diff --git a/doc/pub/NeuralNet/ipynb/NeuralNet.ipynb b/doc/pub/NeuralNet/ipynb/NeuralNet.ipynb
index 0c0f659fd..1d8ec9006 100644
--- a/doc/pub/NeuralNet/ipynb/NeuralNet.ipynb
+++ b/doc/pub/NeuralNet/ipynb/NeuralNet.ipynb
@@ -1688,9 +1688,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "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",
@@ -1755,9 +1775,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
-   "outputs": [],
+   "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",
@@ -1956,7 +1985,24 @@
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "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",
@@ -2121,7 +2167,30 @@
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "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: 0.10438413361169102\n"
+     ]
+    }
+   ],
    "source": [
     "# to categorical turns our integer vector into a onehot representation\n",
     "from sklearn.metrics import accuracy_score\n",
@@ -2343,7 +2412,15 @@
    "cell_type": "code",
    "execution_count": 8,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy score on test set:  0.9277777777777778\n"
+     ]
+    }
+   ],
    "source": [
     "epochs = 100\n",
     "batch_size = 100\n",
@@ -2377,7 +2454,238 @@
    "cell_type": "code",
    "execution_count": 9,
    "metadata": {},
-   "outputs": [],
+   "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:  0.9388888888888889\n",
+      "\n",
+      "Learning rate  =  0.01\n",
+      "Lambda =  0.0001\n",
+      "Accuracy score on test set:  0.9222222222222223\n",
+      "\n",
+      "Learning rate  =  0.01\n",
+      "Lambda =  0.001\n",
+      "Accuracy score on test set:  0.9194444444444444\n",
+      "\n",
+      "Learning rate  =  0.01\n",
+      "Lambda =  0.01\n",
+      "Accuracy score on test set:  0.9277777777777778\n",
+      "\n",
+      "Learning rate  =  0.01\n",
+      "Lambda =  0.1\n",
+      "Accuracy score on test set:  0.8805555555555555\n",
+      "\n",
+      "Learning rate  =  0.01\n",
+      "Lambda =  1.0\n",
+      "Accuracy score on test set:  0.7555555555555555\n",
+      "\n",
+      "Learning rate  =  0.01\n",
+      "Lambda =  10.0\n",
+      "Accuracy score on test set:  0.13333333333333333\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:  0.08888888888888889\n",
+      "\n",
+      "Learning rate  =  0.1\n",
+      "Lambda =  0.0001\n",
+      "Accuracy score on test set:  0.125\n",
+      "\n",
+      "Learning rate  =  0.1\n",
+      "Lambda =  0.001\n",
+      "Accuracy score on test set:  0.07777777777777778\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:  0.07777777777777778\n",
+      "\n",
+      "Learning rate  =  0.1\n",
+      "Lambda =  1.0\n",
+      "Accuracy score on test set:  0.07777777777777778\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:  0.08888888888888889\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",
@@ -2410,9 +2718,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {},
-   "outputs": [],
+   "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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euKz9/2RHNn1z7v6f7t2g81BUmUhOHEnIWT4Re4pCEaGEhgcbDo+9/9RMAOq1vPaC53A5XTz14YM079qADcu3c3jfsfwPPA8UL13U2PePJlEoIpe+/9ICAOo1q3rBcwC0u+MGTh5PZsNXV+eX24vxvPbPea8/lnRB3wf44JXFANS78RrD4/f+coibu9Xnt+37sdmtNGtbG2d2IF2KsGDwSkXo008/Zfbs2QwYMIBevXrRq1cvBgwYwKxZs5g7d643QvhPzObcR6hO14UdPCjExnPj+1C6fDHefnFBfoeWL0z/Md/nx/amVPlivD1yUX6H5hUX3d//8sd27hrUlqSEVO5s/DJ9m40ivHAotz3QKi9DzFcm03/c/+N6U6pcJG+fGRj6u4v2f+d/+0B74+Ep/O+aIYQXKcSdT3XJi9Dynfli+/4//tBU9/tbMevMnCl/8l/e63Pz0ZhluHHz7qLBvPDePfy0YR/ZDv+pBv8Tl9vktZuveWUgZLVayc6+sHNkZGRgs9m8EcJ/Enc0icio8Jzl4iUiOJ2URma6w9AuqlRh3vr8EVxON0/fN4XUi0yuvNqdOJpozDf6IvmWLMz4Tx/C6XTx9ANT/Tbf88UdOS//EoU5nZhGZnrWv3p803Z1+HreZrIdTtJOZ7B6wVbq3lglv8LNcyeO/Yf9P+NhnE53gdr/Jw4nEFmicM5y8dJFOH0qlcy0f7f/G7SpRWRJz+MzUjP5dv5mrrmuQr7EmtfiYk8RGX3Ovi9ZmNOJqf+67wNUqVUGi9XCzk1/5EeI+Sou9vzXfsSZ177jHx51VmhYMFPfWMGjncfzfL8puF3unMNs4j+8MhB65JFH6N69OyNGjGDChAlMmDCBESNG0KtXLx555BFvhPCfbP9hHzXqlqd0hWIAdLyjMRvX7ja0CSscwhvT+/PD6l2MGT6bLD+aE3K+bRt/p0bdcpQu78m3U69GbPxmj6FNWEQIb37yID+s+Y0xT8/163zPt/37GGpcX4HSZ85+6XhXEzau3vWvH//7rsO0PDOnxmI10+SWWuz56UC+xJoftm04b///rzEbvzmvv/+9/1f/xpin5hSo/b9t7S5qNKxC6crRAHTqdxMbV+Q+FyY3Lbs35O6nuwJgs1tp0aMRv6zfc4lHXR22f7eXGvUqntP3m7Jx1a//6Tnq3FCFXzbsy4/w8t3272OoUe+c9/o+Tdi45t8f1u7Y5wb6DvacWFKkWBjt/9eYb5f9+74jVwevzBHq0qULjRs3ZuPGjcTFxeF2u2nYsCEDBw6kRIkS3gjhP0lKSGX8iC94fvydWG0Wjh5KYOyz86haqwyDX+3B47e9S+feNxBVqghNb6lJ01tq5jz22X5TOZ3kHxMl/5aUkMpbLyxgxLjeOfm++fx8qtYszZCRPXjsf+/R+Y7GRJUsTNM2NWna5my+z/T/2O/yPV/SyRTGPzWH59+7x5P/wZOMfWIWVeuUZfDoXjzeefw/Pn7yqCU8OrI7k1c9hcvp4ucN+5j34Tdeiv7Kefb/fEa81efs/n/uC6rWLMOQl3vwWK936XzHmf5+c02a3nzO/n/Q//r7+ZLiT/PW458wYvqjWG1Wju6P481HPqZqvQoMeedeHmv5yj8+fvKIuQwc35dJG17G7XazcflPLJq02kvRX5mkkymMf3IWz39wnyf3A/GMHTaTqnXKMfj1O3i849hLPkfpilEcP5xwyXZXo6SEVMY/O4/nJ97tyf/gScY+NYeqtcsw+LWePN5twj8+fu6H3zD8zd58sGwoJpOJzyauJmbnYS9Fn7+uhknM3mJyu91+d/3IDjWf83UIPuO2BvYVD0wpBeNwzOVyFwrydQi+c/i4ryPwKVPh8Es3Ksjsdl9H4FNfxrzu1e1tP1jea9uqX/6g17aVG11QUURERAycAXSZwcDJVEREROQ8qgiJiIiIwdVwWru3qCIkIiIiAUsVIRERETEIpLPGVBESERGRgKWKkIiIiBg43YFTJwmcTEVERETOo4qQiIiIGLgCqE4SOJmKiIiInEcVIRERETHQWWMiIiIiAUAVIRERETHQWWMiIiIiAUADIREREQlYOjQmIiIiBi5NlhYREREp+FQREhEREQNnANVJAidTERERkfOoIiQiIiIGOn1eREREJACoIiQiIiIG+tFVERERkQCgipCIiIgYON26jpCIiIhIgaeKkIiIiBjoOkIiIiIiAUAVIRERETFw6TpCIiIiIgWfKkIiIiJioDlCIiIiIgFAAyEREREJWDo0JiIiIga6oKKIiIhIAPDLitDxsYE7fkuID/d1CD5lDQ72dQg+Zba4fR2Cz5hM5X0dgk+Vjzzl6xB86o7SG3wdQkDRj66KiIiIBAC/rAiJiIhI/nHqgooiIiIiBZ8qQiIiImLgQmeNiYiIiBR4qgiJiIiIgeYIiYiIiAQAVYRERETEQD+6KiIiIhIAVBESERERA5d+a0xERESk4FNFSERERAw0R0hEREQkAGggJCIiIgFLh8ZERETEwKULKoqIiIgUfKoIiYiIiIFTP7oqIiIiUvCpIiQiIiIGmiMkIiIiEgBUERIREREDzRESERERCQCqCImIiIiB5giJiIiIBABVhERERMTAqYqQiIiISMGnipCIiIgYuHTWmIiIiEjBp4qQiIiIGGiOkIiIiEgAUEVIREREDFxuzRESERERKfA0EBIREZGApUNjIiIiYuAMoDqJBkJnNI+qxsDqbbGZLew7fZxXdi4kNTvT0Oaa8BI8VbMzYdYgXLh5bedidifHYjNbeKpmZxoVq0Radhbr4/by4b61uHH7KJv/rnWZyjxVvxV2s4U9p07w9MYvSXFkGdpUL1KclxvfSrg9CKfLxXObvuLXhOM590fYgpjT/k6e2vAlO08e83YKV+SmUtcwvE5r7GYre5OO8+yWZaRkG/OvVjiKl65vT7gtCKfbxYhtK9h1ypjn8/VupUJYJA99P8eb4V+xm0pew7DabXLyf27bUlLPzz8imhfqtSPcFozT7eLF7cvZlejJf0GbBwm2WHG4nAAsOfQrU2M2ej2Py9GqZFWeqNUGu9nC3qQ4ntu+JNfcR1zn2fcut5sXf1rOrsSjALQtXYOHqzfHbrYSm57E01sXkZiV7otULkvjyJo8ULkTNrOVv1JiGbd3NmnOs+99t5RoSM9yN+Ush1qCiQoqQp+NI8lyZfNEjd6UC43GjIlVx7Yw59BaH2Rx+f7Yksn6T1NwOiCqopX2g8IJCjUOAmI2ZvLDzBRMJhPBYSbaDQynaCnPx+dPy9PY8XUG2VluSlxjpf2gCKy2wJlfUxAEzpDvHxSxhzKy7m0M3z6L29ZP4EhaAgOrtzW0CTbbeK/RfUz/8zvu/OF9Ptr3DaPq9QLggSqtKBVSmP999y53/fABxYPC+V+Fxr5I5bJEBoXwZtOOPPrtIm5ePIVDKYk8Xb+VoU2wxcqMW+5g0q4f6bRsGhN3bmBCiy45999UpjKLOt1DlYhi3g7/ikUGhfJ6oy48tuEL2q78gIOpiTxZt42hTbDFyrSWdzJ5zwa6rprCe799z1s3dDe06Vj2WrqVr+3N0PNEUXsooxt0ZeCmL2j/9fscSk1keO2bDW2CLVY+bn4nU2I20n3NR7y/+zvGNe4BQIjFRvlCRem6ejLd1nxEtzUf+c0gqKg9lNH1uzJw0zzar3qfQ6mncs19avO7mBKzgR5rP+L9PesZ28iTe+0ipXihXgcG/TiPLmsmsf/0SYbWbO2LVC5LYVshhtfozSu7PuH+zaM5mnGSByp3NrRZfXwrj2wdyyNbx/LYtrc4lXWad/fNJ9GRwn2VOhCfmchDW97g8W3j6VymGddGVPBRNv9dWpKLlROS6f5sYR6cVIwiJS2sn5ZqaOPIdLNiXBLdny3Mfe9Eck3jINZOTgEgZkMG25el879RRbj/vUiyM2HbojRfpJLnXG6T126+poEQcGPxquxKOsKhtJMAzDu4mQ6lrzO0aRJ1DYfTEvjhRAwA6+L28MxPswG4tnBpvordSZYrGzduvj3+GzeX9J8PxBalK7Hj5DH2nz4FwGd7f6JbpVqGNi1LV+JASiLfHvkTgFWHfuexdYtz7u9XowHDv19OXHqK9wLPI81LVGZHQiwHUjz5z/x9G13PG9A0L1GZgymJrDv2BwCrY2MYtHFBzv1VwovRv8aNTPztO+8Fnkeal6jMzlOxHEhJAGDWn1tzyb8Kh1JPse7Y7wCsORrD4E3zAagbWZq07CwmN+vN0lse5tm6txJk9o9ic/MSldmZGMuB1DO5/7WVLuXqGNo0i67CoZRTrD9+NvchP34BQNfydZi//2eOpCUBMHH3Oj6K2eDFDK5Mg6LViTl9iCPp8QAsjf2Bm0s0uGj7O8rfTKLjNMuPega67/++kA//WAJAZFAENpOV1OyM/A88j+z/KYuSVW0ULe3pr/U6hPDbugzc7rPVfLfLjdsNmWmedVkZbixnKj671mbQqHsoIeFmTGYTbR8Lp2abYO8nIlfEP96t8lmJ4MIcz0jKWY7LSCbcFkwha1DO4bEKhYpxMvM0L9bpQbWIkpx2ZDBhz0oAdiYepm2pOqw+tguHy0n70tdRPCjMJ7lcjtKFwjmampyzfDTtNBH2IMJs9pzDY5UiinIiPZXXb+zAtUWjSHZkMnrbtzmPuXfNPG+HnWdKhUZwNP1s/sfSkwm3BxNmteccHqsUXoz4jBRGN+xMjSLRJDsyeeOXNQCEWm2MvaEbT21eSp3IUj7J4UqUCsklf1swhaz2nENEFcMiOZGRymv1O1OjSAlOOzJ4Y6cn/0LWIH48sZ+RP3+Jw+VkXOMePFG7Df+342uf5PNflAwpzLG0s6/93HKvFFaME5kpvFa/CzUKlyDZkcGbv64GoGJYMfYmxfF+kzsoE1qYmOQ4v8j7b1HBRTmRmZizfCIziULWEEItQYbDYwARtkL0LHsTA7aNM6x3uV08fe1dtIy6jh9O7ORwWpxXYs8Lp084CS9+th4QXtxMVpqbrHQ3QaGewY49xMytj4Uz88lTBEeYcbvc3Pl6UQBOxTpJS3Ix76VEUhKclK1pp1U//3nv/yeuAKqTeCXT2NjYf7z5mtmUe2nO6Xbl/NtqstAsuhoLDm7h7h8+YPb+jbzT6B5sZgvT/viOP1LimNb0ISbd0I8diQdxuJ3eCv+KmS7ymzLOc74VWc0WWpepzKx9P9N1xadM37ONaTf3xG62eCvMfHPx/X9u/mZalbqG2X9up8fqj5mxbwtTWvTGbrYwumFnPt23lX3JJ7wVcp66WP6uc/M3WWhV8hrm/LWd29dOZcbvW/ioWR9sZgtrj8bw5NbFpGZnkeVyMmnP99xaurq3wr8i/yp3s5lWJap6cv9mCp/9sZnJTe/EZrZgNVloXaoqL/60jO5rJ3MiI5VR9Tvn+pxXI/NFXvvn5v+3TqVuZOPJXzmWkXDBfa/v/pzbfxhBuC2Uuyu2y/M480suaQJgMp/9u5zYn83G2Wn0ez+SAdOL06RXIRaPTsbtduPMhv0/Z9H16QjueSuSjBQX38/wv6q4P1m6dCkdO3bk1ltv5fPPP7/g/l27dnH77bfTtWtXHn74YZKTk3N5FiOvDIQefvhh2rVrR9++fbn77rsNt759+3ojhH90LD2R4kHhOcvRQREkZaWR4XTkrDuReZr9KfH8mnQY8Bwas2CmbEgkhW0hfPbX99zx3bs8uGkKiVmpHEq98M3iahWbmkx06NlvMSVDw0nMTCc9+2z+cWkp/JF0kp/jPRNEVx36HbPJTLnwIl6PN6/FpiYRHXw2/xIhEZ78z9n/cekp/Hk6nl8SPAP31bExWEwm6kaWpmFUefpVa8ySWx9kSK1WNCpejiktens9j8sVm5ZM1Pn5Z52Xf8Zp/jwdz45TnvzXHPXkX75QUVqXqkrD4uVz2powkX3Ol4ir2dG0JKKCz772SwRfJPeUeHacOgKczb1coaLEZZzm++N/Ep+ZihtYcOBn6kWW9XYaly0u8xSR9rP5F7cXJtmRSoYr64K2raLr8dXRzYZ1DYtWp5g9AoAMZxbfxG3nmjD/yT88ykLqqbN99fRJF8FhJuzBZwdCf23Posy1tpzJ0dd3CiH+YDbpyW7CIs1UbRK0f5erAAAgAElEQVREUKgZi81EzZuCid3juGA7/sjpNnnt9m8dP36c8ePHM3PmTBYvXsycOXP4/fffDW1ee+01Bg0axJIlS6hUqRJTp0695PN6ZSA0a9YsKlWqxBtvvMHatWsNtzVr1ngjhH+0Mf536hQpR7lQz0Tf2ys0Yl3cHkObH+JiKB1ShGsjSgNQv2hF3Lg5kn6KViVq8HztbgCEWOzcVakZX8b+4t0krsB3R/dTr3hpKoZ7yr13VavHqkPGzvXtkT8pG1aY2pElAGgcXRa3283h04nnP53f+e74n9QrVoYKYZ7876xSn9WxMYY26479TpnQItQqWhKARsXL4wZ2JMTSbOkEuq6aQtdVU3h71zq2xB/iwe9mezuNy/Z93B/UiyxDhbBIAPpUasCa2L2GNuuP/U6ZQkWoVcSTf8Pi5XG74VDqKUqGRPBMnVsIMlsxY6Jf1SasOPyb1/O4HN/H/cF1kWWoUMiTe+/KDVhzNJfcQ4tQq4jnsGfDYp7cD6ee4qsju7mp5DUUsYcA0LZMDXae8n2V+9/alrCXayMqUiakOACdSzdlY/yvF7QLs4ZQOqQ4u5L/MqxvGV0vpwJkM1loFVWPnxP35X/geaTi9XZi9zo4FZsNwC9fpnPNDUGGNiWqWDn0a1bOgGnfpkwKl7AQWthM9WZB7P0hE0emG7fbze+bMilZ1eb1PALFhg0baNKkCUWKFCE0NJR27dqxcuVKQxuXy0VqqmfCe3p6OsHBl56z5ZU5QmFhYYwaNYp58+bRoMHFJ+L5yqmsVEbuWMCb9XtjM1s4nJbAC7/M59rCpXmxTg/6fP8eJ7NSeGLbTJ6p3YUQix2HK5vh22eR5cpm8eHt1C5SjnktBmI2mVl4aCtrju3ydVr/2smMNJ7csIIPWnXHZrZwIOUUw75fTp1iJXn9xvZ0XDaNExmpPPTNAkbd0JYQq40sl5NH1i0k0+U/hwAvJiEzjac3L+Xdpj2xmS0cTDnFk5sXU7toKf6vYSe6rppCfEYqj/4wj5frdyDUaiPL6WTAhi/IKiD5P7ttKRNvOJN/agJPbVlM7SKleK1BZ7qt+Yj4zFQGbJzLyOs7EmLx7P/HN80jy+Vk9p/bKFeoCItu7o/FbOLHEwd4d/d6X6f1r3hyX8I7Obmf4umti6hdpBSj6neh+9rJxGem8tjGObxU7+/csxn441yyXE6+ORZDyZBwZrS4F7PJRGxaEs9vX+LrtP61REcKY/fM4oVa92EzWYnNiOeN3TOpFl6OYdXv4JGtYwEoHVKchKxkw3QBgA//WMzgar2Y3OgpcLv5If5XFh72j30PUKiImQ6DI1g8OhlntpsiJS10HBbBsX0OVk48zX3vRFLhOjuNbgtl9nOnsFhNBIeb6PF8YQDqdQwhPcXNjKEJuFyeQVPbBwrIHCEvns2VnJyc6yGsiIgIIiIicpbj4uKIiorKWY6OjmbHjh2GxzzzzDP069eP//u//yMkJIS5c+decvsmt/tiR0mvXvVXjPB1CD6TEB9+6UYFmDW4YJSdL5fZ4ncv1zxjMgVu7gDlI0/5OgSfuqP0Vl+H4FMPVvPuGamDf+rjtW1d831T3n333QvWP/744wwcODBnedKkSaSnpzN06FAA5s2bx86dO3nllVcAyMjI4Pbbb2f06NHUrVuXTz75hI0bNzJ58uR/3L7OGhMREREDl9t7Z43de++99OjR44L151aDAEqUKMHWrWcHxHFxcURHR+csx8TEEBQURN26dQG44447mDBhwiW3Hzjnx4mIiMhVJyIigrJly15wO38g1LRpUzZu3EhCQgLp6el8/fXXtGzZMuf+ChUqcOzYMf7803O9uzVr1lCnjvG6YLlRRUhEREQMnBe5tIIvlShRgqFDh3LPPffgcDjo2bMndevWpX///gwaNIg6deowevRohgwZgtvtplixYvzf//3fJZ9XAyERERHxC126dKFLly6GdR999FHOv1u1akWrVq3Of9g/0kBIREREDK6G3wDzFs0REhERkYClgZCIiIgELB0aExEREQNvnj7va4GTqYiIiMh5VBESERERA9dVePp8flFFSERERAKWKkIiIiJi4NTp8yIiIiIFnypCIiIiYqCzxkREREQCgCpCIiIiYqCf2BAREREJAKoIiYiIiIGuIyQiIiISAFQREhEREQPNERIREREJAKoIiYiIiIGuIyQiIiISADQQEhERkYClQ2MiIiJioMnSIiIiIgFAFSEREREx0AUVRURERAKAKkIiIiJioDlCIiIiIgFAFSERERExUEVIREREJACoIiQiIiIGqgiJiIiIBAC/rAh9UXeqr0PwGWcAXdshN44A+paSm1CTy9ch+IzdFNj7vrDZ5usQfOq4M8vXIQQUVYREREREAoBfVoREREQk/+jK0iIiIiIBQBUhERERMdAcIREREZEAoIGQiIiIBCwdGhMREREDHRoTERERCQCqCImIiIiBKkIiIiIiAUAVIRERETFQRUhEREQkAKgiJCIiIgZuVYRERERECj5VhERERMRAP7oqIiIiEgBUERIREREDnTUmIiIiEgBUERIREREDnTUmIiIiEgBUERIREREDzRESERERCQAaCImIiEjA0qExERERMdBkaREREZEAoIqQiIiIGGiytIiIiEgAUEVIREREDNxuX0fgPaoIiYiISMBSRUhEREQMXGiOkIiIiEiBp4qQiIiIGOg6QiIiIiIBQBWhMzZvsvLJlCAcDqhU2cWQ4ekUKmRss3ihjaWL7AQFQbnyLh4blE54hOe+O24Lo3jxs9Psb/9fJm1uyfZiBldmyyYL06cE4XCYqFjZyeDhGYSel//ShTaWLbJjD3JTrryLRwdl5OR/522FKHZO/rf9L4vWfpT/1k0WPptqx+EwUaGyi8efuDD/5QttrFhswx7kpmx5Fw8NzMzJ/29jRgYTWczFQwOzvBd8HvjxvP4/9CL9f8kiO/YgKH+m/0ecyX/pYhsrV9jJzIKqVZ0MHZ6B3e79PC7Hxk0WpkwJwpFlonJlJ08+mXFB7gsW2Fh0pu9XKO9i8OCMnNwXLbaxYrmNzCyoVs3Fk36UO8B3G81M/MiKwwFVK7t58SkHYeflP3uBhTkLLQTZoVIFN88McVA4ApxOeH2ClW2/eL5TN7/BxZBHszH5UTEh0N/7L0bXEQowiYkm3nozmBEj05kyPZWSpVx8MiXY0OaXnyzMmx3E6LFpvDc5lUY3ZDPhrRAADh8yEx7m5r3JqTk3f3ohJCWaePvNYJ4dmc6HZ/KfNiXI0GbHTxa+mG3ntbFpTJycRsMbspn4ludvdPiQibAwmDg5LefmT4OgpESYODaIp17K4L1paZQs5WLGefnv/NnCwjk2Xn4znfEfptOgsZMPxhv7yMI5NnbvtHgz9DyRmGhi3JvBvDAynaln9v/H5/X/n3+yMHd2EGPGpvHBef3/+++sLF5kZ/SbqUyemkpmlomF8/1jJJCYaOKNN4J5eWQ6n36aSqnSLiZ/ZNz3P/1kYdZsO+PGpTHlozRuuCGbcWf6/vr1VhYutDF2bBqffJxGZiZ88YV/5A5wKhFGvm5j7CsOFs7IokxpNxMnG78fb/nJzLSZViaNy2L21CyaNXEyaqwNgOVfW9h/yMTcjz33bfvFzOp1/vOxEujv/eLhtR67evVqZsyYwcGDBw3r58yZ460QLmr7VgvVqjspU9YFQOeuWXyzxma4jsK+fRaur59NVJRnZbPmDn7c5PkW9dsuC2YLPD0slEcfLMTnn9pxOn2RyeXZvtVC1eouypT15Naxq4Nvz8v/931m6tV3UvxM/k2bZ7P5TP67d1kwW9w8OyyExx8MZZaf5f/zNitVq7kofSb/9l0crF9jNeT/R4yZuufk36R5Nls2WXA4PPfv/NnC9i0W2nV2eDv8K7Z9q4Xq5/X/tZfo/83P6f+rv7Zxe88sIiLAbIZBQzK4+Rb/+Dts2WqhenUXZc/s+25dHaw5L/eYGDMNGjhzcm/RIpuNGz25f73Kyv96OXJyHzY0k1tv9Y/cATZuMVOrhovyZ/Lv1dXJl6sthvx37zVxQwMXJaI9yze3cLF+oxmHA1wuyMgwkeUARxY4svGraligv/f/E7fbezdf88pAaOzYsXz22Wfs37+f3r17s3jx4pz7Zs+e7Y0Q/lH8CXNOJwcoHuUmLdVEWtrZNtVrOPnlZyvHj3vKhV9/ZSPbYeJ0sgmnE65vkM2rY9J48+1Utm+1smSR/7wbxJ8wUzzKlbP8d/7p5+RfrYaLHT9biDuT/ypD/iaub+DklTHpjHk7je1brSxbZPN2GpctPs5Eseiz+79YlJu0NGP+VWs42fnT2fzXnpN/QryJqe/ZGfpsJmb/+TKc48QJc84ADyAql/5fo4aTn8/p/199ZcPhMJGcbOLIYTOJiSaeeyaURx4sxGfTgwgLuwre3f6FE3FmoqPP9v2oKDepF+Tu4qefLBw75sl95cqzuR8+bOZUoomnng7hgQdDmTbd7je5AxyPM1HinH0fHeUmJdVE6jn517rWxZafzMQe8ywv/tKCw2EiMRm6tHcSHuamfc8g2t4eRLkyblo1deEvAv29Xzy8Mkdo3bp1LFy4EKvVSt++fbn//vux2+106NAB91UwHHRd5HVrOedDrU5dJ3f1zeTVF0Mxm6Ft+yzCw11YrdCh09lvgHY79OiZxeKFdnrc7h/zRNwXyf/cD/XadZ306ZvFay+GYDLDre0dhIe7sVrdtD8nf5sduvfMYulCG91u949vxhfrgufmX6uuizvuyeL1l4IxmeHmdg7Cwt2YzfDmK8HcPyCLyGK+78uX49/2/7v7ZvLKi6GYzNDuTP+3WSHbCdu3WRn5ahp2O4x9PYRPPg7i0ccyvZPAFXD9i31/3XVO7rknixfP9P0OHRxERHj6fna2iW3bLIx6NR27HcaMCWbq1CAef/zqzx0unv+5+77BdW4eujeb4S/YMZmgW0cnhSPc2KwwebqVokVg9cJMMjLhiRE2Zsyx0PcO/yiLBPp7/z8JpLPGvDIQcrvdmM7MnqtYsSIffvgh/fr1IzIyMme9L0VHu9m752wc8fEmwsLdBIecbZOWBnWuy6ZdR0/HP5Vg4tNPggiPcLNmlY3KlZ1UquJ5VbndYPWjaehR0S727jkb8MmL5F/7umzanpP/Z58EER4Ba1dZqVTZZcjf4kf5F492E7P77P7PLf/0NKhV18ktHTzH/xNPmZg5DY4dNXH8mIlPJnm+BSYmmHC5TDiyTDz2hH98GEZHu9nzL/t/+3P2//Qz/b9YMTfNmmfnTDBtc4uDz2cEAVd//iWiXezefbaznjhhIjzcTch5ude7LptOZ3JPSDDxySdBRERAsWIump+T+y23Ovj0U+Mco6tZyWg3v+4++6kfFw8R5+Wfmgb1r3PRvZNncHMyAT742ErhCFi73sxTg7Ox2cBmg87tXKxeZ/abgVCgv/eLh1cK+e3bt6dv377s2LEDgKpVqzJhwgSGDBlywZwhX6jfMJs9v1k4ctjz51ix1M6NTY3VjISTZp4aVojUVM/yrM+CuKmN5+yI/X+ZmTEtCKcTMjNh6WI7LW/yj2oIwPUNnez9zcKRw543hBVLbTRpapzwl3DSxLPDQkk7k//sz+y0bOPAZIIDf5n5fJo9J/9li220uMl/JgzWa+AkZreZ2DP5f7XURuNc8n/hiZCc/Od+ZqNFGwc1arqYMiuN8R96JlG365xNs5scfjMIAmhwXv9fnkv/P3le///8nP7foqWD9eusZGZ6Pgg2/GClWnX/+CBs2NDJ7t0WDp/Z90uX2mh23r6PjzcxZGhoTu4zZthp09rT91u1zGbdObn/8L2VGn6SO8CNjVzs/M3MwTP5z19ipVUzY/wn4k08NMROypn8P/rUSrs2TkwmqFHNzapvPP3GkQ3rNpipW9N/Do0F+nu/eJjcXjo2tXHjRqKjo6lSpUrOuqNHj/Lxxx/z/PPP/6fn+vNwqbwOj80/Wpk2JYjsbChVysXwZ9I5etTMhHEhvDfZ8wpYssjGssV2XC6oVdvJgEEZBAVBRga8PzGYPb9ZcDqhRcts7n0gM19OIXXm02XPt/zoOX3ek7+bYc+kc+yomXfGBTNxsueA+dJFNpYvtuF2mahZO5tHBmXm5D9pYjB7fzOT7TTRvKWDex7Iypf8HflUrt3245nT57NNlCzlYvDTGRw/aua9t4IY/2E6ACsW2VixxIbbBdfWdtJ/oCf/c82ebic5mXw7fT7UlD8fMpt/tPLxOf3/yTP7f/y4ED440/8XL7KxdLEd95n+/9iZ/u90wqzP7az7xobLBddUdTFo6IWnIF8pez5VjzdtsvDRmdxLl3bz7JnX/ptjg5nykafvL1xoY9GZvl+7TjaDz/R9pxM++8zON99acTlNVK3qZNiwC0+/zwuFzfkz7+77TWdPny9b2s2rzzk4EmvilTdtzJ7q6cezF1iYu8gzibpeHRdPD84mOAgSk+CNd2zsiTFhtkDj+i6GDsjGlg9VkePO/HlN+ct7f+WyR/P+Sf9BnSUveW1bO7u+7LVt5cZrA6G8lB8DIX+RXwMhf5FfAyF/kV8DIX+QXwMhf5FfAyF/kV8DIX+hgVD+0dFMERERMdAFFUVEREQCgCpCIiIiYuB/k2YunypCIiIiErBUERIRERGDQLqgoipCIiIiErBUERIREREDVYREREREAoAqQiIiImIQQCeNqSIkIiIigUsVIRERETHQHCERERGRAKCKkIiIiBgF0CQhVYREREQkYGkgJCIiIgFLh8ZERETEQJOlRURERAKAKkIiIiJi4NZkaREREZGCTxUhERERMdAcIREREZEAoIqQiIiIGKkiJCIiIlLwqSIkIiIiBjprTERERCQAqCIkIiIiRqoIiYiIiBR8qgiJiIiIga4jJCIiIhIAVBESERERI80REhERESn4NBASERGRgKVDYyIiImKgydIiIiIiAcAvK0IVreG+DkFEvGxnVrqvQ/Cp0ceb+zoEn/q9S6SvQ/CpL494eYOaLC0iIiJS8PllRUhERETyk+YIiYiIiBR4GgiJiIiIkduLt/9g6dKldOzYkVtvvZXPP//8gvv//PNP+vbtS9euXXnggQdISkq65HNqICQiIiJXvePHjzN+/HhmzpzJ4sWLmTNnDr///nvO/W63m0cffZT+/fuzZMkSrr32WiZPnnzJ59VASERERIyuworQhg0baNKkCUWKFCE0NJR27dqxcuXKnPt37dpFaGgoLVu2BOCRRx7hrrvuuuTzarK0iIiI+ExycjLJyckXrI+IiCAiIiJnOS4ujqioqJzl6OhoduzYkbN88OBBihcvztNPP81vv/1GtWrVeOGFFy65fVWERERExMht8tpt+vTp3HzzzRfcpk+fbgzJfWH5yGQ6e3ZbdnY2mzdv5u6772bp0qWUK1eOMWPGXDJVVYRERETEZ+6991569Ohxwfpzq0EAJUqUYOvWrTnLcXFxREdH5yxHRUVRoUIF6tSpA0Dnzp0ZNGjQJbevgZCIiIgY5FJ8yTfnHwK7mKZNmzJx4kQSEhIICQnh66+/5tVXX825//rrrychIYE9e/ZQo0YN1q5dS61atS75vBoIiYiIyFWvRIkSDB06lHvuuQeHw0HPnj2pW7cu/fv3Z9CgQdSpU4f33nuPESNGkJ6eTsmSJXnjjTcu+bwmd24H3a5yrmPVfB2CiHhZoP/W2JT4lr4Owaf0W2MTvbq9ClMvPYDIKwceeMpr28qNJkuLiIhIwNJASERERAKW5giJiIiIkVs/uioiIiJS4KkiJCIiIgYmvzuN6vKpIiQiIiIBSxUhERERMVJFSERERKTgU0VIREREjHTWmIiIiEjBp4qQiIiIGGmOkIiIiEjBp4qQiIiIGKkiJCIiIlLwqSIkIiIiRqoIiYiIiBR8qgiJiIiIka4jJCIiIlLwaSAkIiIiAUuHxkRERMTApMnSIiIiIgWfKkIiIiJiFEAVIQ2E/iO3G54bA1Urwf29fR2NdwVy7qD8C3L+2380M3OqHYcDKlRy8cgTWYQWMrb5cpGVlYut2O1QpryLBwdmERYBaanwwTg7sYfMuFzQ6tZsuvfO9k0ieeDE9kT2zT6CK9tFePlQaj1UEWuoxdDm+JZT/DEvFpMZrIWs1HqoAqElgn0U8ZVpdHMt+j3TBVuQlb92x/L2EzNJS8nIte2w8XdzYE8s8z9cm7Ou070taN/nRuzBNn7fcYi3h8/EkeW/+z8Q6dDYf/DHfug3FFZ+4+tIvC+QcwflX5DzT06E98cG8cSLmUz4JIPoUm5mTrUZ2vz6s5nFc6y8+EYmb36YQf3GTj582w7A7Gk2ihV3M+6jDEa/m8GqZVZifvPPt9asZAe/frif64ZWoflbdQiJDiJm1mFDG2eWi53v/UW9YVW4cUwtohsUZs+0Qz6K+MoUjgxj2Ft3MeqhqfRvOYpjB+Lp91zXC9qVu6YEo+cOpEWX6w3rm3a4jq79WvJs73d5pPX/YQ+20b1/a2+FL3nEa6/W/fv3c/z4cQDmzZvHqFGjWLFihbc2nydmLoIeHaB9APbzQM4dlH9Bzv+XbRaqVHNRqqznWEDbLtl8t8aK+5xDA3/uM1PnehfFojwrGzd3sm2ThWwH9BvgoO/DDgASE0w4HCZCC/nncYWTO5IpXLkQhUp5qjvlbo3i2A8JuM/5Y7hdbnBDdpoTgOwMF2a7f15zpn6rGsT8cpDYv04AsOzT72ndo+EF7Trf15JVczbx3dKfDOtv7tmYBR+uJSUxDbfbzbvPzGHt/M1eiV3yziUPjX388ce8++67OJ1OSpcuTfXq1XNu1apVo2zZspfcyLRp05gxYwYul4smTZpw9OhRbr31VubPn89ff/3FY489lifJ5LcXhnj+v2m7b+PwhUDOHZR/Qc7/5AkTxaJcOcvFotykp5lITyPn8Ng11V18udDKieMmokq4+fYrK9kOE6eToWgxsFjgnTF2flxvoVEzJ6XL+udAKONkFsHF7DnLQZF2stOdONNdOYfHrMEWaj5Qnh9f2oM9zIrb5abxyzV8FfIVKV66KCdiT+Usxx9NpFBECKFhwYbDYx+MmAdAvebVDY8vWzmKmOLhvPrZoxQrUZhfN//B1FGLvRN8Pguks8YuORD68MMPeeONN6hbty6HDh0iJiaGvXv3sn79evbt2wdA1apVmTVr1kWfY/78+axYsYL4+Hg6d+7Mpk2bCAoKolevXvTs2dNvBkIiUvC4XLmvN59TL69Z10Wvvg7GjgzCZHLTur2TsHA31nOOoA16JouMwTDu5SC++MzG/+515G/g+cB9sQ+/c/4Wpw+m8ceCozQbW4vQEsEcWHmcn8f/wY1jamIy+VdlyGzOPV6n8yKd4jwWm4XrW1bnlX4fkZXp4Im3+3LfM5358KUFeRmm5LNLDoTCwsK46aabsFqtREdH06BBA8P9hw8fzhkQXYzL5cJut1OmTBnuv/9+goKCcu5zOp2XGbqIyJUrHu3m9z1nP+kT4k0UCncTHHK2TXqaZzDUpoOnSpB4CuZMsxEWDj9vMVO+kpvI4p7HNGudzabv/fM8lOBidpJ+T81ZzkzIwlrIgjX47GTpkzuSKVItLGdydPm20ez99BCO09nYI2wXPOfVLO5IAtWvr5CzXLxkYU6fSiUzPetfPT7hWBIbvtyRUz1au2ALdw5pny+xep1+YuOshx56iHnz5l30/rJly9K69T9PHGjbti133303TqeTgQMHArBnzx7uvPNOOnTo8B9DFhHJO9c1cLJvt4Wjhz1v/KuWWWl0o/EL2qmTJkYODyLtzBhh/mc2mrXOxmSCjeutfPGZZ06RIws2rrNSu55/fsErVjeCpH0ppB71fLAfXn2C6IZFDG3CK4ZyavdpMhM9Fa+4LYmERAf53SAIYPu6PdSoX5HSlaIA6Ni3ORu/3vmvH//98p9p0fl67MGe3G9sV5eYXw7mS6ySfy75tWXMmDE4HA6+++47WrRowbXXXkv16tUJCQm51ENzDB48mC1btmCxnP1WYbfbGThwIK1atbq8yEVE8kDhovDo8EzeejWIbAeUKO3m8acy+WOvmUlv2XnzwwxKl3PT/Y5snh8YjMsNNWq7eOBxT9Xgnoez+GiCneEPeSokjZo56djDP0+fDipso9YjFfnl7T9wZ7sJKRFEnQGVSPojld8+2s+NY2pRrHYEFbuUZOurezFZTdjCrFz/xDW+Dv2yJJ1MYfywz3l+8gNYbRaOHohn7OAZVK1bjsFj7+Txtq//4+OXTf+OsCKFmPjlk5gtZn7feZgpryz0UvT5LIDmCJnc7oseFQbg0KFD7Nmzh71797J371727NlDbGwsZcuW5auvvvJWnAauY9V8sl0R8Z2dWem+DsGnpsS39HUIPvV7l0hfh+BTXx6Z6NXtVX77La9t688hw7y2rdxcsiJUrlw5ypUrx6233pqzLi0tjZiYmHwNTERERHwkgCpCl3UdodDQUOrVq5fXsYiIiIh4lX+e2iAiIiL5JpCuI+Sf14EXERERyQOqCImIiIiRKkIiIiIiBZ8GQiIiIhKwdGhMREREjHRoTERERKTgU0VIREREDHT6vIiIiEgAUEVIREREjNwmX0fgNaoIiYiISMBSRUhERESMNEdIREREpOBTRUhEREQMdNaYiIiISABQRUhERESMVBESERERKfhUERIREREDzRESERERCQCqCImIiIiRKkIiIiIiBZ8GQiIiIhKwdGhMREREjHRoTERERKTgU0VIREREDHT6vIiIiEgA0EBIREREApYGQiIiIhKwNEdIREREjDRHSERERKTgU0VIREREDHTWmIiIiEgA8MuKUPty9X0dgs+4nU5fh+BTJovF1yGI+Ehgv/bdzmO+DiGwqCIkIiIiUvD5ZUVIRERE8pEqQiIiIiIFnypCIiIiYqCzxkREREQCgP3EVOIAABfISURBVAZCIiIiErB0aExERESMdGhMREREpOBTRUhEREQMNFlaREREJACoIiQiIiJGqgiJiIiIFHyqCInI/7d399FR1Xcexz8jJNVxgfCQBLHuoRiWoGwA0xMSdMODhIeEBcEcDWwN1YAFhGgUCyIKsoIJIimIVaiIUEI1WBFDaUiUggjjarQl9kEQUEtKIAGODmgSQzL7R3envWYIAZJ7w/zer3PmHO5wnfv9Op7xez73d+8FACsSIQAAgOBHIgQAACy4agwAAMAAJEIAAMCKRAgAACD4kQgBAAArEiEAAIDgRyIEAAAsuGoMAADAAAxCAADAWJwaAwAAVpwaAwAACH4kQgAAwILF0gAAAAYgEQIAAFYkQgAAAMGPRAgAAFiRCAEAAAQ/EiEAAGDhcroAG5EIAQAAY5EIAQAAK4PWCDEInUPcqP66Z1GaQkJD9NnHf9Wye1fpm9NVAfedtWaaPv/jEb2Wu9XmKi9NXPJNylg8USHfC9FnpV/omcnPN+jxXPu06/gvyvz5FF3fr7uqv67W9pd/py0rCyVJ8aNj9fDLM1T51xP+z8lKfExVZ6pt7e9imfDdN4b+6T/Y++e3D/+MU2MBdOjSTrNenKqFd+Qqo8+DKv+sQhmLJzTY77roblpSNE+JqfEOVHlpOnRpr1kvTdfC1KW6p/f9Kv/suDKy/6vJ+0xdNklVX1dr8o1Zykx4VHEj+2tAyk2SpBsG9tJrz7ypqTc97H9dLj8EJnz3jaF/+g/2/vntaxqXz76X0xwZhLKzs504bJPFJsVof8khHT14TJK0dVWxhk64pcF+Y6aN0PZ1u/TOa+/ZXeIlix0eowMfHNLf/q/HgueLdOvE/2jyPj1je+itX+5SfX29ztae1f9s+0iJtydIkm5M6KV+Q/rouQ9ytGzXQv37f/S2sbNLY8J33xj6p/9g75/fPnxXi58ae+SRRxq8t2PHDn311VeSpKeeeqqlS7hg4d/vrMqyk/7tyrKTurqDW+52V1ni0+fuXytJ6j+0j+01Xqrw67qosuwf8W2gHhvb55P3D2rYXYP0pz37FfK9EN0yPl51tWclSd6Tp/XWhne05433dePN0Vr4xk/1k36zdOJvp+xt8iKY8N03hv7pP9j757eviVpBUmOXFk+EwsLCtHPnTkVHRysuLk5xcXFyu93+P7dGrisC/2upr6u3uZKWc8UVgS+O/OceG9tn1UPrJJ9Pz3+0RAtef1gfvbVPtd/+/cfgidSl2vPG+5KkP+35RH/au1+xSTHN3EHLMOG7bwz9038gwdQ/v334rhYfhGbPnq1ly5Zp27Zt6tatm8aNG6cOHTpo3LhxGjduXEsf/qJUHjmhTl07+re7XNtJ3lNnVP1NjYNVNa+Kv56/x8b2cbd36xc/3aB7Yx7SnBH/LV+9T0cPHdPVHdya8Ij1e3W5XDpbW9fyTTUDE777xtA//Qd7//z24btsWSOUkJCgVatWaePGjcrJyVFdXev+D+PD4lL1HhClblFdJUmj7x0mT0GJw1U1rw+L9ql3fE9d+/89Th0uz5YPmrzPf05N0qSFd0qSwiI6aNTkYdqx8V1Vna7WmOkjdcv4AZKk6/t1V6+4KH1Q+Ae7WrskJnz3jaF/+g/2/vntayKfjS+H2Xb5fFhYmJYvX65NmzZp//79dh32onxZ6dXSyS/osVezFBLSVkcPH9fTdz+nnrE99OCqezXth3OcLvGSfVnp1dJ7fq7HNj2kkNC2OnrouJZMWql/i+2hB38xTVNvevic+0jSr57arNnrZ2p16TNyuVz65RP5OlBySJI0/7Yc3bciQ+kL7lD92XotSsuV9+RpJ9ttMhO++8bQP/0He//89uG7XD6frxXMYxdmeEia0yU4xtfK07SW5mrTxukSADjA9N++4vpNth6v38xc2471h2ezbDtWINxHCAAAGIs7SwMAAKvL7lzRxSMRAgAAxmIQAgAAFq31ERsFBQVKTk5WUlKS8vLyzrnfzp07NXTo0CZ9JqfGAABAq3f8+HHl5ubq9ddfV2hoqNLS0jRgwABFRUVZ9jtx4oRycnKa/LkkQgAAwMrG+wh5vV6VlZU1eHm9XktJe/fuVXx8vMLCwuR2uzVixAgVFhY2KH3evHmaMWNGk1slEQIAAI5Zt26dVq5c2eD9GTNmaObMmf7tiooKhYeH+7cjIiJUWlpq+WfWr1+vG264QX379m3y8RmEAACAxYWu3bkUkyZNCvjIrfbt21u2A9320OX6x3PhDhw4oKKiIr388ss6duxYk4/PIAQAABzTvn37BkNPIJGRkSop+ccjXyoqKhQREeHfLiwsVGVlpW6//XbV1taqoqJCEydO1MaNGxv9XNYIAQAAq1b4rLGBAwfK4/Ho1KlTqqqqUlFRkRITE/1/n5mZqe3bt2vLli1avXq1IiIizjsESQxCAADgMhAZGamsrCylp6frtttu0+jRoxUTE6MpU6bo448/vujP5VljlxnTn7fDs8YAM5n+22f3s8Zu+ol9zxr7aBXPGgMAAHAEgxAAADAWV40BAAALOy+fdxqJEAAAMBaJEAAAsCIRAgAACH4kQgAAwMJ1+d1Z56KRCAEAAGORCAEAACtzAiESIQAAYC4SIQAAYMF9hAAAAAxAIgQAAKxIhAAAAIIfiRAAALBgjRAAAIABSIQAAIAViRAAAEDwYxACAADG4tQYAACwYLE0AACAAUiELjOuNm2cLgEAbMdvn81IhAAAAIIfiRAAALBgjRAAAIABSIQAAICVz5xIiEQIAAAYi0QIAABYsEYIAADAACRCAADAikQIAAAg+JEIAQAAC1e90xXYh0QIAAAYi0QIAABYsUYIAAAg+DEIAQAAY3FqDAAAWHBDRQAAAAOQCAEAACseugoAABD8SIQAAIAFa4QAAAAMQCIEAACsSIQAAACCH4kQAACwYI0QAACAAUiEAACAFfcRAgAACH4kQgAAwII1QgAAAAYgEQIAAFYkQgAAAMGPQQgAABiLU2MAAMCCxdIAAAAGIBECAABW9eZEQiRC5xA3qr9e+ChHa/64TPN+9YDc7a46576z1kxTatZoG6treSb3b3LvEv3Tv7n9m9y7yRiEAujQpZ1mvThVC+/IVUafB1X+WYUyFk9osN910d20pGieElPjHaiy5Zjcv8m9S/RP/+b2b3LvAflsfDnMlkGotLTU/2ePx6Ps7GwtXbpU+/bts+PwFyw2KUb7Sw7p6MFjkqStq4o1dMItDfYbM22Etq/bpXdee8/uEluUyf2b3LtE//Rvbv8m9246Wwah+fPnS5Ly8vK0ePFide3aVV26dNHjjz+uDRs22FHCBQn/fmdVlp30b1eWndTVHdwNYtLn7l+rt/N2211eizO5f5N7l+if/s3t3+TeA3H57Hs5zdbF0vn5+Vq/fr06duwoSUpNTVVqaqp+9KMf2VnGebmuCDwf1tfV21yJM0zu3+TeJfqnf3P7N7l309mSCJ09e1b19fXq3Lmz3G63//3Q0FBdcY7/+JxUeeSEOnXt6N/ucm0neU+dUfU3NQ5WZR+T+ze5d4n+6d/c/k3uPSCfz76Xw2yZQjp27KhBgwbp4MGD/tNkHo9HaWlpGjlypB0lXJAPi0vVe0CUukV1lSSNvneYPAUlDldlH5P7N7l3if7p39z+Te7ddLacGlu/fr0k6fDhw/J6vZL+ngZlZmZq8ODBdpRwQb6s9Grp5Bf02KtZCglpq6OHj+vpu59Tz9geenDVvZr2wzlOl9iiTO7f5N4l+qd/c/s3ufdAWsPaHbu4fL5WkEtdoOEhaU6XAACAbYpqX7H1eENG5Nh2rN9tn23bsQLhztIAAMDqsotILl7rW6kMAABgExIhAABg4br8Vs1cNBIhAABgLAYhAABgLE6NAQAAK4NuqE0iBAAAjEUiBAAALFgsDQAAYAASIQAAYGVOIEQiBAAAzEUiBAAArFgjBAAAEPxIhAAAgIXLnECIRAgAAJiLRAgAAFixRggAACD4kQgBAAALF88aAwAACH4kQgAAwIo1QgAAAMGPRAgAAFiZEwiRCAEAAHMxCAEAAGNxagwAAFi4WCwNAAAQ/EiEAACAFYkQAABA8CMRAgAAVjxiAwAAIPiRCAEAAAuuGgMAADAAiRAAALAiEQIAAAh+JEIAAMCKRAgAACD4kQgBAAAr7iMEAAAQ/EiEAACABfcRAgAAMACDEAAAMBanxgAAgBWnxgAAAIIfiRAAALAiEQIAAAh+JEIAAMCKRAgAAKB1KSgoUHJyspKSkpSXl9fg79966y2NHTtWY8aM0fTp0/XVV1+d9zMZhAAAgFW9ja8mOn78uHJzc7Vx40Zt2bJFr776qg4ePOj/+zNnzmjBggVavXq13nzzTfXq1UvPPvvseT+XQQgAADjG6/WqrKyswcvr9Vr227t3r+Lj4xUWFia3260RI0aosLDQ//e1tbVasGCBIiMjJUm9evVSeXn5eY/PGiEAAGBh5yM21q1bp5UrVzZ4f8aMGZo5c6Z/u6KiQuHh4f7tiIgIlZaW+rc7duyoYcOGSZKqq6u1evVq3XXXXec9PoMQAABwzKRJkzRu3LgG77dv396y7QswnLlcrgbvnT59WtOnT1d0dHTAz/0uBiEAAGBlYyLUvn37BkNPIJGRkSopKfFvV1RUKCIiwrJPRUWFMjIyFB8fr7lz5zbp+KwRAgAArd7AgQPl8Xh06tQpVVVVqaioSImJif6/r6ur09SpUzVq1Cg9+uijAdOiQEiEAACAVX3ru49QZGSksrKylJ6ertraWqWmpiomJkZTpkxRZmamjh07pj//+c+qq6vT9u3bJUl9+vTRokWLGv1cly/QSbdWbnhImtMlAABgm6LaV2w93qhec2w71m/3Z9t2rEBIhAAAgNXll5FcNNYIAQAAYzEIAQAAY3FqDAAAWHFqDAAAIPiRCAEAACsSIQAAgOBHIgQAAKxa4Q0VWwqJEAAAMBaJEAAAsPLVO12BbUiEAACAsUiEAACAFVeNAQAABD8SIQAAYMVVY4gb1V8vfJSjNX9cpnm/ekDudledc99Za6YpNWu0jdW1PJP7N7l3if7p39z+Te7dZAxCAXTo0k6zXpyqhXfkKqPPgyr/rEIZiyc02O+66G5aUjRPianxDlTZckzu3+TeJfqnf3P7N7n3gHw++14Os20Q2r17t7xeryTpjTfe0MKFC/XrX//arsNfkNikGO0vOaSjB49JkrauKtbQCbc02G/MtBHavm6X3nntPbtLbFEm929y7xL907+5/Zvcu+lsGYQWLVqkVatWqaamRj/72c9UUFCgqKgoFRcX68knn7SjhAsS/v3Oqiw76d+uLDupqzu4G8Skz92/Vm/n7ba7vBZncv8m9y7RP/2b27/JvQdkUCJky2LpPXv2qKCgQG3atNHOnTuVn5+v0NBQ3XnnnRo9uvWdY3VdEXg+rK8z4wZTJvdvcu8S/dO/uf2b3LvpbEmErrzySp08+fdJu3Pnzvrmm28kSVVVVWrbtvVduFZ55IQ6de3o3+5ybSd5T51R9Tc1DlZlH5P7N7l3if7p39z+Te7ddLYMQjNmzFBqaqpycnLUo0cP3XXXXVq8eLHuuOMO3X333XaUcEE+LC5V7wFR6hbVVZI0+t5h8hSUOFyVfUzu3+TeJfqnf3P7N7n3gAw6NWbLIDR06FDl5eUpIiJCtbW16tevn66++mplZ2dr/PjxdpRwQb6s9Grp5Bf02KtZerH0GXXv869a/fAv1TO2h54vyXa6vBZncv8m9y7RP/2b27/JvZvO5fO1gnHsAg0PSXO6BAAAbFNU+4qtxxt1zX22Heu35c/ZdqxAuI8QAAAwVutbqQwAAJx1+Z0sumgkQgAAwFgkQgAAwIpECAAAIPiRCAEAAKt6EiEAAICgRyIEAAAsfD5znrFGIgQAAIxFIgQAAKxYIwQAABD8SIQAAIAV9xECAAAIfgxCAADAWJwaAwAAVvVcPg8AABD0SIQAAIAVi6UBAACCH4kQAACw8LFGCAAAIPiRCAEAACvWCAEAAAQ/EiEAAGDFQ1cBAACCH4kQAACw8nHVGAAAQNAjEQIAABY+1ggBAAAEPxIhAABgxRohAACA4McgBAAAjMWpMQAAYMFiaQAAAAOQCAEAACuDFku7fD6DHjELAADwTzg1BgAAjMUgBAAAjMUgBAAAjMUgBAAAjMUgBAAAjMUgBAAAjMUgBAAAjMUgBAAAjMUgBAAAjMUgdAEKCgqUnJyspKQk5eXlOV2O7c6cOaPRo0errKzM6VJst3LlSqWkpCglJUVLlixxuhzbLV++XMnJyUpJSdHatWudLscxOTk5mjNnjtNl2C49PV0pKSkaO3asxo4dq3379jldkq127Nih8ePHa+TIkXryySedLgfNjGeNNdHx48eVm5ur119/XaGhoUpLS9OAAQMUFRXldGm22Ldvn+bNm6fPP//c6VJst3fvXr377rvavHmzXC6XJk+erOLiYiUlJTldmi3ef/99vffee3rzzTd19uxZJScna9CgQerRo4fTpdnK4/Fo8+bNGjx4sNOl2Mrn8+nw4cPauXOn2rY1738ZR44c0fz587Vp0yZ17txZkyZN0q5duzRo0CCnS0MzIRFqor179yo+Pl5hYWFyu90aMWKECgsLnS7LNvn5+Zo/f74iIiKcLsV24eHhmjNnjkJDQxUSEqLrr79eR48edbos28TFxWn9+vVq27atTp48qbq6OrndbqfLstWXX36p3NxcTZ061elSbHf48GG5XC5NmTJFY8aM0YYNG5wuyVbFxcVKTk5W165dFRISotzcXPXt29fpstCMzBvvL1JFRYXCw8P92xERESotLXWwInstWrTI6RIc07NnT/+fP//8c23btk2vvPKKgxXZLyQkRCtWrNBLL72kkSNHKjIy0umSbPX4448rKytL5eXlTpdiO6/Xq4SEBC1YsEDV1dVKT0/XD37wA918881Ol2aLL774QiEhIcrIyFBlZaWGDBmiBx54wOmy0IxIhJrI5/M1eM/lcjlQCZzy6aef6p577tHs2bPVvXt3p8uxXWZmpjwej8rLy5Wfn+90ObbZtGmTrrnmGiUkJDhdiiP69++vJUuWyO12q1OnTkpNTdWuXbucLss2dXV18ng8evrpp5Wfn6+PP/5YmzdvdrosNCMGoSaKjIzUiRMn/NsVFRVGniYy1Ycffqgf//jHeuihhzRu3Diny7HVoUOH9Je//EWSdNVVV2n48OHav3+/w1XZZ9u2bdqzZ4/Gjh2rFStWaMeOHVq8eLHTZdmmpKREHo/Hv+3z+YxaK9SlSxclJCSoU6dOuvLKK3XrrbcadTbABAxCTTRw4EB5PB6dOnVKVVVVKioqUmJiotNlwQbl5eW67777tHTpUqWkpDhdju3Kyso0b948ffvtt/r222/19ttvKzY21umybLN27Vpt3bpVW7ZsUWZmpoYOHaq5c+c6XZZtTp8+rSVLlqimpkZnzpzR5s2bjblQQJKGDBmid999V16vV3V1ddq9e7duvPFGp8tCMzJnrL9EkZGRysrKUnp6umpra5WamqqYmBiny4IN1qxZo5qaGmVnZ/vfS0tL04QJExysyj6DBg3Svn37dNttt6lNmzYaPny4kQOhqYYMGeL//uvr6zVx4kT179/f6bJs07dvX02ePFkTJ05UbW2tbr75Zt1+++1Ol4Vm5PIFWvwCAABgAE6NAQAAYzEIAQAAYzEIAQAAYzEIAQAAYzEIAQAAYzEIAQAAYzEIAQAAYzEIAWiSUaNGKTExUZ9++qnTpQBAs2EQAtAkW7duVffu3bV9+3anSwGAZsMgBKBJ2rRpo9jYWKMeuAog+PGsMQBNUl1drd/85jfiqTwAggmJEIAmyc3NVWRkpI4cOaKvv/7a6XIAoFkwCAE4r9///vcqLCzUs88+q3bt2unAgQNOlwQAzYJBCECjampq9Mgjj+iJJ55QWFiYoqOjWScEIGgwCAFo1PLly9W/f38NHjxYkhQdHa1PPvnE2aIAoJkwCAE4p9LSUhUWFmru3Ln+93r37k0iBCBouHxcAgIAAAxFIgQAAIzFIAQAAIzFIAQAAIzFIAQAAIzFIAQAAIzFIAQAAIzFIAQAAIzFIAQAAIz1v5zuTSzilSuRAAAAAElFTkSuQmCC\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",
diff --git a/doc/web/course.do.txt b/doc/web/course.do.txt
index 00f118d71..ae14c13f5 100644
--- a/doc/web/course.do.txt
+++ b/doc/web/course.do.txt
@@ -252,7 +252,7 @@ Acronyms for textbooks and references to chapter
 |----------------------------------------------------------------------------------------------------------------------------|
 | Week 39 | Logistic regression and optimization | Project 1  | "Lecture notes":"https://compphysics.github.io/MachineLearning/doc/pub/Bayesian/html/Bayesian-bs.html" | Work on Project 1|
 |----------------------------------------------------------------------------------------------------------------------------|
-| Week 40 | Neural Networks | Presentation of project 2, deadline November 5  | "Lecture notes":"https://compphysics.github.io/MachineLearning/doc/pub/Statistics/html/Statistics-bs.html" | Deadline project 1, October 1|
+| Week 40 | Neural Networks | Presentation of project 2, deadline November 12  | "Lecture notes":"https://compphysics.github.io/MachineLearning/doc/pub/Statistics/html/Statistics-bs.html" | Deadline project 1, October 1|
 |----------------------------------------------------------------------------------------------------------------------------|
 | Week 41 | Neural Networks | Project 2  | "Lecture notes":"https://compphysics.github.io/MachineLearning/doc/pub/Statistics/html/Statistics-bs.html" | Work on project 2 |
 |----------------------------------------------------------------------------------------------------------------------------|
@@ -262,11 +262,11 @@ Acronyms for textbooks and references to chapter
 |----------------------------------------------------------------------------------------------------------------------------|
 | Week 44 | SVM and tree and forest models | Project 2  | HTF chapter 9 and "lecture notes":"https://compphysics.github.io/MachineLearning/doc/pub/NeuralNet/html/NeuralNet-bs.html" | Work on project 2 |
 |----------------------------------------------------------------------------------------------------------------------------|
-| Week 45 | Unsupervised learning, Boltzmann machines | Presentation and discussion of project 3  | HTF chapter 14 and "lecture notes":"https://compphysics.github.io/MachineLearning/doc/pub/BM/html/BM-bs.html" | Deadline project 2 November 5 |
+| Week 45 | SVM and tree and forest models | Presentation and discussion of project 3  | HTF chapter 14 and "lecture notes":"https://compphysics.github.io/MachineLearning/doc/pub/BM/html/BM-bs.html" | Deadline project 2 November 12 |
 |----------------------------------------------------------------------------------------------------------------------------|
 | Week 46 | Bayesian statitics | Project 3  | TBA | Work on project 3 |
 |----------------------------------------------------------------------------------------------------------------------------|
-| Week 47 | Bayesian statistics | Project 3  | TBA | Work on project 3 |
+| Week 47 | Unsupervised learning, Boltzmann machines | Project 3  | TBA | Work on project 3 |
 |----------------------------------------------------------------------------------------------------------------------------|
 | Week 48 | Summary of course and final workshop | Project 3  | Lecture notes | Final workshop with presentation of project 3 on November 30|
 |----------------------------------------------------------------------------------------------------------------------------|
diff --git a/doc/web/course.html b/doc/web/course.html
index bc9a32bbe..fcc56918f 100644
--- a/doc/web/course.html
+++ b/doc/web/course.html
@@ -845,14 +845,14 @@ Acronyms for textbooks and references to chapter
 <tr><td align="center">   Week 37          </td> <td align="center">   Classification and logistic regression                  </td> <td align="center">   <a href="https://compphysics.github.io/MachineLearning/doc/Projects/2018/Project1/html/Project1-bs.html" target="_self">Presentation of Project 1, deadline October 1</a>    </td> <td align="center">   HTF chapter 4 and <a href="https://compphysics.github.io/MachineLearning/doc/pub/Regression/html/Regression-bs.html" target="_self">lecture notes</a>               </td> <td align="center">   Work on project 1                                                                               </td> </tr>
 <tr><td align="center">   Week 38          </td> <td align="center">   Optimization methods                                    </td> <td align="center">   Exercises and project 1                                                                                                                                                      </td> <td align="center">   HTF chapter 5 and <a href="https://compphysics.github.io/MachineLearning/doc/pub/Splines/html/Splines-bs.html" target="_self">lecture notes</a>                     </td> <td align="center">   Work on project 1, deadline October 1                                                           </td> </tr>
 <tr><td align="center">   Week 39          </td> <td align="center">   Logistic regression and optimization                    </td> <td align="center">   Project 1                                                                                                                                                                    </td> <td align="center">   <a href="https://compphysics.github.io/MachineLearning/doc/pub/Bayesian/html/Bayesian-bs.html" target="_self">Lecture notes</a>                                     </td> <td align="center">   Work on Project 1                                                                               </td> </tr>
-<tr><td align="center">   Week 40          </td> <td align="center">   Neural Networks                                         </td> <td align="center">   Presentation of project 2, deadline November 5                                                                                                                               </td> <td align="center">   <a href="https://compphysics.github.io/MachineLearning/doc/pub/Statistics/html/Statistics-bs.html" target="_self">Lecture notes</a>                                 </td> <td align="center">   Deadline project 1, October 1                                                                   </td> </tr>
+<tr><td align="center">   Week 40          </td> <td align="center">   Neural Networks                                         </td> <td align="center">   Presentation of project 2, deadline November 12                                                                                                                              </td> <td align="center">   <a href="https://compphysics.github.io/MachineLearning/doc/pub/Statistics/html/Statistics-bs.html" target="_self">Lecture notes</a>                                 </td> <td align="center">   Deadline project 1, October 1                                                                   </td> </tr>
 <tr><td align="center">   Week 41          </td> <td align="center">   Neural Networks                                         </td> <td align="center">   Project 2                                                                                                                                                                    </td> <td align="center">   <a href="https://compphysics.github.io/MachineLearning/doc/pub/Statistics/html/Statistics-bs.html" target="_self">Lecture notes</a>                                 </td> <td align="center">   Work on project 2                                                                               </td> </tr>
 <tr><td align="center">   Week 42          </td> <td align="center">   Neural Networks                                         </td> <td align="center">   Project 2                                                                                                                                                                    </td> <td align="center">   HTF chapter 11 and <a href="https://compphysics.github.io/MachineLearning/doc/pub/NeuralNet/html/NeuralNet-bs.html" target="_self">lecture notes</a>                </td> <td align="center">   Work on project 2                                                                               </td> </tr>
 <tr><td align="center">   Week 43          </td> <td align="center">   Dimensionality reduction and support vector machines    </td> <td align="center">   Project 2                                                                                                                                                                    </td> <td align="center">   HTF chapters 3 and 12 and <a href="https://compphysics.github.io/MachineLearning/doc/pub/NeuralNet/html/NeuralNet-bs.html" target="_self">lecture notes</a>         </td> <td align="center">   Work on project 2                                                                               </td> </tr>
 <tr><td align="center">   Week 44          </td> <td align="center">   SVM and tree and forest models                          </td> <td align="center">   Project 2                                                                                                                                                                    </td> <td align="center">   HTF chapter 9 and <a href="https://compphysics.github.io/MachineLearning/doc/pub/NeuralNet/html/NeuralNet-bs.html" target="_self">lecture notes</a>                 </td> <td align="center">   Work on project 2                                                                               </td> </tr>
-<tr><td align="center">   Week 45          </td> <td align="center">   Unsupervised learning, Boltzmann machines               </td> <td align="center">   Presentation and discussion of project 3                                                                                                                                     </td> <td align="center">   HTF chapter 14 and <a href="https://compphysics.github.io/MachineLearning/doc/pub/BM/html/BM-bs.html" target="_self">lecture notes</a>                              </td> <td align="center">   Deadline project 2 November 5                                                                   </td> </tr>
+<tr><td align="center">   Week 45          </td> <td align="center">   SVM and tree and forest models                          </td> <td align="center">   Presentation and discussion of project 3                                                                                                                                     </td> <td align="center">   HTF chapter 14 and <a href="https://compphysics.github.io/MachineLearning/doc/pub/BM/html/BM-bs.html" target="_self">lecture notes</a>                              </td> <td align="center">   Deadline project 2 November 12                                                                  </td> </tr>
 <tr><td align="center">   Week 46          </td> <td align="center">   Bayesian statitics                                      </td> <td align="center">   Project 3                                                                                                                                                                    </td> <td align="center">   TBA                                                                                                                                                                 </td> <td align="center">   Work on project 3                                                                               </td> </tr>
-<tr><td align="center">   Week 47          </td> <td align="center">   Bayesian statistics                                     </td> <td align="center">   Project 3                                                                                                                                                                    </td> <td align="center">   TBA                                                                                                                                                                 </td> <td align="center">   Work on project 3                                                                               </td> </tr>
+<tr><td align="center">   Week 47          </td> <td align="center">   Unsupervised learning, Boltzmann machines               </td> <td align="center">   Project 3                                                                                                                                                                    </td> <td align="center">   TBA                                                                                                                                                                 </td> <td align="center">   Work on project 3                                                                               </td> </tr>
 <tr><td align="center">   Week 48          </td> <td align="center">   Summary of course and final workshop                    </td> <td align="center">   Project 3                                                                                                                                                                    </td> <td align="center">   Lecture notes                                                                                                                                                       </td> <td align="center">   Final workshop with presentation of project 3 on November 30                                    </td> </tr>
 </tbody>
 </table>