✳flâneur — a map of the web's best reading

Neural networks and deep learning

neuralnetworksanddeeplearning.com · 813 words · saved by 1 readers

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 It's not uncommon for technical books to include an admonition from the author that readers must do the exercises and problems. I always feel a little peculiar when I read such warnings. Will something bad happen to me if I don't do the exercises and problems? Of course not. I'll gain some time, but at the expense of depth of understanding. Sometimes that's worth it. Sometimes it's not. So what

Neural networks and deep learning On the exercises and problems 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 function The Hadamard p

Explore this link on the map →

related reading