Repo Structure Refactoring + Merge from PyData London Version

leriomaggio · Jun 20, 2017 · 5dff21c · 5dff21c
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diff --git a/0. Preamble.ipynb b/0. Preamble.ipynb
@@ -53,44 +53,45 @@
     "\n",
     "- **Part I**: **Introduction**\n",
     "\n",
-    "    - Intro to Deep Learning and ANN\n",
+    "    - Intro to Artificial Neural Networks\n",
     "        - Perceptron and MLP    \n",
-    "\t- naive pure-Python implementation\n",
+    "        - naive pure-Python implementation\n",
     "        - fast forward, sgd, backprop\n",
     "        \n",
-    "    - Intro to Tensorflow \n",
-    "        - Model + SGD with Tensorflow\n",
-    "            \n",
-    "    - Introduction to Keras\n",
-    "        - Overview and main features\n",
-    "            - Keras Backend\n",
+    "    - Introduction to Deep Learning Frameworks\n",
+    "        - Intro to Theano\n",
+    "        - Intro to Tensorflow\n",
+    "        - Intro to Keras\n",
+    "            - Overview and main features\n",
     "            - Overview of the `core` layers\n",
-    "        - Multi-Layer Perceptron and Fully Connected\n",
-    "            - Examples with `keras.models.Sequential` and `Dense`\n",
-    "            - HandsOn: FC with keras\n",
+    "            - Multi-Layer Perceptron and Fully Connected\n",
+    "                - Examples with `keras.models.Sequential` and `Dense`\n",
+    "            - Keras Backend\n",
     "    \n",
-    "- **Part II**: **Supervised Learning and Convolutional Neural Nets**\n",
+    "- **Part II**: **Supervised Learning **\n",
     "    \n",
-    "    - Intro: Focus on Image Classification\n",
-    "\n",
-    "    - Intro to ConvNets\n",
+    "    - Fully Connected Networks and Embeddings\n",
+    "        - Intro to MNIST Dataset\n",
+    "        - Hidden Leayer Representation and Embeddings\n",
+    "        \n",
+    "    - Convolutional Neural Networks\n",
     "        - meaning of convolutional filters\n",
     "            - examples from ImageNet    \n",
     "        - Visualising ConvNets \n",
     "\n",
-    "    - Advanced CNN\n",
-    "        - Dropout\n",
-    "        - MaxPooling\n",
-    "        - Batch Normalisation\n",
-    "\t\t\n",
-    "    - HandsOn: MNIST Dataset\n",
-    "        - FC and MNIST\n",
-    "        - CNN and MNIST\n",
+    "        - Advanced CNN\n",
+    "            - Dropout\n",
+    "            - MaxPooling\n",
+    "            - Batch Normalisation\n",
+    "\n",
+    "        - HandsOn: MNIST Dataset\n",
+    "            - FC and MNIST\n",
+    "            - CNN and MNIST\n",
     "        \n",
-    "    - Deep Convolutiona Neural Networks with Keras (ref: `keras.applications`)\n",
-    "        - VGG16\n",
-    "        - VGG19\n",
-    "        - ResNet50\n",
+    "        - Deep Convolutiona Neural Networks with Keras (ref: `keras.applications`)\n",
+    "            - VGG16\n",
+    "            - VGG19\n",
+    "            - ResNet50\n",
     "    - Transfer Learning and FineTuning\n",
     "    - Hyperparameters Optimisation \n",
     "        \n",
@@ -104,12 +105,9 @@
     "- **Part IV**: **Recurrent Neural Networks**\n",
     "    - Recurrent Neural Network in Keras \n",
     "        -  `SimpleRNN`, `LSTM`, `GRU`\n",
+    "    - LSTM for Sentence Generation\n",
     "\t\t\n",
     "- **PartV**: **Additional Materials**:  \n",
-    "   - Quick tutorial on `theano`\n",
-    "   - Perceptron and Adaline (pure-python) implementations \n",
-    "   - MLP and MNIST (pure-python)\n",
-    "   - LSTM for Sentence Generation\n",
     "   - Custom Layers in Keras \n",
     "   - Multi modal Network Topologies with Keras"
    ]
@@ -265,7 +263,9 @@
   {
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-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "import numpy as np\n",