Brief Table of Contents
Chapter 1. Introducing deep learning: why you should learn it
Chapter 2. Fundamental concepts: how do machines learn?
Chapter 3. Introduction to neural prediction: forward propagation
Chapter 4. Introduction to neural learning: gradient descent
Chapter 5. Learning multiple weights at a time: generalizing gradient descent
Chapter 6. Building your first deep neural network: introduction to backpropagation
Chapter 7. How to picture neural networks: in your head and on paper
Chapter 8. Learning signal and ignoring noise: introduction to regularization and batching
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