diff --git a/doc/pub/NeuralNet/ipynb/NeuralNet.ipynb b/doc/pub/NeuralNet/ipynb/NeuralNet.ipynb
index 6d7b7115e..b66a2a5e6 100644
--- a/doc/pub/NeuralNet/ipynb/NeuralNet.ipynb
+++ b/doc/pub/NeuralNet/ipynb/NeuralNet.ipynb
@@ -597,9 +597,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "%matplotlib inline\n",
@@ -1520,10 +1518,28 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "inputs = (n_inputs, pixel_width, pixel_height) = (1797, 8, 8)\n",
+      "labels = (n_inputs) = (1797,)\n",
+      "X = (n_inputs, n_features) = (1797, 64)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 864x864 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# import necessary packages\n",
     "import numpy as np\n",
@@ -1589,10 +1605,17 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of training images: 1437\n",
+      "Number of test images: 360\n"
+     ]
+    }
+   ],
    "source": [
     "from sklearn.model_selection import train_test_split\n",
     "\n",
@@ -1712,10 +1735,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 5,
+   "metadata": {},
    "outputs": [],
    "source": [
     "# building our neural network\n",
@@ -1791,11 +1812,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "probabilities = (n_inputs, n_categories) = (1437, 10)\n",
+      "probability that image 0 is in category 0,1,2,...,9 = \n",
+      "[3.89940599e-05 1.79115580e-01 1.47286800e-02 7.96733555e-01\n",
+      " 3.28982767e-04 1.49752254e-07 9.19699482e-05 4.42365585e-03\n",
+      " 3.57722690e-06 4.53485505e-03]\n",
+      "probabilities sum up to: 1.0000000000000002\n",
+      "\n",
+      "predictions = (n_inputs) = (1437,)\n",
+      "prediction for image 0: 3\n",
+      "correct label for image 0: 6\n"
+     ]
+    }
+   ],
    "source": [
     "# setup the feed-forward pass, subscript h = hidden layer\n",
     "\n",
@@ -1991,11 +2027,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Old accuracy on training data: 0.16423103688239388\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/ipykernel_launcher.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "  after removing the cwd from sys.path.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "New accuracy on training data: 0.10090466249130133\n"
+     ]
+    }
+   ],
    "source": [
     "# to categorical turns our integer vector into a onehot representation\n",
     "#from keras.utils import to_categorical\n",
@@ -2095,10 +2152,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 8,
+   "metadata": {},
    "outputs": [],
    "source": [
     "class NeuralNetwork:\n",
@@ -2220,11 +2275,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy score on test set:  0.9305555555555556\n"
+     ]
+    }
+   ],
    "source": [
     "epochs = 100\n",
     "batch_size = 100\n",
@@ -2256,11 +2317,240 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  1e-05\n",
+      "Lambda =  1e-05\n",
+      "Accuracy score on test set:  0.18888888888888888\n",
+      "\n",
+      "Learning rate  =  1e-05\n",
+      "Lambda =  0.0001\n",
+      "Accuracy score on test set:  0.18333333333333332\n",
+      "\n",
+      "Learning rate  =  1e-05\n",
+      "Lambda =  0.001\n",
+      "Accuracy score on test set:  0.20277777777777778\n",
+      "\n",
+      "Learning rate  =  1e-05\n",
+      "Lambda =  0.01\n",
+      "Accuracy score on test set:  0.2\n",
+      "\n",
+      "Learning rate  =  1e-05\n",
+      "Lambda =  0.1\n",
+      "Accuracy score on test set:  0.12222222222222222\n",
+      "\n",
+      "Learning rate  =  1e-05\n",
+      "Lambda =  1.0\n",
+      "Accuracy score on test set:  0.18888888888888888\n",
+      "\n",
+      "Learning rate  =  1e-05\n",
+      "Lambda =  10.0\n",
+      "Accuracy score on test set:  0.1527777777777778\n",
+      "\n",
+      "Learning rate  =  0.0001\n",
+      "Lambda =  1e-05\n",
+      "Accuracy score on test set:  0.5916666666666667\n",
+      "\n",
+      "Learning rate  =  0.0001\n",
+      "Lambda =  0.0001\n",
+      "Accuracy score on test set:  0.5583333333333333\n",
+      "\n",
+      "Learning rate  =  0.0001\n",
+      "Lambda =  0.001\n",
+      "Accuracy score on test set:  0.5361111111111111\n",
+      "\n",
+      "Learning rate  =  0.0001\n",
+      "Lambda =  0.01\n",
+      "Accuracy score on test set:  0.5777777777777777\n",
+      "\n",
+      "Learning rate  =  0.0001\n",
+      "Lambda =  0.1\n",
+      "Accuracy score on test set:  0.6055555555555555\n",
+      "\n",
+      "Learning rate  =  0.0001\n",
+      "Lambda =  1.0\n",
+      "Accuracy score on test set:  0.6416666666666667\n",
+      "\n",
+      "Learning rate  =  0.0001\n",
+      "Lambda =  10.0\n",
+      "Accuracy score on test set:  0.8111111111111111\n",
+      "\n",
+      "Learning rate  =  0.001\n",
+      "Lambda =  1e-05\n",
+      "Accuracy score on test set:  0.8833333333333333\n",
+      "\n",
+      "Learning rate  =  0.001\n",
+      "Lambda =  0.0001\n",
+      "Accuracy score on test set:  0.9\n",
+      "\n",
+      "Learning rate  =  0.001\n",
+      "Lambda =  0.001\n",
+      "Accuracy score on test set:  0.8666666666666667\n",
+      "\n",
+      "Learning rate  =  0.001\n",
+      "Lambda =  0.01\n",
+      "Accuracy score on test set:  0.875\n",
+      "\n",
+      "Learning rate  =  0.001\n",
+      "Lambda =  0.1\n",
+      "Accuracy score on test set:  0.8666666666666667\n",
+      "\n",
+      "Learning rate  =  0.001\n",
+      "Lambda =  1.0\n",
+      "Accuracy score on test set:  0.9416666666666667\n",
+      "\n",
+      "Learning rate  =  0.001\n",
+      "Lambda =  10.0\n",
+      "Accuracy score on test set:  0.9416666666666667\n",
+      "\n",
+      "Learning rate  =  0.01\n",
+      "Lambda =  1e-05\n",
+      "Accuracy score on test set:  0.9472222222222222\n",
+      "\n",
+      "Learning rate  =  0.01\n",
+      "Lambda =  0.0001\n",
+      "Accuracy score on test set:  0.9333333333333333\n",
+      "\n",
+      "Learning rate  =  0.01\n",
+      "Lambda =  0.001\n",
+      "Accuracy score on test set:  0.9416666666666667\n",
+      "\n",
+      "Learning rate  =  0.01\n",
+      "Lambda =  0.01\n",
+      "Accuracy score on test set:  0.9361111111111111\n",
+      "\n",
+      "Learning rate  =  0.01\n",
+      "Lambda =  0.1\n",
+      "Accuracy score on test set:  0.9527777777777777\n",
+      "\n",
+      "Learning rate  =  0.01\n",
+      "Lambda =  1.0\n",
+      "Accuracy score on test set:  0.9333333333333333\n",
+      "\n",
+      "Learning rate  =  0.01\n",
+      "Lambda =  10.0\n",
+      "Accuracy score on test set:  0.23055555555555557\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/ipykernel_launcher.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "  after removing the cwd from sys.path.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  0.1\n",
+      "Lambda =  1e-05\n",
+      "Accuracy score on test set:  0.10555555555555556\n",
+      "\n",
+      "Learning rate  =  0.1\n",
+      "Lambda =  0.0001\n",
+      "Accuracy score on test set:  0.10555555555555556\n",
+      "\n",
+      "Learning rate  =  0.1\n",
+      "Lambda =  0.001\n",
+      "Accuracy score on test set:  0.11666666666666667\n",
+      "\n",
+      "Learning rate  =  0.1\n",
+      "Lambda =  0.01\n",
+      "Accuracy score on test set:  0.07777777777777778\n",
+      "\n",
+      "Learning rate  =  0.1\n",
+      "Lambda =  0.1\n",
+      "Accuracy score on test set:  0.08611111111111111\n",
+      "\n",
+      "Learning rate  =  0.1\n",
+      "Lambda =  1.0\n",
+      "Accuracy score on test set:  0.10555555555555556\n",
+      "\n",
+      "Learning rate  =  0.1\n",
+      "Lambda =  10.0\n",
+      "Accuracy score on test set:  0.125\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/ipykernel_launcher.py:44: RuntimeWarning: overflow encountered in exp\n",
+      "/usr/local/lib/python3.7/site-packages/ipykernel_launcher.py:45: RuntimeWarning: invalid value encountered in true_divide\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  1.0\n",
+      "Lambda =  1e-05\n",
+      "Accuracy score on test set:  0.07777777777777778\n",
+      "\n",
+      "Learning rate  =  1.0\n",
+      "Lambda =  0.0001\n",
+      "Accuracy score on test set:  0.07777777777777778\n",
+      "\n",
+      "Learning rate  =  1.0\n",
+      "Lambda =  0.001\n",
+      "Accuracy score on test set:  0.07777777777777778\n",
+      "\n",
+      "Learning rate  =  1.0\n",
+      "Lambda =  0.01\n",
+      "Accuracy score on test set:  0.07777777777777778\n",
+      "\n",
+      "Learning rate  =  1.0\n",
+      "Lambda =  0.1\n",
+      "Accuracy score on test set:  0.07777777777777778\n",
+      "\n",
+      "Learning rate  =  1.0\n",
+      "Lambda =  1.0\n",
+      "Accuracy score on test set:  0.10555555555555556\n",
+      "\n",
+      "Learning rate  =  1.0\n",
+      "Lambda =  10.0\n",
+      "Accuracy score on test set:  0.07777777777777778\n",
+      "\n",
+      "Learning rate  =  10.0\n",
+      "Lambda =  1e-05\n",
+      "Accuracy score on test set:  0.07777777777777778\n",
+      "\n",
+      "Learning rate  =  10.0\n",
+      "Lambda =  0.0001\n",
+      "Accuracy score on test set:  0.07777777777777778\n",
+      "\n",
+      "Learning rate  =  10.0\n",
+      "Lambda =  0.001\n",
+      "Accuracy score on test set:  0.07777777777777778\n",
+      "\n",
+      "Learning rate  =  10.0\n",
+      "Lambda =  0.01\n",
+      "Accuracy score on test set:  0.07777777777777778\n",
+      "\n",
+      "Learning rate  =  10.0\n",
+      "Lambda =  0.1\n",
+      "Accuracy score on test set:  0.07777777777777778\n",
+      "\n",
+      "Learning rate  =  10.0\n",
+      "Lambda =  1.0\n",
+      "Accuracy score on test set:  0.07777777777777778\n",
+      "\n",
+      "Learning rate  =  10.0\n",
+      "Lambda =  10.0\n",
+      "Accuracy score on test set:  0.07777777777777778\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "eta_vals = np.logspace(-5, 1, 7)\n",
     "lmbd_vals = np.logspace(-5, 1, 7)\n",
@@ -2293,11 +2583,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/ipykernel_launcher.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "  after removing the cwd from sys.path.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# visual representation of grid search\n",
     "# uses seaborn heatmap, you can also do this with matplotlib imshow\n",
@@ -2355,11 +2672,220 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  1e-05\n",
+      "Lambda =  1e-05\n",
+      "Accuracy score on test set:  0.20833333333333334\n",
+      "\n",
+      "Learning rate  =  1e-05\n",
+      "Lambda =  0.0001\n",
+      "Accuracy score on test set:  0.16944444444444445\n",
+      "\n",
+      "Learning rate  =  1e-05\n",
+      "Lambda =  0.001\n",
+      "Accuracy score on test set:  0.18055555555555555\n",
+      "\n",
+      "Learning rate  =  1e-05\n",
+      "Lambda =  0.01\n",
+      "Accuracy score on test set:  0.17222222222222222\n",
+      "\n",
+      "Learning rate  =  1e-05\n",
+      "Lambda =  0.1\n",
+      "Accuracy score on test set:  0.23333333333333334\n",
+      "\n",
+      "Learning rate  =  1e-05\n",
+      "Lambda =  1.0\n",
+      "Accuracy score on test set:  0.2916666666666667\n",
+      "\n",
+      "Learning rate  =  1e-05\n",
+      "Lambda =  10.0\n",
+      "Accuracy score on test set:  0.1527777777777778\n",
+      "\n",
+      "Learning rate  =  0.0001\n",
+      "Lambda =  1e-05\n",
+      "Accuracy score on test set:  0.8666666666666667\n",
+      "\n",
+      "Learning rate  =  0.0001\n",
+      "Lambda =  0.0001\n",
+      "Accuracy score on test set:  0.8416666666666667\n",
+      "\n",
+      "Learning rate  =  0.0001\n",
+      "Lambda =  0.001\n",
+      "Accuracy score on test set:  0.9277777777777778\n",
+      "\n",
+      "Learning rate  =  0.0001\n",
+      "Lambda =  0.01\n",
+      "Accuracy score on test set:  0.8861111111111111\n",
+      "\n",
+      "Learning rate  =  0.0001\n",
+      "Lambda =  0.1\n",
+      "Accuracy score on test set:  0.8805555555555555\n",
+      "\n",
+      "Learning rate  =  0.0001\n",
+      "Lambda =  1.0\n",
+      "Accuracy score on test set:  0.875\n",
+      "\n",
+      "Learning rate  =  0.0001\n",
+      "Lambda =  10.0\n",
+      "Accuracy score on test set:  0.875\n",
+      "\n",
+      "Learning rate  =  0.001\n",
+      "Lambda =  1e-05\n",
+      "Accuracy score on test set:  0.9833333333333333\n",
+      "\n",
+      "Learning rate  =  0.001\n",
+      "Lambda =  0.0001\n",
+      "Accuracy score on test set:  0.9833333333333333\n",
+      "\n",
+      "Learning rate  =  0.001\n",
+      "Lambda =  0.001\n",
+      "Accuracy score on test set:  0.9805555555555555\n",
+      "\n",
+      "Learning rate  =  0.001\n",
+      "Lambda =  0.01\n",
+      "Accuracy score on test set:  0.9861111111111112\n",
+      "\n",
+      "Learning rate  =  0.001\n",
+      "Lambda =  0.1\n",
+      "Accuracy score on test set:  0.9805555555555555\n",
+      "\n",
+      "Learning rate  =  0.001\n",
+      "Lambda =  1.0\n",
+      "Accuracy score on test set:  0.9805555555555555\n",
+      "\n",
+      "Learning rate  =  0.001\n",
+      "Lambda =  10.0\n",
+      "Accuracy score on test set:  0.9527777777777777\n",
+      "\n",
+      "Learning rate  =  0.01\n",
+      "Lambda =  1e-05\n",
+      "Accuracy score on test set:  0.9861111111111112\n",
+      "\n",
+      "Learning rate  =  0.01\n",
+      "Lambda =  0.0001\n",
+      "Accuracy score on test set:  0.9916666666666667\n",
+      "\n",
+      "Learning rate  =  0.01\n",
+      "Lambda =  0.001\n",
+      "Accuracy score on test set:  0.9861111111111112\n",
+      "\n",
+      "Learning rate  =  0.01\n",
+      "Lambda =  0.01\n",
+      "Accuracy score on test set:  0.9833333333333333\n",
+      "\n",
+      "Learning rate  =  0.01\n",
+      "Lambda =  0.1\n",
+      "Accuracy score on test set:  0.9944444444444445\n",
+      "\n",
+      "Learning rate  =  0.01\n",
+      "Lambda =  1.0\n",
+      "Accuracy score on test set:  0.975\n",
+      "\n",
+      "Learning rate  =  0.01\n",
+      "Lambda =  10.0\n",
+      "Accuracy score on test set:  0.9416666666666667\n",
+      "\n",
+      "Learning rate  =  0.1\n",
+      "Lambda =  1e-05\n",
+      "Accuracy score on test set:  0.9416666666666667\n",
+      "\n",
+      "Learning rate  =  0.1\n",
+      "Lambda =  0.0001\n",
+      "Accuracy score on test set:  0.75\n",
+      "\n",
+      "Learning rate  =  0.1\n",
+      "Lambda =  0.001\n",
+      "Accuracy score on test set:  0.8972222222222223\n",
+      "\n",
+      "Learning rate  =  0.1\n",
+      "Lambda =  0.01\n",
+      "Accuracy score on test set:  0.8944444444444445\n",
+      "\n",
+      "Learning rate  =  0.1\n",
+      "Lambda =  0.1\n",
+      "Accuracy score on test set:  0.9277777777777778\n",
+      "\n",
+      "Learning rate  =  0.1\n",
+      "Lambda =  1.0\n",
+      "Accuracy score on test set:  0.8972222222222223\n",
+      "\n",
+      "Learning rate  =  0.1\n",
+      "Lambda =  10.0\n",
+      "Accuracy score on test set:  0.7388888888888889\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",
+      "Learning rate  =  1.0\n",
+      "Lambda =  0.001\n",
+      "Accuracy score on test set:  0.11388888888888889\n",
+      "\n",
+      "Learning rate  =  1.0\n",
+      "Lambda =  0.01\n",
+      "Accuracy score on test set:  0.1527777777777778\n",
+      "\n",
+      "Learning rate  =  1.0\n",
+      "Lambda =  0.1\n",
+      "Accuracy score on test set:  0.15\n",
+      "\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:  0.15\n",
+      "\n",
+      "Learning rate  =  10.0\n",
+      "Lambda =  1e-05\n",
+      "Accuracy score on test set:  0.1\n",
+      "\n",
+      "Learning rate  =  10.0\n",
+      "Lambda =  0.0001\n",
+      "Accuracy score on test set:  0.11944444444444445\n",
+      "\n",
+      "Learning rate  =  10.0\n",
+      "Lambda =  0.001\n",
+      "Accuracy score on test set:  0.18055555555555555\n",
+      "\n",
+      "Learning rate  =  10.0\n",
+      "Lambda =  0.01\n",
+      "Accuracy score on test set:  0.10555555555555556\n",
+      "\n",
+      "Learning rate  =  10.0\n",
+      "Lambda =  0.1\n",
+      "Accuracy score on test set:  0.08333333333333333\n",
+      "\n",
+      "Learning rate  =  10.0\n",
+      "Lambda =  1.0\n",
+      "Accuracy score on test set:  0.09166666666666666\n",
+      "\n",
+      "Learning rate  =  10.0\n",
+      "Lambda =  10.0\n",
+      "Accuracy score on test set:  0.08055555555555556\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "from sklearn.neural_network import MLPClassifier\n",
     "# store models for later use\n",
@@ -2388,11 +2914,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# optional\n",
     "# visual representation of grid search\n",
@@ -2474,9 +3019,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "pip3 install tensorflow"
@@ -2492,9 +3035,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "conda install tensorflow"
@@ -2509,11 +3050,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "inputs = (n_inputs, pixel_width, pixel_height) = (1797, 8, 8)\n",
+      "labels = (n_inputs) = (1797,)\n",
+      "X = (n_inputs, n_features) = (1797, 64)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 864x864 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# import necessary packages\n",
     "import numpy as np\n",
@@ -2561,11 +3120,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using TensorFlow backend.\n"
+     ]
+    },
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'tensorflow'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-15-b532db97c8ce>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutils\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mto_categorical\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel_selection\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtrain_test_split\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m# one-hot representation of labels\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mto_categorical\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/keras/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0m__future__\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mabsolute_import\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mutils\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mactivations\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mapplications\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/keras/utils/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdata_utils\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mio_utils\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mconv_utils\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;31m# Globally-importable utils.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/keras/utils/conv_utils.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmoves\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0;34m.\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mbackend\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mK\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/keras/backend/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     87\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0m_BACKEND\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'tensorflow'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     88\u001b[0m     \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstderr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Using TensorFlow backend.\\n'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 89\u001b[0;31m     \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mtensorflow_backend\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     90\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     91\u001b[0m     \u001b[0;31m# Try and load external backend.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0m__future__\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mprint_function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mtensorflow\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mframework\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mops\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mtf_ops\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtraining\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmoving_averages\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'tensorflow'"
+     ]
+    }
+   ],
    "source": [
     "from keras.utils import to_categorical\n",
     "from sklearn.model_selection import train_test_split\n",
@@ -2594,9 +3175,7 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import tensorflow as tf\n",
@@ -2742,9 +3321,7 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "epochs = 100\n",
@@ -2759,9 +3336,7 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "DNN_tf = np.zeros((len(eta_vals), len(lmbd_vals)), dtype=object)\n",
@@ -2784,9 +3359,7 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# optional\n",
@@ -2825,9 +3398,7 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# optional\n",
@@ -2851,9 +3422,7 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "conda install keras"
@@ -2869,9 +3438,7 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "pip3 install keras"
@@ -2887,9 +3454,7 @@
   {
    "cell_type": "code",
    "execution_count": 24,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from keras.models import Sequential\n",
@@ -2912,9 +3477,7 @@
   {
    "cell_type": "code",
    "execution_count": 25,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "DNN_keras = np.zeros((len(eta_vals), len(lmbd_vals)), dtype=object)\n",
@@ -2937,9 +3500,7 @@
   {
    "cell_type": "code",
    "execution_count": 26,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# optional\n",
@@ -2976,7 +3537,25 @@
    ]
   }
  ],
- "metadata": {},
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.7.0"
+  }
+ },
  "nbformat": 4,
  "nbformat_minor": 2
 }
diff --git a/doc/src/Splines/Splines.do.txt b/doc/src/Splines/Splines.do.txt
index a6eab5fd4..a4556fe71 100644
--- a/doc/src/Splines/Splines.do.txt
+++ b/doc/src/Splines/Splines.do.txt
@@ -613,6 +613,11 @@ it_array = np.array(guesses)
 pt.plot(it_array.T[0], it_array.T[1], "x-")
 !ec
 
+!split
+===== Conjugate gradient =====
+
+
+
 !split 
 ===== Revisiting our first homework =====
 
@@ -694,6 +699,9 @@ The Hessian matrix of $C(\beta)$ is given by
 !et
 This result implies that $C(\beta)$ is a convex function since the matrix $X^T X$ always is positive semi-definite.
 
+
+
+
 !split
 ===== Simple program =====
 
@@ -1367,6 +1375,10 @@ print("gamma_j after %d epochs: %g" % (n_epochs,gamma_j))
 
 
 
-
+!split
+=====  Program for stochastic gradient =====
+
+!split
+===== Momentum based methods =====