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
Chapter 9. Modeling probabilities and nonlinearities: activation functions
Chapter 10. Neural learning about edges and corners: intro to convolutional neural networks
Chapter 11. Neural networks that understand language: king – man + woman == ?
Chapter 12. Neural networks that write like Shakespeare: recurrent layers for variable-length data
Chapter 13. Introducing automatic optimization: let’s build a deep learning framework
Chapter 14. Learning to write like Shakespeare: long short-term memory
Chapter 15. Deep learning on unseen data: introducing federated learning
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