Intro to Neural Networks - WORKSHOP

[Slides] (https://slides.com/chunkzer/deck)

If you don't have Python:

For OSx users:

• $ brew install python 2.7

For Linux

• $ sudo add-apt-repository ppa:fkrull/deadsnakes
• $ sudo apt-get update
• $ sudo apt-get install python2.7

If you don't have pip:

• $ sudo easy_install pip
• $ sudo easy_install --upgrade six

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Additional resources:

For Neural Networks:

• http://karpathy.github.io/neuralnets/

-- Most pragmatic explanation of backpropagation and gradient descent I've seen.

• http://neuralnetworksanddeeplearning.com/

-- Michael Nielsens ongoing "book." Very detailed in its steps.

For Convolutional Neural Networks

• http://colah.github.io/posts/2014-07-Understanding-Convolutions/

For Reccurrent Neural Networks:

• http://colah.github.io/posts/2015-08-Understanding-LSTMs/

-- Excellent explanation of RNN inner workings.

• http://karpathy.github.io/2015/05/21/rnn-effectiveness/

-- Excellent demonstration of RNN Power.

• https://github.com/karpathy/char-rnn

-- Original char-rnn python implementation. Done using Torch.

• https://openai.com/blog/generative-models/

-- Overview of the cutting edge on generative models. Their current state and possible future.