Kdd2018 workshop

kdd2018/README.md

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+# Deep Learning with Keras - Hands-on Workshop
+
+## What to expect:
+
+This workshop is intended for an audience that is new to Keras with a very basic knowledge of Deep Learning.
+
+Workshop Agenda:
+
+Section 1:
+
+* Introduction to Keras
+* How to build and train a model using Keras APIs
+* Using Callbacks
+
+
+Section 2
+
+* Building a CNN model using Keras Layers
+* Data augmentation with  ImageGenerator
+* Feature extraction with pre-trained CNN model
+* Introduciton to Fine Tuning
+
+
+Section 3
+
+* Word Embeddings in Keras
+* Using pre-trained word embeddings with the Embedding layer
+* Introduction to RNN layers
+
+
+Section 4
+
+* Workflow for solving Text Classification problems
+* Using a N-gram model
+* Using a Sequence model
+
+
+
+Installation instructions
+
+Required packages:
+
+* Install [Anaconda](https://www.anaconda.com/download/#macos) and create an environment that you can use for this workshop.
+
+* Install [TensorFlow](https://www.tensorflow.org/install/). There is a section specific to installing TensorFlow in a conda environment.
+
+* Install [Keras](https://keras.io/#installation).
+
+* Install [Pillow](https://pypi.org/project/Pillow/2.2.1/).
+
+* Confirm that TensorBoard is installed.
+
+Required Datasets:
+
+* Download the Cats Vs Dogs dataset from the [Kaggle website](https://www.kaggle.com/c/dogs-vs-cats/data). You will have to create an account if you don't have one already.
+
+* Download the GloVe pre-trained embedding from the [GloVe website](https://nlp.stanford.edu/projects/glove/).
+
+* Download the IMDB dataset from this [website](http://ai.stanford.edu/~amaas/data/sentiment/)
+
+* Download the Rotten Tomatotes dataset from the [Kaggle site](https://www.kaggle.com/c/sentiment-analysis-on-movie-reviews/data)
+
+Verify Installation:
+
+* git clone https://github.com/anj-s/kdd2018.git
+
+* Activate your conda environment
+
+* Start the Jupyter notebook by running "jupyter notebook"
+
+* Verify that you can run Keras and TensorFlow by running the "Verify Installation" jupyter notebook.
+
+NOTE:
+You will need to modify the data directory paths in the notebooks since you will be pointing to a local directory. 
+

kdd2018/Verify Installation.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'1.8.0'"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import tensorflow as tf\n",
+    "tf.__version__"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using TensorFlow backend.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'2.2.0'"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import keras\n",
+    "keras.__version__"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'5.2.0'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import PIL\n",
+    "PIL.__version__"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}