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Deep Reinforcement Learning Hands-On
book

Deep Reinforcement Learning Hands-On

by Oleg Vasilev, Maxim Lapan, Martijn van Otterlo, Mikhail Yurushkin, Basem O. F. Alijla
June 2018
Intermediate to advanced content levelIntermediate to advanced
546 pages
13h 30m
English
Packt Publishing
Content preview from Deep Reinforcement Learning Hands-On

Gradients

Even with transparent GPU support, all of this dancing with tensors isn't worth bothering with, without one "killer feature": the automatic computation of gradients. This functionality was originally implemented in the Caffe toolkit and then became the de-facto standard in DL libraries. Computing gradients manually was extremely painful to implement and debug, even for the simplest neural network (NN). You had to calculate derivatives for all your functions, apply the chain rule, and then implement the result of the calculations, praying that everything was done right. This could be a very useful exercise for understanding the nuts and bolts of DL, but it's not something that you wanted to repeat over and over again by experimenting ...

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Publisher Resources

ISBN: 9781788834247Supplemental Content