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Neural networks and deep learning

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If you benefit from the book, please make a small donation. I suggest $5, but you can choose the amount. Alternately, you can make a donation by sending me Bitcoin, at address 1Kd6tXH5SDAmiFb49J9hknG5pqj7KStSAx Thanks to all the supporters who made the book possible, with especial thanks to Pavel Dudrenov. Thanks also to all the contributors to the Bugfinder Hall of Fame. Michael Nielsen on Twitter Book FAQ Code repository Michael Nielsen's project announcement mailing list Deep Learning, book by Ian Goodfellow, Yoshua Bengio, and Aaron Courville cognitivemedium.com By Michael Nielsen / Dec 2019 In the last chapter we saw how neural networks can learn their weights and biases using the gradient descent algorithm. There was, however, a gap in our explanation: we didn't discuss how to compute the gradient of the cost function. That's quite a gap! In this chapter I'll explain a fast algorithm for computing such gradients, an algorithm known as backpropagation. The backpropagation algorith

Neural networks and deep learning CHAPTER 2 How the backpropagation algorithm works Neural Networks and Deep Learning What this book is about On the exercises and problems Using neural nets to recognize handwritten digits Perceptrons Sigmoid neurons The architecture of neural networks A simple network to classify handwritten digits Learning with gradient descent Implementing our network to classify digits Toward deep learning How the backpropagation algorithm works Warm up: a fast matrix-based approach to computing the output from a neural network The two assumptions we need about the cost fun

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