This tutorial requires tensorflow, keras and IPython with the IPython Notebook. These can be installed with pip by typing the following in Terminal:

pip install --upgrade pip
pip install scipy
pip install numpy pandas sklearn ipython matplotlib
pip install tensorflow keras
pip install jupyter

Next, clone the material in this tutorial using git as follows:

git clone https://github.com/savarin/neural-networks.git

We will be reviewing the materials with the IPython Notebook. You should be able to type

jupyter notebook

in your terminal window and see the notebook panel load in your web browser.

This tutorial is designed to get the audience training neural networks at the end of a 1-hour session. In particular, it covers areas where neural networks really shines - CNNs and RNNs. These techniques are applied on the Kaggle Titanic, MNIST and Rotten Tomatoes datasets.

• 1-0_Prelude
• 1-1_Basic_NN-Titanic
• 2-1_Basic_NN-MNIST
• 2-2_Regularized_NN
• 2-3_CNN
• 3-3_RNN
• Appendix-SGD

Code-only versions of these notebooks can be found here.

Special thanks for the excellent materials by Andrej Karpathy and the CS231n teaching staff, Chris Olah and Denny Britz (do check out their posts!), as well as the teams at Google TensorFlow, Keras and Kaggle.