This article was written by Ian Goodfellow, Yoshua Bengio, and Aaron Courville. It consists of summaries, dozens of formulas, and numerous small sections that will help the beginner quickly grasp the essential of deep learning. The presentation style is very similar to a cheat sheet

Example of bad data science: over-fitting

Content:

Machine Learning

Generalization and Overfitting

Feedforward Networks

Designing the Output Layer

Finding θ

Choosing the Cost Function

Regularization

Deep Feedforward Networks

Designing Hidden Layers

Optimizaton Methods

Simplifying the Network

Convolution Networks

Pooling

Recurrent Networks

Useful Data Sets

Autoencoders

Representation Learning

Practical Advice

Appendix: Probability

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