[ebook] Neural Networks and Deep Learning free online book.

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CHAPTER 1: Using neural nets to recognize handwritten digits

In this chapter we'll write a computer program implementing a neural network that learns to recognize handwritten digits. 

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CHAPTER 2: How the backpropagation algorithm works

In this chapter I'll explain a fast algorithm for computing such gradients, an algorithm known as backpropagation.

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CHAPTER 3:Improving the way neural networks learn

In this chapter I explain a suite of techniques which can be used to improve on our vanilla implementation of backpropagation, and so improve the way our networks learn.

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CHAPTER 4:A visual proof that neural nets can compute any function

In this chapter I give a simple and mostly visual explanation of the universality theorem. 

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CHAPTER 5:Why are deep neural networks hard to train?

In this chapter, we'll try training deep networks using our workhorse learning algorithm - stochastic gradient descent by backpropagation.

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CHAPTER 6:Deep learning

In this chapter, we'll develop techniques which can be used to train deep networks, and apply them in practice.

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