASjtuROgddEAAAAAAmIYCBAAAAIBpGMECAAAAPOCLN/0rjeiAAAAAADANBQgAAAAA0zCCBQAAAHig0Ml3995AFgEAAACYhgIEAAAAgGkYwQIAAAA8UMR3915BFgEAAACYhgIEAAAAgGkYwQIAAAA8wI0IvYMOCAAAAADTUIAAAAAAMA0jWAAAAIAHuBGhd5BFAAAAAKahAAEAAABgGkawAAAAAA8UcRUsr6ADAgAAAMA0FCAAAAAATMMIFgAAAOCBQr679wqyCAAAAMA0FCAAAAAATMMIFgAAAOABbkToHWQRAAAAgGkoQAAAAACYhhEsAAAAwANFfHfvFWQRAAAAgGkoQAAAAACYhhEsAAAAwAOFTpvVIZQJdEAAAAAAmIYCBAAAAIBpKEAAAAAAmIZzQAAAAAAPFPLdvVeQRQAAAKCcKioq0rRp09SpUye1bNlSAwcO1LFjxy66/YkTJzRkyBC1bdtWHTp00KRJk5SdnX1Fx6QAAQAAAMqpmTNnauHChZo0aZIWLVokm82m2NhY5eXluW179uxZ9e3bV+np6frggw/07rvvas+ePRo+fPgVHZMCBAAAAPBAkbNCqXxcrby8PM2ZM0cjRoxQly5dFBkZqalTpyo5OVlr1651237ZsmXKzMzUO++8oxYtWqh58+aaOnWqNm3apO3bt3t8XAoQAAAAoByKj4/XuXPn1L59++K1oKAgRUVFadu2bW7bHzlyRI0aNVJwcHDxWlhYmGrUqKGtW7d6fFxOQgcAAAB8WLdu3S75+rp160pcT0pKknS+iPhfISEhSkxMdNu+Vq1aSk1NVWFhoSpWrChJyszMVHp6uk6dOuVxvHRAAAAAAA8UqkKpfFytCyeP2+12l3U/Pz/l5ua6bX/33XcrPT1dr7zyis6dO6eMjAyNHz9eNputxHNGLoYOCAAAAODDLtbhuByHwyHp/LkgF/4sSbm5uapSpYrb9vXr19f06dP1wgsv6OOPP5bD4dAjjzyim266SQEBAR4flwIEAAAAKIcujF6lpKSoXr16xespKSmKjIwscZ8uXbroP//5j1JTUxUYGCiHw6Fbb71V999/v8fHZQQLAAAA8ECh01YqH1crMjJSAQEB2rJlS/FaRkaG4uLiFBMT47b9jh071K9fP+Xl5alWrVpyOBzaunWrzpw5o1tvvdXj49IBAQAAAMohu92ufv36acqUKQoODladOnU0efJkhYaGqkePHiosLNTp06eLOx2NGzfWwYMH9corr+jRRx/ViRMnNHbsWD300EOqW7eux8elAAEAAADKqZEjR6qgoEDjxo1TTk6O2rRpo9mzZ8tutyshIUHdunXTq6++qvvvv1/Vq1fXe++9p1dffVX33HOPatSooYceekhDhw69omPanE6n06Cfp9RpPXSq1SEAAADgEnbOGmV1CBf14UHPx4zM1D9ik9UhXBHOAQEAAABgGgoQAAAAAKbhHBAAAADAA4VOvrv3BrIIAAAAwDQUIAAAAABMwwgWAAAA4IEiXf1N//BfdEAAAAAAmIYCBAAAAIBpGMECAAAAPMBVsLyDLAIAAAAwDQUIAAAAANMwggUAAAB4oJDv7r2CLAIAAAAwDQUIAAAAANMwggUAAAB4oMjJjQi9gQ4IAAAAANNQgAAAAAAwDSNYAAAAgAe4CpZ3kEUAAAAApqEAAQAAAGAaRrAAAAAADxQ5+e7eG8giAAAAANNQgAAAAAAwDSNYAAAAgAcKxY0IvYEOCAAAAADTUIAAAAAAMA0jWAAAAIAHuAqWd5BFAAAAAKahAAEAAABgGkawAAAAAA9wFSzvoAMCAAAAwDQUIAAAAABMwwgWAAAA4AGuguUdZBEAAACAaShAAAAAAJiGESwAAADAA4WMYHkFWQQAAABgGgoQAAAAAKZhBAsAAADwQBE3IvQKOiAAAAAATEMBAgAAAMA0jGABAAAAHuAqWN5BFgEAAACYhgIEAAAAgGkYwQIAAAA8UOTkKljeQAcEAAAAgGkoQAAAAACYhhEsAAAAwAOFfHfvFRQgFrk1qr6G3dtBDcOClXY2W4vX79bcNdsuun2D2jW0dMIAt/UjSafVe+KHBkbqu8ixsciv8cix8cixsciv8cgxfBEFiAVaNArT1KG99NWOA5r52Ua1alJHw+/toAo2m2av3lriPk3r1pIkxb75qfIKCorXc/IKSty+vCPHxiK/xiPHxiPHxiK/xiPH8FUUIBYYfHd77U9I1fPzVkuSNsUdU6UKFTTgzjaav26HcvML3fZpFh6ik7+ka8fBBLPD9Unk2Fjk13jk2Hjk2Fjk13jk2HxcBcs7GGQzWeVKFXVzRLi+2XXIZf3rXQfl77ArukmdEvdrFl5L+xNSzQjR55FjY5Ff45Fj45FjY5Ff45Fj+DI6ICYLr1lN9sqVdDzljMv6idQ0SVL9kBravO+4235Nw2vpSNIpzRvzoJrVDdHZrFx9vjlOsz7bpIKiIjNC9xnk2Fjk13jk2Hjk2Fjk13jkGL6MAsRkgVX8JEmZOXku61m/PvevYnfbJziwqmpW85fT6dS0ZRuUeCZDbZvV04A7YlS7RoDGzV1tfOA+hBwbi/wajxwbjxwbi/wajxxbo4jhIa+gADGZzfbr7KDTWeLrJX35cC4nV0PeXqJjyaeVfCZTkrTz4EnlFxRqeK8Omr1qq44knTYqZJ9Djo1Ffo1Hjo1Hjo1Ffo1HjuHLKONMdjY7V5Lk73D9ZqLqr88zc3Ld9snNL9TW+OPFvywuWL/niCQpIrymEaH6LHJsLPJrPHJsPHJsLPJrPHIMX2Z5B+SRRx75bxV/GR999JHB0RgvITVNBYVFqhtS3WW9bq3zz39KPOW2T/3aNRTTtK5Wb4vXuf9ptToqn/+fLy0zx7B4fRE5Nhb5NR45Nh45Nhb5NR45tkYhV8HyCss7ILfccou2bdumU6dOqU6dOpd8lAV5BYXadShBt7dq4rLePTpCGVk52ns0yW2fkOoBeu6P3dQ9OsJl/Y6YpsrMztW+48mGxuxryLGxyK/xyLHxyLGxyK/xyDF8meUdkGHDhqlq1aqaNm2a/v73vys8PNzqkAz3waqtmjWyt14fdLdWfLdXLRuF6U89YjRt+Xrl5hfK32FXo7BgnUhNV1pmtnYcSNC2/Sc0uk9nOfwq62jSaXVq3lAP3Ratt5Z+q7NZ7m3W8o4cG4v8Go8cG48cG4v8Go8cw1fZnM6LnL1kskGDBql69eqaMmWKYcdoPXSqYe99pbq2bKwhPW9R/do1lJJ+Tp/8+3vNX7dTknRzRLjeH/2Axn+4Rp9vjpMkBTjsGtzzFt3WsrFqVvNXQmq6FnyzU8s27rHyxyjVyLGxyK/xyLHxyLGxyK/xymKOd84aZXUIF/WXXX2tDqFEb0f/0+oQrkipKUCSk5MVFxenrl27GnaM0lSAAAAAwB0FyJXztQLE8hGsC2rXrq3atWtbHQYAAAAAA5WaAgQAAAAozYqcll+/qUwgiwAAAABMQwECAAAAwDSMYAEAAAAeKBQ3IvQGOiAAAAAATEMBAgAAAMA0jGABAAAAHihyMoLlDXRAAAAAAJiGAgQAAACAaRjBAgAAADzAjQi9gywCAAAAMA0FCAAAAADTMIIFAAAAeKCIGxF6BR0QAAAAAKahAAEAAABgGkawAAAAAA8UciNCr6ADAgAAAMA0FCAAAAAATMMIFgAAAOABbkToHWQRAAAAgGkoQAAAAACYhhEsAAAAwANFXAXLK+iAAAAAADANBQgAAAAA0zCCBQAAAHigSIxgeQMdEAAAAACmoQABAAAAYBpGsAAAAAAPcBUs76ADAgAAAJRTRUVFmjZtmjp16qSWLVtq4MCBOnbs2EW3T01N1ejRo9WuXTu1a9dOf/nLX5SUlHRFx6QAAQAAAMqpmTNnauHChZo0aZIWLVokm82m2NhY5eXllbj9qFGjlJiYqLlz52ru3LlKSkrSsGHDruiYFCAAAACAB4qcFUrl42rl5eVpzpw5GjFihLp06aLIyEhNnTpVycnJWrt2rdv2GRkZ2rZtm2JjYxUVFaWoqCg99thj2rt3r86cOePxcSlAAAAAgHIoPj5e586dU/v27YvXgoKCFBUVpW3btrlt7+fnp6pVq2r58uXKzMxUZmamVqxYoQYNGqhatWoeH5eT0AEAAIBy6MK5G2FhYS7rISEhSkxMdNvez89PL7/8sl588UXFxMTIZrOpVq1amj9/vipU8LyvQQECAAAAeKC0XgWrW7dul3x93bp1Ja5nZ2dLkux2u8u6n5+f0tPT3bZ3Op3av3+/oqOjNWjQIBUWFmrq1KkaPny4/vnPfyogIMCjeClAAAAAgHLI4XBIOn8uyIU/S1Jubq6qVKnitv2XX36pBQsW6F//+ldxsfHuu++qa9euWrJkifr37+/RcSlAAAAAAB92sQ7H5VwYvUpJSVG9evWK11NSUhQZGem2/Y4dO9SwYUOXTke1atXUsGFDHT161OPjchI6AAAA4IEi2Url42pFRkYqICBAW7ZsKV7LyMhQXFycYmJi3LYPCwvTsWPHlJubW7yWnZ2thIQE1a9f3+PjUoAAAAAA5ZDdble/fv00ZcoUrVu3TvHx8Ro1apRCQ0PVo0cPFRYWKjU1VTk5OZKk++67T5L0xBNPKD4+vnh7u92u+++/3+PjUoAAAAAA5dTIkSPVp08fjRs3Tn379lXFihU1e/Zs2e12JSYmqmPHjlq5cqWk81fHWrBggZxOp/r3768///nPqly5sv75z38qKCjI42PanE6n06gfqLRpPXSq1SEAAADgEnbOGmV1CBf1h43DrQ6hRMs6vGN1CFeEDggAAAAA01CAAAAAADANl+EFAAAAPFBab0Toa+iAAAAAADANBQgAAAAA0zCCBQAAAHiAESzvoAMCAAAAwDQUIAAAAABMwwgWAAAA4AFGsLyDDggAAAAA01CAAAAAADANI1gAAACAB4rECJY30AEBAAAAYBoKEAAAAACmYQQLAAAA8ABXwfIOOiAAAAAATEMBAgAAAMA0jGABAAAAHmAEyzvogAAAAAAwDQUIAAAAANMwggUAAAB4gBEs76ADAgAAAMA0FCAAAAAATMMIFgAAAOABRrC8gw4IAAAAANNQgAAAAAAwDSNYAAAAgAecjGB5BR0QAAAAAKahAAEAAABgGkawAAAAAA8UiREsb6ADAgAAAMA0FCAAAAAATMMIFgAAAOABbkToHXRAAAAAAJiGAgQAAACAaRjBAgAAADzAjQi9gw4IAAAAANNQgAAAAAAwDSNYAAAAgAe4CpZ30AEBAAAAYBoKEAAAAACmYQQLAAAA8ABXwfIOOiAAAAAATEMBAgAAAMA0jGABAAAAHuAqWN5BBwQAAACAaShAAAAAAJiGESwAAADAA06n1RGUDXRAAAAAAJiGAgQAAACAaRjBAgAAADxQJK6C5Q10QAAAAACYhgIEAAAAgGkYwQIAAAA84ORGhF5BBwQAAACAaShAAAAAAJiGESyL3BpVX8Pu7aCGYcFKO5utxet3a+6abRfdvkHtGlo6YYDb+pGk0+o98UMDI/Vd5NhY5Nd45Nh45NhY5Nd45NhcRYxgeQUFiAVaNArT1KG99NWOA5r52Ua1alJHw+/toAo2m2av3lriPk3r1pIkxb75qfIKCorXc/IKSty+vCPHxiK/xiPHxiPHxiK/xiPH8FUUIBYYfHd77U9I1fPzVkuSNsUdU6UKFTTgzjaav26HcvML3fZpFh6ik7+ka8fBBLPD9Unk2Fjk13jk2Hjk2Fjk13jkGL6Kc0BMVrlSRd0cEa5vdh1yWf9610H5O+yKblKnxP2ahdfS/oRUM0L0eeTYWOTXeOTYeOTYWOTXeOTYGk5n6Xz4GjogJguvWU32ypV0POWMy/qJ1DRJUv2QGtq877jbfk3Da+lI0inNG/OgmtUN0dmsXH2+OU6zPtukgqIiM0L3GeTYWOTXeOTYeOTYWOTXeOQYvszyAuTIkSP64osvlJ6erk6dOqlLly4ur2dmZurll1/Wq6++alGE3hVYxU+SlJmT57Ke9etz/yp2t32CA6uqZjV/OZ1OTVu2QYlnMtS2WT0NuCNGtWsEaNzc1cYH7kPIsbHIr/HIsfHIsbHIr/HIMXyZpQXIjh079Oijj6p27dpyOp36+OOP1b17d73xxhuy28//xcnJydHy5cvLTAFis/169YSL9MtK+vLhXE6uhry9RMeSTyv5TKYkaefBk8ovKNTwXh00e9VWHUk6bVTIPoccG4v8Go8cG48cG4v8Go8cW4MbEXqHpeeAvPHGG+rTp4/WrFmjr776Sm+++aY2btyoIUOGKD8/38rQDHM2O1eS5O9w/Wai6q/PM3Ny3fbJzS/U1vjjxb8sLli/54gkKSK8phGh+ixybCzyazxybDxybCzyazxyDF9maQGyf/9+9evXr/j5XXfdpffff1+7du3S2LFjLYzMOAmpaSooLFLdkOou63VrnX/+U+Ipt33q166h3p1auP2ScVQ+38BKy8wxJFZfRY6NRX6NR46NR46NRX6NR47hyywtQAICAnTmjOvJUzfffLMmT56sNWvWlJmxq/+VV1CoXYcSdHurJi7r3aMjlJGVo71Hk9z2CakeoOf+2E3doyNc1u+IaarM7FztO55saMy+hhwbi/wajxwbjxwbi/wajxxbw+m0lcqHr7H0HJAuXbroxRdf1IQJExQVFaXKlStLkrp3765nn31WkyZNUmJiopUhGuKDVVs1a2RvvT7obq34bq9aNgrTn3rEaNry9crNL5S/w65GYcE6kZqutMxs7TiQoG37T2h0n85y+FXW0aTT6tS8oR66LVpvLf1WZ7Pc26zlHTk2Fvk1Hjk2Hjk2Fvk1HjmGr7I5ndZdPTg9PV2jRo3Sd999p7///e/q3Lmzy+sLFizQK6+8osLCQu3bt++aj9d66NRrfg9v6dqysYb0vEX1a9dQSvo5ffLv7zV/3U5J0s0R4Xp/9AMa/+Eafb45TpIU4LBrcM9bdFvLxqpZzV8Jqela8M1OLdu4x8ofo1Qjx8Yiv8Yjx8Yjx8Yiv8YrizneOWuU1SFcVPPPxlsdQol+vHei1SFcEUsLkAuOHz+uGjVqKDAw0O21I0eO6KuvvtLgwYOv+TilqQABAACAu9JcgNy4YoLVIZRob68JVodwRSy/D4gk1atX76KvNWzY0CvFBwAAAADrWXoSOgAAAIDypVR0QAAAAIDSzvoTF8oGOiAAAAAATEMBAgAAAMA0jGABAAAAHvDFm/6VRnRAAAAAAJiGAgQAAACAaRjBAgAAADzACJZ30AEBAAAAYBoKEAAAAACmYQQLAAAA8AD3IfQOOiAAAAAATEMBAgAAAMA0jGABAAAAHuAqWN5BBwQAAACAaShAAAAAAJiGESwAAADAE1wGyyvogAAAAAAwDQUIAAAAANMwggUAAAB4gKtgeQcdEAAAAACmoQABAAAAyqmioiJNmzZNnTp1UsuWLTVw4EAdO3asxG2nT5+uZs2alfh45plnPD4mBQgAAADgAaezdD6uxcyZM7Vw4UJNmjRJixYtks1mU2xsrPLy8ty2HThwoDZs2ODyeOKJJ+RwONS/f3+Pj0kBAgAAAJRDeXl5mjNnjkaMGKEuXbooMjJSU6dOVXJystauXeu2vb+/v2rVqlX8yM7O1t///nc9/fTTioyM9Pi4FCAAAABAORQfH69z586pffv2xWtBQUGKiorStm3bLrv/a6+9poiICD344INXdFyuggUAAAB4oLReBatbt26XfH3dunUlriclJUmSwsLCXNZDQkKUmJh4yff88ccftW7dOn344YeqUOHKehp0QAAAAIByKDs7W5Jkt9td1v38/JSbm3vJfefNm6eWLVu6dE88RQcEAAAA8GEX63BcjsPhkHT+XJALf5ak3NxcValS5aL7ZWVlae3atRo/fvxVHZcOCAAAAOAJp610Pq7ShdGrlJQUl/WUlBSFhoZedL/169erqKhIPXr0uKrjUoAAAAAA5VBkZKQCAgK0ZcuW4rWMjAzFxcUpJibmovvt2LFDN954o4KCgq7quIxgAQAAAOWQ3W5Xv379NGXKFAUHB6tOnTqaPHmyQkND1aNHDxUWFur06dMKDAx0GdGKj49X06ZNr/q4dEAAAAAAD1h9w0EjbkQ4cuRI9enTR+PGjVPfvn1VsWJFzZ49W3a7XYmJierYsaNWrlzpss8vv/yi6tWrX/UxbU7ntYbtO1oPnWp1CAAAALiEnbNGWR3CRTX65ytWh1Cin/o+a3UIV4QOCAAAAADTcA4IAAAA4IlyMzdkLDogAAAAAExDAQIAAADANIxgAQAAAB5wXsNN//BfdEAAAAAAmIYCBAAAAIBpGMECAAAAPMFVsLyCDggAAAAA01CAAAAAADANI1gAAACAB7gKlnfQAQEAAABgGgoQAAAAAKZhBAsAAADwBFfB8go6IAAAAABMU646INf9fZPVIZRp6SsjrA6hzCtYUcvqEMq0Sr1SrQ6hzOMzDAAoVwUIAAAAcPW4CpY3MIIFAAAAwDQUIAAAAABMwwgWAAAA4AmuguUVdEAAAAAAmIYCBAAAAIBpGMECAAAAPMEIllfQAQEAAABgGgoQAAAAAKZhBAsAAADwhJMbEXoDHRAAAAAApqEAAQAAAGAaRrAAAAAADzi5CpZX0AEBAAAAYBoKEAAAAACmYQQLAAAA8AQjWF5BBwQAAACAaShAAAAAAJiGESwAAADAE9yI0CvogAAAAAAwDQUIAAAAANMwggUAAAB4wMZVsLyCDggAAAAA01CAAAAAADANI1gAAACAJxjB8go6IAAAAABMQwECAAAAwDSMYAEAAACe4EaEXkEHBAAAAIBpKEAAAAAAmIYRLAAAAMATXAXLK+iAAAAAADANBQgAAAAA0zCCBQAAAHiCESyvoAMCAAAAwDQUIAAAAABMwwgWAAAA4AlGsLyCDggAAAAA01CAAAAAADANI1gAAACAJ5w2qyMoE+iAAAAAADANBQgAAAAA0zCCBQAAAHjAxlWwvIIOCAAAAADTUIAAAAAAMA0jWAAAAIAnGMHyCjogAAAAAExDB8QkMXe20p9fekj1osKVnpqhL/7+lRa+tvyi21eqXEl9nuypHn+6TbXqXqdfEk7rmwXrtfC15SrILyjebtHP7ys4tLrb/v8XFqszyWne/0FKqfY1IzQ0oocaBoToTN45LT2xVR/+9J8St727TmuNb97nou81cfen+vLnXZKkVV2f0XV+gW7b3PXNKzqVl+md4H3UrVH1NezeDmoYFqy0s9lavH635q7ZdtHtG9SuoaUTBritH0k6rd4TPzQwUt/AZ9gafI6NRX6NR47hiyhATBB1S1O9uOIp/WfRJs19fqFu6hipP0/qqwoVKmjBK0tL3GfoWwPU409d9PGkJTqw7ZCatG6kR8Y/oJD6tfTmoFmSpOoh1RQcWl2zRs/Tvu8OuOyfceqs4T9XadG8ej290foRrU38UbMOrlWrGg00NKKHKsimuT/92237jSn7NfC7WS5rNpv07I33y7+Snzamns9lsD1A1/kFauq+L/Vj2nGX7dPyswz7eXxBi0Zhmjq0l77acUAzP9uoVk3qaPi9HVTBZtPs1VtL3Kdp3VqSpNg3P1VewX+L6Jy8ghK3L0/4DFuDz7GxyK/xyDF8FQWICR554QEd/v6oXu8/XZK0fc33qlS5oh586j4tfvML5eXkuWwfWCNAPQf30AdPf6xPp3wmSdr1zR5J0mN/e0Szn/5Y6b9kqEl0Q0nShqVblHL8FxN/otIltsntOpCRqAk/fipJ2vzLQVWyVdCfGnXRgqMblFvk+ks1Lf+c0tLPuaw9WP9WNQiopUGb31Va/vnXmgaFSZL+lbxXSTlpxv8gPmTw3e21PyFVz89bLUnaFHdMlSpU0IA722j+uh3KzS9026dZeIhO/pKuHQcTzA631OMzbA0+x8Yiv8Yjx/BVV3QOSGpqqiZMmKBHH31UTz31lObNm6ft27crOzvbqPh8XmV7JbW47UZtWLbFZf3bxZtVNbCKmneKdNvHv1pVffH3tfrus+0u6wkHfpYkhTUKkSQ1btVAZ89kluvio7KtoloHN9K/kve6rK9L2iP/Sn5qVaPBZd/jOnuAhkT00JLjW7Q3/b+/kJsGhikjP5t/uP1G5UoVdXNEuL7Zdchl/etdB+XvsCu6SZ0S92sWXkv7E1LNCNGn8Bm2Bp9jY5Ff45Fj+LIr6oA8++yz2rBhgyIiIpSQkKDPP/9cTqdTFSpUUKNGjXTTTTepefPmat68uSIjI1W5cmWj4vYZYY1qy+5XWSd/LR4u+PlQkiQpvOn12rF2t8trSUdTNH34B27v1fEP7ZSfV6CEA4mSpMYtGyjzzDmNX/xXRXdrrgoVK2jzFzv07uh5Op2UZswPVMrUqRose4VKOp51ymU94dfn9fxrasupQyXtWuyxiO4qchbp3YNrXdabBl2vs/nZer3Vw2pzXWNVsNm0IXW/psZ/qVO55WfE7bfCa1aTvXIlHU8547J+IjVNklQ/pIY27zvutl/T8Fo6knRK88Y8qGZ1Q3Q2K1efb47TrM82qaCoyIzQSyU+w9bgc2ws8ms8cmwNbkToHVdUgOzatUtjxozRwIEDJUlZWVnau3evfvzxR/3444/atm2bli1bJkmy2+3avXv3pd5OkpSbm6uDBw+qSZMmcjgc2rdvn+bPn6/k5GRFRESof//+Cg0NvYofrXTwr+4vSTqX4dolyjp7/nnVoCoevU/H+9up+yOdtWzaSmWmnR+vaNyqgWqGX6eVH3ytJW99oXo3hKv/xAf1xr8namjrscrJyvXiT1I6BVZ2SJLOFeS4rGcVnh9r86/kd8n9a9j99fvro/Xx0Q3K/M17NA0MU4gjSMsTtumfxzaqgX8tDY7orr+3jVW/TdOVU5jvxZ/EdwRWOZ/TzN+MDmb9+ty/it1tn+DAqqpZzV9Op1PTlm1Q4pkMtW1WTwPuiFHtGgEaN3e18YGXUnyGrcHn2Fjk13jkGL7sigoQPz8/RUVFFT+vWrWq2rRpozZt2hSvpaWlaffu3dqzZ89l3+/w4cMaMGCAUlNTdf3112vSpEkaNmyYwsPD1bhxY3399ddaunSpFixYoMaNG19JqKVGhQq2839wllwyFxVdvpTu1Lu9np4/Urv/E6fZT39cvD5l4Ezl5eTp8PdHJUl7NsTr2N4TemvDJHX/Uxd98e5X1xx/aWfThfyW/HrRRfJ+wX3hbVTBZtPCo5vcXntpz2LlFhbowNnzHafvzxzVT5nJ+qD9EN19fWstObHFbZ/ywGa73Gfafe1cTq6GvL1Ex5JPK/nM+Ssv7Tx4UvkFhRreq4Nmr9qqI0mnjQq5VOMzbA0+x8Yiv8Yjx/BlV3QOSPfu3RUXF3fJbapXr67OnTtr2LBhl32/v/3tb4qOjtby5ct18803a+jQobrnnnv0+eef6+2339aqVavUoUMHvfrqq1cSZqlyoVtRNaiqy3rVwPOdj3Ppl74STe9RPfXcwlHauyFez9/7uvL/5yoV+zYfKC4+Lti7ab8y086pccv6Xoi+9Lvwje9vvyWuWvH8Nz/nCi7dBbo99CZt+eVQ8Um7/+vHtBPF/3C7YHfacZ3Nz1ZEoO925a7V2ezzOfV3uH67VvXX55k57jnPzS/U1vjjxf/Bu2D9niOSpIjwmkaE6hP4DFuDz7GxyK/xyLFFnLbS+fAxV1SA9O7dW6tWrdKhQ5eeR/bU1q1b9cQTTygyMlJPPfWUcnNz1bdv3+KqvlKlShoyZIh27NjhleNZ4efDySosKNT1TVz/Y3/h+fG4i1+FYvi0gRryRn+tX7xZz939inLO/Xe8wr9aVd35566qd0O4236V7JWU/kv5mO9OyDqtgqJChVe9zmX9wvOfMlMuum+IX5CaBV2vr5N+dHstoJJD99S5WQ39Q9xeq1yhYrm+hGlCapoKCotUN6S6y3rdWuef/5R4ym2f+rVrqHenFm7/oXRUPt+ETcvMcdunvOAzbA0+x8Yiv8Yjx/BlV1SA/N///Z/27NmjBx54QM8884xWrlypY8eOXfXBHQ6HcnLOf9hr1qyp//u//5Ofn+u3gBkZGQoMdL+Jlq/Iz83X7m/3qeMf2rmsd+7TXmfPZCp+a8nF3MBX/qj7Hr9Li6d+oZf7TnXpfEhSQV6BRr4zSA89dZ/L+q292shR1U8//Nv1ijplVV5Rgb4/c1RdQ290We8WepMy8rMVl37iovtGVa8rSfrhjPtnOL+oUGOj7lX/Rp1d1ruE3CBHRbt2nP7JC9H7pryCQu06lKDbWzVxWe8eHaGMrBztPZrktk9I9QA998du6h4d4bJ+R0xTZWbnat/xZENjLs34DFuDz7GxyK/xyDF82RWdAzJp0iTt27dPe/fu1apVq7Rs2TLZbDb5+/srKipKN910k8aOHevx+3Xs2FEvvfSSJk2apMaNG+vFF18sfs3pdGrr1q2aOHGiunfvfiVhljoLXl6i19c+r+cXjdbqud8o6tZmeuCv9+qDpz9WXk6eqgZWUf2ocP18OFnpv2SoccsGenBsL+3fdkjffrJJN7Rz/UVxLC5BWWez9cnkz9Tv+T46k5Ku7at3qWGL+vrT+P/T5i92aNc6929Ey6o5h/+lGW0G6tVWffVZwg61qF5P/Rp20oz9a5RbVCD/in5qGBCihKzTLmMqTQJqK7cwXyez3eddc4vy9Y8j6zWoye06lZepzakH1SQwVLFNumlDSry2nTps5o9Y6nywaqtmjeyt1wfdrRXf7VXLRmH6U48YTVu+Xrn5hfJ32NUoLFgnUtOVlpmtHQcStG3/CY3u01kOv8o6mnRanZo31EO3Reutpd/qbDm4YMKl8Bm2Bp9jY5Ff45FjC3AVLK+wOZ2XOcPxIoqKinT48GHt3btXe/bsUVxcnOLj47Vz506P3+P06dMaMmSI6tatqzfeeMPltS+//FJPPvmkOnXqpKlTpyogIOBqwnTRo8ID1/weV6vDfW31pwn/p/Bm1+vUydP6bOZqLX7zC0lSiy5ReuNfEzX5z+/oqw//rf4TH1S/5/tc9L2e7Dpeu/8TJ5vNpnuG3qGeQ+7Q9Y1DlXHqrL755wZ9NP4Tt5sbmiF9ZcTlNzLIbSFRio3opvr+tZSak6FPj2/WgqMbJEmtgxvq3baxmvjjYn158r+fz7FR96pr7Rt1179KPsfIJpt612un3nXbqU7VYKXnZ+mrn3/Qe4e+drsxnFkKVtSy5Lgl6dqysYb0vEX1a9dQSvo5ffLv7zV/3fn83hwRrvdHP6DxH67R55vPnzcW4LBrcM9bdFvLxqpZzV8Jqela8M1OLdt4+QtWmKVSL+uujc9n2Bpl8XNcmpBf45XFHO+cNcrqEC6q0VtvWh1CiX56YrTVIVyRqy5ASuJ0Ov97VYYrkJaWpurVq7usnT59WikpKYqMdL9R39WysgApD6wsQMqL0vaPt7LGygKkvOAzDOByKECunK8VIFc0gnU5V1N8SHIrPiQpODhYwcHB1xgRAAAA4CWMYHnFFZ2EDgAAAADXggIEAAAAgGm8OoIFAAAAlFU2RrC8gg4IAAAAANNQgAAAAAAwDSNYAAAAgCcYwfIKOiAAAAAATEMBAgAAAMA0jGABAAAAnmAEyyvogAAAAAAwDQUIAAAAANMwggUAAAB4gBsRegcdEAAAAACmoQABAAAAYBoKEAAAAMATTlvpfFyDoqIiTZs2TZ06dVLLli01cOBAHTt27KLb5+fn64033lCnTp3UqlUr9evXT/v27buiY1KAAAAAAOXUzJkztXDhQk2aNEmLFi2SzWZTbGys8vLyStx+woQJWrx4sV566SUtWbJE1atXV2xsrM6ePevxMSlAAAAAgHIoLy9Pc+bM0YgRI9SlSxdFRkZq6tSpSk5O1tq1a922P3HihBYvXqxXX31Vt912mxo3bqxXXnlFdrtde/bs8fi4FCAAAACAJ5yl9HGV4uPjde7cObVv3754LSgoSFFRUdq2bZvb9hs2bFBQUJA6d+7ssv0333yjW265xePjchleAAAAwId169btkq+vW7euxPWkpCRJUlhYmMt6SEiIEhMT3bY/evSo6tatq6+++krvvfeekpOTFRUVpaefflqNGzf2OF46IAAAAEA5lJ2dLUmy2+0u635+fsrNzXXbPjMzU8ePH9fMmTM1evRozZo1S5UqVdIf//hHnTp1yuPj0gEBAAAAPFBab0R4sQ7H5TgcDknnzwW58GdJys3NVZUqVdy2r1y5ss6ePaupU6cWdzymTp2qLl26aNmyZRo0aJBHx6UDAgAAAJRDF0avUlJSXNZTUlIUGhrqtn1oaKgqVarkMm7lcDhUt25dJSQkeHxcChAAAACgHIqMjFRAQIC2bNlSvJaRkaG4uDjFxMS4bR8TE6OCggL9+OOPxWs5OTk6ceKE6tev7/FxGcECAAAAPFFKR7Cult1uV79+/TRlyhQFBwerTp06mjx5skJDQ9WjRw8VFhbq9OnTCgwMlMPhUExMjG699VY99dRTevHFF1W9enVNmzZNFStWVK9evTw+Lh0QAAAAoJwaOXKk+vTpo3Hjxqlv376qWLGiZs+eLbvdrsTERHXs2FErV64s3n769Olq27atHn/8cfXp00eZmZn66KOPFBwc7PExbU6ns4zVchfXo8IDVodQpqWvjLA6hDKvYEUtq0Mo0yr1SrU6hDKPzzCAy9k5a5TVIVxUxKtTrQ6hRAefKb05KwkjWAAAAIAHSutVsHwNI1gAAAAATEMBAgAAAMA0jGABAAAAnmAEyyvogAAAAAAwDQUIAAAAANMwggUAAAB4ghEsr6ADAgAAAMA0FCAAAAAATMMIFgAAAOABbkToHXRAAAAAAJiGAgQAAACAaShAAAAAAJiGAgQAAACAaShAAAAAAJiGq2ABAAAAnuAqWF5BBwQAAACAaShAAAAAAJiGESwAAADAA9yI0DvogAAAAAAwDQUIAAAAANMwggUAAAB4ghEsryhXBcipwbdaHULZtsLqAMq+7eNnWR0CcE1iVgy1OgQAgMUYwQIAAABgmnLVAQEAAACuGiNYXkEHBAAAAIBpKEAAAAAAmIYRLAAAAMAD3IjQO+iAAAAAADANBQgAAAAA0zCCBQAAAHiCESyvoAMCAAAAwDQUIAAAAABMwwgWAAAA4AGuguUddEAAAAAAmIYCBAAAAIBpGMECAAAAPMEIllfQAQEAAABgGgoQAAAAAKZhBAsAAADwBCNYXkEHBAAAAIBpKEAAAAAAmIYRLAAAAMAD3IjQO+iAAAAAADANBQgAAAAA0zCCBQAAAHiCESyvoAMCAAAAwDQUIAAAAABMwwgWAAAA4AlGsLyCDggAAAAA01CAAAAAADANI1gAAACAB7gRoXfQAQEAAABgGgoQAAAAAKZhBAsAAADwBCNYXkEHBAAAAIBpKEAAAAAAmIYRLAAAAMADXAXLO+iAAAAAADANBQgAAAAA0zCCBQAAAHiCESyvoAMCAAAAwDQUIAAAAABMwwgWAAAA4AlGsLyCDggAAAAA01CAAAAAADANBQgAAAAA03AOCAAAAOABm9UBlBF0QAAAAACYhgIEAAAAgGkYwbLIrVH1NezeDmoYFqy0s9lavH635q7ZdtHtG9SuoaUTBritH0k6rd4TPzQwUt9Fjq2XmCL1+rM0Y5LUNtrqaMomcnxt+D1hLPJrPHJsMi7D6xUUIBZo0ShMU4f20lc7DmjmZxvVqkkdDb+3gyrYbJq9emuJ+zStW0uSFPvmp8orKChez8krKHH78o4cW+9kkhQ7RjqbaRO/sY1Bjq8NvyeMRX6NR47hqyhALDD47vban5Cq5+etliRtijumShUqaMCdbTR/3Q7l5he67dMsPEQnf0nXjoMJZofrk8ixdYqKpOWrpb/NsjqSsoscewe/J4xFfo1HjuGrOAfEZJUrVdTNEeH6Ztchl/Wvdx2Uv8Ou6CZ1StyvWXgt7U9INSNEn0eOrbX/sDRxqnTfndLrz1kdTdlEjq8dvyeMRX6NR46tYXOWzoevoQNisvCa1WSvXEnHU864rJ9ITZMk1Q+poc37jrvt1zS8lo4kndK8MQ+qWd0Qnc3K1eeb4zTrs00qKCoyI3SfQY6tFVZbWvOxFBoibd1ldTRlEzm+dvyeMBb5NR45hi8rtQXIPffco/fee09hYWFWh+JVgVX8JEmZOXku61m/PvevYnfbJziwqmpW85fT6dS0ZRuUeCZDbZvV04A7YlS7RoDGzV1tfOA+hBxbq3qQpCCroyjbyPG14/eEsciv8cgxfJmlBcjy5csv+tqxY8e0atUqBQcHS5Luu+8+c4IymM326y1snCX3y0r68uFcTq6GvL1Ex5JPK/lMpiRp58GTyi8o1PBeHTR71VYdSTptVMg+hxwDuBx+TxiL/BqPHFvEB8edSiNLC5CJEycqJydHkuQs4S/Q3/72N0nn/5KVlQLkbHauJMnf4frNRNVfn2fm5Lrtk5tfqK3x7m3U9XuOaHivDooIr8kvjP9BjgFcDr8njEV+jUeO4cssPQl96dKlioqKUrt27fSf//xH8fHxxY8qVapo7dq1io+P1759+6wM06sSUtNUUFikuiHVXdbr1jr//KfEU2771K9dQ707tXD7JeOofL5+TMvMMSRWX0WOAVwOvyeMRX6NR47hyywtQBo2bKhFixapRYsW6tWrl1auXGllOKbIKyjUrkMJur1VE5f17tERysjK0d6jSW77hFQP0HN/7Kbu0REu63fENFVmdq72HU82NGZfQ44BXA6/J4xFfo1Hji3iLKUPH2P5SeiVKlXS6NGj1alTJz311FNat26dJkyYYHVYhvpg1VbNGtlbrw+6Wyu+26uWjcL0px4xmrZ8vXLzC+XvsKtRWLBOpKYrLTNbOw4kaNv+Exrdp7McfpV1NOm0OjVvqIdui9ZbS7/V2Sz3Nmt5R44BXA6/J4xFfo1HjuGrbM6STr6wSEZGhiZOnKjt27fr1KlTWrVqlerWreu19289dKrX3utadW3ZWEN63qL6tWsoJf2cPvn395q/bqck6eaIcL0/+gGN/3CNPt8cJ0kKcNg1uOctuq1lY9Ws5q+E1HQt+Ganlm3cY+WPUaqVxRxvH+9bd57bukvq/4RNH77lVNtoq6Mpm3wtxzETh1odgouy+HuiNCG/xiuLOd45a5TVIVxUy5Gl59+S/+uHaaU3ZyUpVQXIBcuXL9fSpUs1ZcoUhYSEeO19S1MBAlwNXytAgN8qbQUIgNKnNBcgrUaUzn9Lfj+99OasJJaPYJXkvvvuKzNXvQIAAADwX5aehA4AAACgfCmVHRAAAACg1Cl1Jy74JjogAAAAAExDAQIAAADANIxgAQAAAB6wMYLlFXRAAAAAAJiGAgQAAAAop4qKijRt2jR16tRJLVu21MCBA3Xs2LGLbr9s2TI1a9bM7XGpfX6LESwAAADAE2VwBGvmzJlauHChXn31VdWuXVuTJ09WbGysvvjiC9ntdrft9+/fr7Zt2+rNN990WQ8ODvb4mHRAAAAAgHIoLy9Pc+bM0YgRI9SlSxdFRkZq6tSpSk5O1tq1a0vc58CBA4qMjFStWrVcHhUrVvT4uBQgAAAAQDkUHx+vc+fOqX379sVrQUFBioqK0rZt20rcZ//+/WrSpMk1HZcCBAAAAPCAzVk6H1crKSlJkhQWFuayHhISosTERLftT58+rV9++UXbtm1Tz5491bFjRw0fPlxHjhy5ouNyDggAAADgw7p163bJ19etW1fienZ2tiS5nevh5+en9PR0t+0PHDggSapYsaJef/11ZWVlaebMmfrjH/+ozz//XDVr1vQoXgoQAAAAoBxyOBySzp8LcuHPkpSbm6sqVaq4bd++fXtt3bpV1apVK15755131LVrVy1dulSPPfaYR8elAAEAAAA8UUqvgnWxDsflXBi9SklJUb169YrXU1JSFBkZWeI+/1t8SFLVqlUVHh6u5ORkj4/LOSAAAABAORQZGamAgABt2bKleC0jI0NxcXGKiYlx237BggVq166dcnJyitcyMzN19OjRKzoxnQIEAAAAKIfsdrv69eunKVOmaN26dYqPj9eoUaMUGhqqHj16qLCwUKmpqcUFR9euXeV0OjV27FgdPHhQP/74o0aMGKHg4GD94Q9/8Pi4FCAAAACAJ5yl9HENRo4cqT59+mjcuHHq27evKlasqNmzZ8tutysxMVEdO3bUypUrJZ0f2frwww917tw59e3bVwMGDFBgYKA++ugjl3NILodzQAAAAIByqmLFihozZozGjBnj9lp4eLj279/vsnbDDTdo9uzZ13RMOiAAAAAATEMHBAAAAPDAtdz0D/9FBwQAAACAaShAAAAAAJiGESwAAADAE4xgeQUdEAAAAACmoQABAAAAYBpGsAAAAAAP2JzMYHkDHRAAAAAApqEAAQAAAGAaRrAAAAAATzCB5RV0QAAAAACYhgIEAAAAgGkYwQIAAAA8YGMEyyvogAAAAAAwDQUIAAAAANMwggUAAAB4ghEsr6ADAgAAAMA0FCAAAAAATMMIFgAAAOABroLlHXRAAAAAAJiGAgQAAACAaRjBAgAAADzBCJZX0AEBAAAAYBoKEAAAAACmYQQLAAAA8ABXwfIOOiAAAAAATEMBAgAAAMA0jGABAAAAnmAEyyvogAAAAAAwTbnqgFTqlWp1CMA1af99H6tDKNMKVtSyOgQAAMq8clWAAAAAAFeLq2B5ByNYAAAAAExDAQIAAADANIxgAQAAAJ5wMoPlDXRAAAAAAJiGAgQAAACAaRjBAgAAADzAVbC8gw4IAAAAANNQgAAAAAAwDSNYAAAAgCcYwfIKOiAAAAAATEMBAgAAAMA0jGABAAAAHrAVWR1B2UAHBAAAAIBpKEAAAAAAmIYRLAAAAMATXAXLK+iAAAAAADANBQgAAAAA0zCCBQAAAHjAxgiWV9ABAQAAAGAaChAAAAAApmEECwAAAPCEkxksb6ADAgAAAMA0FCAAAAAATMMIFgAAAOABroLlHXRAAAAAAJiGAgQAAACAaRjBAgAAADzBCJZX0AEBAAAAYBoKEAAAAACmYQQLAAAA8ABXwfIOOiAAAAAATEMBAgAAAMA0jGABAAAAnnAyg+UNdEAAAAAAmIYCBAAAAIBpGMECAAAAPMBVsLyDDggAAAAA01CAAAAAADANI1gAAACAJxjB8go6IAAAAABMQwECAAAAwDSMYAEAAAAe4CpY3kEHBAAAAIBpKEAAAAAAmIYRLAAAAMATRcxgeQMdEAAAAACmoQNikvY1IzQ0oocaBoToTN45LT2xVR/+9J8St727TmuNb97nou81cfen+vLnXZKkVV2f0XV+gW7b3PXNKzqVl+md4H0A+TUeOTbfrVH1NezeDmoYFqy0s9lavH635q7ZdtHtG9SuoaUTBritH0k6rd4TPzQwUt9Fjo1Ffo1HjuGLKEBM0Lx6Pb3R+hGtTfxRsw6uVasaDTQ0oocqyKa5P/3bbfuNKfs18LtZLms2m/TsjffLv5KfNqYekCQF2wN0nV+gpu77Uj+mHXfZPi0/y7Cfp7Qhv8Yjx+Zr0ShMU4f20lc7DmjmZxvVqkkdDb+3gyrYbJq9emuJ+zStW0uSFPvmp8orKChez8krKHH78o4cG4v8Go8cW4AJLK+gADFBbJPbdSAjURN+/FSStPmXg6pkq6A/NeqiBUc3KLfI9S99Wv45paWfc1l7sP6tahBQS4M2v6u0/POvNQ0KkyT9K3mvknLSjP9BSinyazxybL7Bd7fX/oRUPT9vtSRpU9wxVapQQQPubKP563YoN7/QbZ9m4SE6+Uu6dhxMMDtcn0SOjUV+jUeO4as4B8RglW0V1Tq4kf6VvNdlfV3SHvlX8lOrGg0u+x7X2QM0JKKHlhzfor3p//2F0TQwTBn52eX6H27k13jk2HyVK1XUzRHh+mbXIZf1r3cdlL/DrugmdUrcr1l4Le1PSDUjRJ9Hjo1Ffo1HjuHL6IAYrE7VYNkrVNLxrFMu6wm/Pq/nX1NbTh0qaddij0V0V5GzSO8eXOuy3jToep3Nz9brrR5Wm+saq4LNpg2p+zU1/kudyj3r3R+klCK/xiPH5guvWU32ypV0POWMy/qJ1DRJUv2QGtq877jbfk3Da+lI0inNG/OgmtUN0dmsXH2+OU6zPtukgqIiM0L3GeTYWOTXeOTYGtyI0DssLUAWL16se++9V3a7vXht8+bNmjNnjpKSkhQREaGhQ4eqSZMmFkZ5bQIrOyRJ5wpyXNazCvMkSf6V/C65fw27v35/fbQ+PrpBmb95j6aBYQpxBGl5wjb989hGNfCvpcER3fX3trHqt2m6cgrzvfiTlE7k13jk2HyBVc7nNDMnz2U969fn/lXsbvsEB1ZVzWr+cjqdmrZsgxLPZKhts3oacEeMatcI0Li5q40P3IeQY2ORX+ORY/gySwuQ559/Xl27dtV1110nSdqwYYNiY2PVoUMHdezYUXv27FHv3r01d+5ctW7d2spQr5pNtvN/uEjFXOS8dCl9X3gbVbDZtPDoJrfXXtqzWLmFBTpwNlGS9P2Zo/opM1kftB+iu69vrSUntlxT7L6A/BqPHJvPZruQ85JzW9KXlOdycjXk7SU6lnxayWfOXz1s58GTyi8o1PBeHTR71VYdSTptVMg+hxwbi/wajxzDl1l6DojzN39pZs6cqT/96U/64IMPNHbsWH300Ud66KGHNGXKFIsivHYXvvH97bfEVSue/2biXEHuJfe/PfQmbfnlUPFJu//rx7QTxf9wu2B32nGdzc9WRGDotYTtM8iv8cix+c5mn8+pv8P1G8yqvz7PzHHPeW5+obbGHy/+R8UF6/cckSRFhNc0IlSfRY6NRX6NR44t4nSWzoePKVUnoR87dky9evVyWXvwwQcVFxdnUUTXLiHrtAqKChVe9TqX9QvPf8pMuei+IX5BahZ0vb5O+tHttYBKDt1T52Y19A9xe61yhYrl5hKm5Nd45Nh8CalpKigsUt2Q6i7rdWudf/5T4im3ferXrqHenVq4/WPEUfl8ozstM8dtn/KMHBuL/BqPHMOXWVqAFLcPf9WgQQNlZbn+o+PMmTMKDHS/SZmvyCsq0Pdnjqpr6I0u691Cb1JGfrbi0k9cdN+o6nUlST+cOeb2Wn5RocZG3av+jTq7rHcJuUGOinbtOP2TF6Iv/civ8cix+fIKCrXrUIJub+V6/lv36AhlZOVo79Ekt31CqgfouT92U/foCJf1O2KaKjM7V/uOJxsas68hx8Yiv8Yjx/Bllp4D4nQ61a1bNzVs2FCNGzeW3W7X5MmTNX/+fFWuXFk7d+7UxIkT1aVLFyvDvGZzDv9LM9oM1Kut+uqzhB1qUb2e+jXspBn71yi3qED+Ff3UMCBECVmnXcZUmgTUVm5hvk5mu89j5hbl6x9H1mtQk9t1Ki9Tm1MPqklgqGKbdNOGlHhtO3XYzB/RUuTXeOTYfB+s2qpZI3vr9UF3a8V3e9WyUZj+1CNG05avV25+ofwddjUKC9aJ1HSlZWZrx4EEbdt/QqP7dJbDr7KOJp1Wp+YN9dBt0Xpr6bc6m3XpUbnyiBwbi/wajxybj6tgeYfN+dsTMUz0888/a//+/Tpw4EDx/z969Ki2b98uh8Oh6OhoNWvWTLNmzVKNGjWu+XhtVz/rhaivzm0hUYqN6Kb6/rWUmpOhT49v1oKjGyRJrYMb6t22sZr442J9eXJn8T5jo+5V19o36q5/vVrie9pkU+967dS7bjvVqRqs9PwsffXzD3rv0NduN4Yr68iv8cpDjgtW1DL9mJfStWVjDel5i+rXrqGU9HP65N/fa/668/m9OSJc749+QOM/XKPPN58fUw1w2DW45y26rWVj1azmr4TUdC34ZqeWbdxj5Y9RqpFjY5Ff45XFHO+cNcrqEC6q652vWx1Cif615imrQ7gilhYgJcnPz1flypUlSfv371fTpk3dRrWulpUFCIDSr7QVIABQHlGAXDlfK0BK1UnokoqLD0lq1qyZ14oPAAAA4Jo4S+njGhQVFWnatGnq1KmTWrZsqYEDB+rYMfdzN0vy+eefq1mzZkpISLiiY5a6AgQAAACAOWbOnKmFCxdq0qRJWrRokWw2m2JjY5WXl3fJ/U6ePKmJEyde1TEpQAAAAIByKC8vT3PmzNGIESPUpUsXRUZGaurUqUpOTtbatWsvul9RUZHGjBmjG2+88aLbXAoFCAAAAOABm9NZKh9XKz4+XufOnVP79u2L14KCghQVFaVt27ZddL93331X+fn5Gjx48FUd19LL8AIAAAC4Nt26dbvk6+vWrStxPSnp/P1iwsLCXNZDQkKUmJhY4j67d+/WnDlztHjxYiUnX929Y+iAAAAAAOVQdna2JMlut7us+/n5KTfX/b4wWVlZ+utf/6q//vWvatCgwVUflw4IAAAA4IkiqwMo2cU6HJfjcDgknT8X5MKfJSk3N1dVqlRx237SpElq0KCBHnrooasL9FcUIAAAAEA5dGH0KiUlRfXq1SteT0lJUWRkpNv2S5Yskd1uV3R0tCSpsLBQktSzZ0/de++9evHFFz06LgUIAAAAUA5FRkYqICBAW7ZsKS5AMjIyFBcXp379+rlt/9VXX7k8/+GHHzRmzBi99957aty4scfHpQABAAAAPHAtV5wqjex2u/r166cpU6YoODhYderU0eTJkxUaGqoePXqosLBQp0+fVmBgoBwOh+rXr++y/4WT2K+//npdd911Hh+Xk9ABAACAcmrkyJHq06ePxo0bp759+6pixYqaPXu27Ha7EhMT1bFjR61cudKrx7Q5nWWslLuEtquftToEAKVYwYpaVocAAOXezlmjrA7horrd/qrVIZRo3TfPWB3CFWEECwAAAPBEufna3liMYAEAAAAwDQUIAAAAANMwggUAAAB4ovycOm0oOiAAAAAATEMBAgAAAMA0jGABAAAAHrAxgeUVdEAAAAAAmIYCBAAAAIBpGMECAAAAPMFVsLyCDggAAAAA01CAAAAAADANI1gAAACAB2xFVkdQNtABAQAAAGAaChAAAAAApmEECwAAAPAEV8HyCjogAAAAAExDAQIAAADANIxgAQAAAJ5gAssr6IAAAAAAMA0FCAAAAADTMIIFAAAAeMDGVbC8gg4IAAAAANNQgAAAAAAwDSNYAAAAgCcYwfIKOiAAAAAATEMBAgAAAMA0jGABAAAAniiyOoCygQ4IAAAAANNQgAAAAAAwDSNYAAAAgAe4EaF30AEBAAAAYBoKEAAAAACmYQQLAAAA8AQjWF5BBwQAAACAaShAAAAAAJiGESwAAADAE4xgeUW5KkAKVtSyOgQAAACgXGMECwAAAIBpylUHBAAAALhqRVYHUDbQAQEAAABgGgoQAAAAAKZhBAsAAADwgI2rYHkFHRAAAAAApqEAAQAAAGAaRrAAAAAATzCC5RV0QAAAAACYhgIEAAAAgGkYwQIAAAA8wQiWV9ABAQAAAGAaChAAAAAApmEECwAAAPAEI1heQQcEAAAAgGkoQAAAAACYhhEsAAAAwBNFVgdQNtABAQAAAGAaChAAAAAApmEECwAAAPCAjatgeQUdEAAAAACmoQABAAAAYBpGsAAAAABPMILlFXRAAAAAAJiGAgQAAACAaRjBAgAAADxRxAiWN9ABAQAAAGAaChAAAAAApmEECwAAAPAEV8HyCjogAAAAAExDAQIAAADANIxgAQAAAJ5gBMsr6IAAAAAAMA0FCAAAAADTMIIFAAAAeIIRLK+gAwIAAADANBQgAAAAAEzDCBYAAADgiSJGsLyBDggAAAAA01CAAAAAADANI1gAAACAJ5xFVkdQJtABAQAAAGAaChAAAAAApmEECwAAAPAENyL0CjogAAAAAExDAQIAAADANIxgAQAAAJ7gRoReQQFikVuj6mvYvR3UMCxYaWeztXj9bs1ds+2i2zeoXUNLJwxwWz+SdFq9J35oYKS+ixwbi/wajxwbjxwbi/wajxzDF1GAWKBFozBNHdpLX+04oJmfbVSrJnU0/N4OqmCzafbqrSXu07RuLUlS7JufKq+goHg9J6+gxO3LO3JsLPJrPHJsPHJsLPJrPHIMX0UBYoHBd7fX/oRUPT9vtSRpU9wxVapQQQPubKP563YoN7/QbZ9m4SE6+Uu6dhxMMDtcn0SOjUV+jUeOjUeOjUV+jUeOLcBVsLyCk9BNVrlSRd0cEa5vdh1yWf9610H5O+yKblKnxP2ahdfS/oRUM0L0eeTYWOTXeOTYeOTYWOTXeOQYvowOiMnCa1aTvXIlHU8547J+IjVNklQ/pIY27zvutl/T8Fo6knRK88Y8qGZ1Q3Q2K1efb47TrM82qaCoyIzQfQY5Nhb5NR45Nh45Nhb5NR45hi+zvAD54YcftGXLFj322GOSpM2bN2vevHlKSEhQvXr1NHDgQMXExFgcpfcEVvGTJGXm5LmsZ/363L+K3W2f4MCqqlnNX06nU9OWbVDimQy1bVZPA+6IUe0aARo3d7XxgfsQcmws8ms8cmw8cmws8ms8cmwRRrC8wtICZPXq1Ro9erRuvfVWPfbYY/rXv/6lYcOGqXPnzurSpYsOHDig/v37a8aMGeratauVoXqNzWY7/4eLfIBL+vLhXE6uhry9RMeSTyv5TKYkaefBk8ovKNTwXh00e9VWHUk6bVTIPoccG4v8Go8cG48cG4v8Go8cw1uKioo0Y8YMffrpp8rIyNDNN9+s8ePHq379+iVuv2fPHk2ePFm7d++Wn5+f7rjjDv31r39VUFCQx8e09ByQGTNm6PHHH9cHH3wgSZo1a5aGDBmiv//97xozZozef/99Pf7445o2bZqVYXrV2excSZK/w/Wbiaq/Ps/MyXXbJze/UFvjjxf/srhg/Z4jkqSI8JpGhOqzyLGxyK/xyLHxyLGxyK/xyDG8ZebMmVq4cKEmTZqkRYsWyWazKTY2Vnl5eW7bpqSk6M9//rPq1aunZcuWaebMmdq5c6eeeuqpKzqmpQXI8ePHdc899xQ/T0hI0J133umyTc+ePXX48GGzQzNMQmqaCgqLVDekust63Vrnn/+UeMptn/q1a6h3pxZuv2Qclc83sNIycwyJ1VeRY2ORX+ORY+ORY2ORX+ORY4s4naXzcZXy8vI0Z84cjRgxQl26dFFkZKSmTp2q5ORkrV271m37kydPqlOnTho/frwaNGig1q1b64EHHtB33313Rce1tACpW7eu/vOf/xQ/v+GGGxQfH++yze7du1W7dm2zQzNMXkGhdh1K0O2tmrisd4+OUEZWjvYeTXLbJ6R6gJ77Yzd1j45wWb8jpqkys3O173iyoTH7GnJsLPJrPHJsPHJsLPJrPHIMb4iPj9e5c+fUvn374rWgoCBFRUVp2zb3G1pGR0frzTffVKVK54vWQ4cOadmyZerQocMVHdfSc0BiY2P13HPPKSkpST179tSwYcP09NNPKzc3VxEREfrhhx/0zjvv6PHHH7cyTK/7YNVWzRrZW68Pulsrvturlo3C9KceMZq2fL1y8wvl77CrUViwTqSmKy0zWzsOJGjb/hMa3aezHH6VdTTptDo1b6iHbovWW0u/1dks9zZreUeOjUV+jUeOjUeOjUV+jUeOcUG3bt0u+fq6detKXE9KOl+ohoWFuayHhIQoMTHxku9555136ujRo6pTp45mzpx5BdFKNqfT2tP5V6xYoWnTpunkyZOy2Wz633D8/f01aNAgDR061CvHaj10qlfexxu6tmysIT1vUf3aNZSSfk6f/Pt7zV+3U5J0c0S43h/9gMZ/uEafb46TJAU47Brc8xbd1rKxalbzV0JquhZ8s1PLNu6x8sco1cixsciv8cix8cixsciv8cpijnfOGmV1CBd1V9hwq0MoUV5U/CVfv1gBsmLFCo0dO1b79u1ThQr/HYwaO3asUlJSNG/evIu+548//qicnBxNmTJFp06d0ooVK+Tv7+9RvJYXIBf89NNPOnr0qDIzM1W5cmWFhoYqKipKfn5+XjtGaSpAAAAA4I4C5MqtSnznqvZbs2aNRo4cqR9++EEOh6N4/S9/+Yvy8vI0a9asy75HSkqKunTpoldffVX33XefR8e1/D4gFzRq1EiNGjWyOgwAAACgXLgwepWSkqJ69eoVr6ekpCgyMtJt+8OHDyshIUFdunQpXgsJCVG1atWUnOz5OUSWnoQOAAAA+Ayrr3bl5atgRUZGKiAgQFu2bCley8jIUFxcXIk3Al+/fr3+8pe/KDPzv5dyPn78uM6cOaPGjRt7fFwKEAAAAKAcstvt6tevn6ZMmaJ169YpPj5eo0aNUmhoqHr06KHCwkKlpqYqJ+f8JZp79eqlwMBAjRkzRgcPHtT27ds1cuRItWjR4opuGk4BAgAAAJRTI0eOVJ8+fTRu3Dj17dtXFStW1OzZs2W325WYmKiOHTtq5cqVkqQaNWroo48+UlFRkfr27avhw4crKipKs2fPVsWKFT0+Zqk5Cd0MnIQOAABQupXqk9Bre+fKrN62KvnyJ4uXJnRAAAAAAJiGAgQAAACAaUrNZXgBAACAUq2o3Jy5YCg6IAAAAABMQwECAAAAwDSMYAEAAAAecDqLrA6hTKADAgAAAMA0FCAAAAAATMMIFgAAAOAJroLlFXRAAAAAAJiGAgQAAACAaRjBAgAAADzhZATLG+iAAAAAADANBQgAAAAA0zCCBQAAAHiiiBsRegMdEAAAAACmoQABAAAAYBpGsAAAAABPcBUsr6ADAgAAAMA0FCAAAAAATMMIFgAAAOABJ1fB8go6IAAAAABMQwECAAAAwDSMYAEAAACe4CpYXkEHBAAAAIBpKEAAAAAAmIYRLAAAAMATRYxgeQMdEAAAAACmoQABAAAAYBpGsAAAAABPOLkRoTfQAQEAAABgGgoQAAAAAKZhBAsAAADwgJOrYHkFHRAAAAAApqEAAQAAAGAaRrAAAAAAT3AVLK+gAwIAAADANBQgAAAAAEzDCBYAAADgAa6C5R10QAAAAACYhgIEAAAAgGkYwQIAAAA8wVWwvIIOCAAAAADTUIAAAAAAMI3N6XRyOj8AAAAAU9ABAQAAAGAaChAAAAAApqEAAQAAAGAaChAAAAAApqEAAQAAAGAaChAAAAAApqEAAQAAAGAaChAAAAAApqEAAQAAAGAaChAAAAAApqEAAQAAAGAaChAAAAAApqEAAQAAAGAaCpBSpqioSNOmTVOnTp3UsmVLDRw4UMeOHbM6rDJr5syZeuSRR6wOo0xJS0vTCy+8oM6dO6t169bq27evtm/fbnVYZcqpU6c0ZswYtW/fXtHR0Xrsscd06NAhq8Mqk44cOaLo6GgtXbrU6lDKlJMnT6pZs2Zuj08//dTq0MqU5cuX6/e//72aN2+uu+++W6tWrbI6JEASBUipM3PmTC1cuFCTJk3SokWLZLPZFBsbq7y8PKtDK3PmzZunadOmWR1GmTN69Gj98MMPevPNN7V48WLdeOONevTRR3X48GGrQyszhg4dqhMnTuj999/X4sWL5XA4NGDAAGVnZ1sdWpmSn5+vv/71r8rKyrI6lDJn//798vPz0/r167Vhw4bixz333GN1aGXGihUr9Oyzz+rBBx/UF198od///vcaPXq0du3aZXVoAAVIaZKXl6c5c+ZoxIgR6tKliyIjIzV16lQlJydr7dq1VodXZiQnJ2vQoEF6++231bBhQ6vDKVOOHTumjRs3avz48YqJiVGjRo303HPPqXbt2vriiy+sDq9MOHPmjMLDw/XSSy+pefPmaty4sYYNG6bU1FQdPHjQ6vDKlOnTp8vf39/qMMqkAwcOqGHDhgoJCVGtWrWKHw6Hw+rQygSn06m3335b/fv3V//+/VW/fn0NHz5ct956q7Zu3Wp1eAAFSGkSHx+vc+fOqX379sVrQUFBioqK0rZt2yyMrGzZu3evqlWrps8++0wtW7a0OpwypUaNGnrvvfd00003Fa/ZbDY5nU6lp6dbGFnZUaNGDb355puKiIiQJP3yyy+aPXu2QkND1aRJE4ujKzu2bdumRYsW6fXXX7c6lDJp//79fF4N9NNPP+nkyZNuHaXZs2dr8ODBFkUF/FclqwPAfyUlJUmSwsLCXNZDQkKUmJhoRUhl0u23367bb7/d6jDKpKCgIHXp0sVlbdWqVTp+/Lg6duxoUVRl1/PPP69PPvlEdrtds2bNUtWqVa0OqUzIyMjQ2LFjNW7cOLffx/COAwcOqFatWvrjH/+oo0ePqn79+ho2bJg6depkdWhlwtGjRyVJWVlZevTRRxUXF6fw8HANHTqU//6hVKADUopcmN+22+0u635+fsrNzbUiJOCa7NixQ88++6y6devGf/QM0L9/fy1ZskT33nuvhg8frr1791odUpkwYcIEtWrVivMRDJKXl6ejR48qMzNTTzzxhN577z01b95csbGx+u6776wOr0zIzMyUJD311FPq2bOn5syZow4dOmjYsGHkGKUCHZBS5MLsa15ensscbG5urqpUqWJVWMBV+frrr/XXv/5VLVu21Jtvvml1OGXShRGWl156Sd9//73mz5+vV1991eKofNvy5cu1fft2ff7551aHUmbZ7XZt27ZNlSpVKv7C7aabbtLhw4c1e/Zs3XLLLRZH6PsqV64sSXr00Uf1hz/8QZJ0ww03KC4uTnPnziXHsBwdkFLkQqs/JSXFZT0lJUWhoaFWhARclfnz52vEiBHq3Lmz3n//fU4s9aJTp07piy++UGFhYfFahQoV1LhxY7ffHbhyS5Ys0alTp3TbbbcpOjpa0dHRkqTx48fr7rvvtji6sqNq1apu3f6mTZsqOTnZoojKlgv/ZmjatKnLepMmTZSQkGBFSIALCpBSJDIyUgEBAdqyZUvxWkZGhuLi4hQTE2NhZIDnFixYoJdeekkPP/yw3nrrLbd/ZODapKSk6Mknn3S5kk1+fr7i4uLUuHFjCyMrG6ZMmaKVK1dq+fLlxQ9JGjlypN577z1rgysj4uPjFR0d7XZ/oD179nBiupdERUXJ399fP/zwg8v6gQMHVK9ePYuiAv6LEaxSxG63q1+/fpoyZYqCg4NVp04dTZ48WaGhoerRo4fV4QGXdeTIEb3yyivq0aOHBg8erFOnThW/5nA4FBgYaGF0ZUNkZKQ6duyoiRMnatKkSQoKCtK7776rjIwMDRgwwOrwfF7t2rVLXL/uuutUp04dk6Mpm5o2baqIiAhNnDhR48ePV40aNfTJJ5/o+++/1+LFi60Or0xwOBwaNGiQ3nnnHdWuXVstWrTQl19+qY0bN2revHlWhwdQgJQ2I0eOVEFBgcaNG6ecnBy1adNGs2fP5ltk+IQ1a9YoPz9fa9eudbt3zR/+8Ae99tprFkVWdthsNr311lt644039MQTT+js2bOKiYnRxx9/rOuvv97q8IDLqlChgt59911NmTJFTzzxhDIyMhQVFaW5c+eqWbNmVodXZgwbNkxVqlQpvp9Y48aNNX36dLVr187q0ADZnE6n0+ogAAAAAJQPnAMCAAAAwDQUIAAAAABMQwECAAAAwDQUIAAAAABMQwECAAAAwDQUIAAAAABMQwECAAAAwDQUIAAAAABMQwECAD4mNzdXUVFRio6O1ksvvWR1OAAAXBEKEADwMTabTR9++KFatGih+fPn68iRI1aHBACAxyhAAMDH2O12tWnTRoMGDZIk7d271+KIAADwHAUIAPioRo0aSZL27dtncSQAAHiOAgQAfNT7778vSYqPj7c4EgAAPEcBAgA+aMOGDfrnP/+patWqKS4uzupwAADwGAUIAPiYjIwMPfvss+rWrZv69u2r06dPKzk52eqwAADwCAUIAPiYiRMnqqCgQJMmTVJUVJQkxrAAAL6DAgQAfMjq1av1xRdf6OWXX1ZwcHBxAcKJ6AAAX0EBAgA+IjU1VePHj9eDDz6orl27SpLq1q2roKAgzgMBAPgMChAA8BHPP/+8qlWrpqefftpl/YYbbmAECwDgMyhAAMAHfPrpp/r222/1t7/9TVWrVnV5LSoqSsePH1dmZqZF0QEA4Dmb0+l0Wh0EAAAAgPKBDggAAAAA01CAAAAAADANBQgAAAAA01CAAAAAADANBQgAAAAA01CAAAAAADANBQgAAAAA01CAAAAAADANBQgAAAAA01CAAAAAADANBQgAAAAA01CAAAAAADDN/wOpUE87FNLINAAAAABJRU5ErkJggg==\n",
@@ -3623,7 +3425,7 @@
      },
      "metadata": {
       "filenames": {
-       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week42_88_4.png"
+       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week42_88_2.png"
       }
      },
      "output_type": "display_data"
@@ -3692,9 +3494,7 @@
   {
    "cell_type": "markdown",
    "id": "9b182ae1",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Building neural networks in Tensorflow and Keras\n",
     "\n",
@@ -3710,9 +3510,7 @@
   {
    "cell_type": "markdown",
    "id": "60683ec5",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Tensorflow\n",
     "\n",
@@ -3745,10 +3543,7 @@
    "cell_type": "code",
    "execution_count": 14,
    "id": "8a0c6901",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "ename": "SyntaxError",
@@ -3766,9 +3561,7 @@
   {
    "cell_type": "markdown",
    "id": "b66e0227",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "and/or if you use **anaconda**, just write (or install from the graphical user interface)\n",
     "(current release of CPU-only TensorFlow)"
@@ -3778,10 +3571,7 @@
    "cell_type": "code",
    "execution_count": 15,
    "id": "df994f58",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "conda create -n tf tensorflow\n",
@@ -3791,9 +3581,7 @@
   {
    "cell_type": "markdown",
    "id": "b9005559",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "To install the current release of GPU TensorFlow"
    ]
@@ -3802,10 +3590,7 @@
    "cell_type": "code",
    "execution_count": 16,
    "id": "7287b5eb",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "conda create -n tf-gpu tensorflow-gpu\n",
@@ -3815,9 +3600,7 @@
   {
    "cell_type": "markdown",
    "id": "c066b083",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Using Keras\n",
     "\n",
@@ -3830,10 +3613,7 @@
    "cell_type": "code",
    "execution_count": 17,
    "id": "6582adea",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "conda install keras"
@@ -3842,9 +3622,7 @@
   {
    "cell_type": "markdown",
    "id": "7d305596",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "You can look up the [instructions here](https://keras.io/) for more information.\n",
     "\n",
@@ -3854,9 +3632,7 @@
   {
    "cell_type": "markdown",
    "id": "a4508850",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Collect and pre-process data\n",
     "\n",
@@ -3867,10 +3643,7 @@
    "cell_type": "code",
    "execution_count": 18,
    "id": "5f2256f6",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# import necessary packages\n",
@@ -3922,10 +3695,7 @@
    "cell_type": "code",
    "execution_count": 19,
    "id": "a5dfa0e9",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from tensorflow.keras.layers import Input\n",
@@ -3951,10 +3721,7 @@
    "cell_type": "code",
    "execution_count": 20,
    "id": "dd935ce0",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "\n",
@@ -3981,10 +3748,7 @@
    "cell_type": "code",
    "execution_count": 21,
    "id": "67158cb2",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "DNN_keras = np.zeros((len(eta_vals), len(lmbd_vals)), dtype=object)\n",
@@ -4008,10 +3772,7 @@
    "cell_type": "code",
    "execution_count": 22,
    "id": "86d74ee3",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# optional\n",
@@ -4050,9 +3811,7 @@
   {
    "cell_type": "markdown",
    "id": "563d3f68",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## The Breast Cancer Data, now with Keras"
    ]
@@ -4061,10 +3820,7 @@
    "cell_type": "code",
    "execution_count": 23,
    "id": "34e6467a",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "\n",
@@ -4238,9 +3994,7 @@
   {
    "cell_type": "markdown",
    "id": "09879108",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Fine-tuning neural network hyperparameters\n",
     "\n",
@@ -4266,9 +4020,7 @@
   {
    "cell_type": "markdown",
    "id": "f8ec1769",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Hidden layers\n",
     "\n",
@@ -4289,9 +4041,7 @@
   {
    "cell_type": "markdown",
    "id": "43cc1fe5",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Which activation function should I use?\n",
     "\n",
@@ -4320,9 +4070,7 @@
   {
    "cell_type": "markdown",
    "id": "a9cbce9f",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Is the Logistic activation function (Sigmoid)  our choice?\n",
     "\n",
@@ -4352,9 +4100,7 @@
   {
    "cell_type": "markdown",
    "id": "2dfb3f9a",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## The derivative of the Logistic funtion\n",
     "\n",
@@ -4390,9 +4136,7 @@
   {
    "cell_type": "markdown",
    "id": "f806c047",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## The RELU function family\n",
     "\n",
@@ -4415,9 +4159,7 @@
   {
    "cell_type": "markdown",
    "id": "ef9e2a08",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "ELU(z) = \\left\\{\\begin{array}{cc} \\alpha\\left( \\exp{(z)}-1\\right) & z < 0,\\\\  z & z \\ge 0.\\end{array}\\right.\n",
@@ -4427,9 +4169,7 @@
   {
    "cell_type": "markdown",
    "id": "2e2750d8",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Which activation function should we use?\n",
     "\n",
@@ -4450,9 +4190,7 @@
   {
    "cell_type": "markdown",
    "id": "4e566f13",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## More on activation functions, output layers\n",
     "\n",
@@ -4470,9 +4208,7 @@
   {
    "cell_type": "markdown",
    "id": "03205666",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Batch Normalization\n",
     "\n",
@@ -4492,9 +4228,7 @@
   {
    "cell_type": "markdown",
    "id": "ad7c3e53",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Dropout\n",
     "\n",
@@ -4510,9 +4244,7 @@
   {
    "cell_type": "markdown",
    "id": "c3b98a7c",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Gradient Clipping\n",
     "\n",
@@ -4529,9 +4261,7 @@
   {
    "cell_type": "markdown",
    "id": "e7f21477",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## A very nice website on Neural Networks\n",
     "\n",
@@ -4541,9 +4271,7 @@
   {
    "cell_type": "markdown",
    "id": "54968291",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## A top-down perspective on Neural networks\n",
     "\n",
@@ -4586,9 +4314,7 @@
   {
    "cell_type": "markdown",
    "id": "4500b85e",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Limitations of supervised learning with deep networks\n",
     "\n",
@@ -4615,6 +4341,11 @@
   }
  ],
  "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
diff --git a/doc/LectureNotes/_build/jupyter_execute/week42_88_2.png b/doc/LectureNotes/_build/jupyter_execute/week42_88_2.png
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diff --git a/doc/LectureNotes/_build/jupyter_execute/week43.ipynb b/doc/LectureNotes/_build/jupyter_execute/week43.ipynb
index bedb76abd..385bb5b42 100644
--- a/doc/LectureNotes/_build/jupyter_execute/week43.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/week43.ipynb
@@ -604,7 +604,13 @@
       "Learning rate  =  0.0001\n",
       "Lambda =  1.0\n",
       "Accuracy score on data set:  0.5\n",
-      "\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "Learning rate  =  0.0001\n",
       "Lambda =  10.0\n",
       "Accuracy score on data set:  0.5\n",
@@ -786,7 +792,7 @@
      },
      "metadata": {
       "filenames": {
-       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week43_30_2.png"
+       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week43_30_3.png"
       }
      },
      "output_type": "display_data"
@@ -10894,20 +10900,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Adam: Eta=0.001, Lambda=0"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "Adam: Eta=0.001, Lambda=0\n",
       "\r",
       "  [----------------------------------------] 0.000% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392 "
      ]
@@ -43593,7 +43586,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "predictions = (n_inputs) = (1437,)\n",
+      "predictions = (n_inputs) = (1437,)"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
       "prediction for image 0: 8\n",
       "correct label for image 0: 6\n"
      ]
diff --git a/doc/LectureNotes/_build/jupyter_execute/week43_30_3.png b/doc/LectureNotes/_build/jupyter_execute/week43_30_3.png
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diff --git a/doc/LectureNotes/gaussian.pdf b/doc/LectureNotes/gaussian.pdf
index 7d8e4e1a7..58b91e8ef 100644
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