You're reading from Deep Reinforcement Learning Hands-On. - Second Edition

Product typeBook

Published inJan 2020

Reading LevelIntermediate

PublisherPackt

ISBN-139781838826994

Edition2nd Edition

Languages

Python

Tools

Keras TensorFlow

Concepts

Deep Reinforcement Learning

Author (1)

Maxim Lapan

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 wasn't something that you wanted to repeat over and over again by experimenting with different NN architectures.

Luckily, those days have gone now, much like programming your hardware using a soldering iron and vacuum tubes! Now, defining an NN of hundreds of layers requires nothing more than...

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Deep Reinforcement Learning Hands-On. - Second Edition

Published in: Jan 2020Publisher: PacktISBN-13: 9781838826994

Author (1)

Maxim Lapan

Maxim has been working as a software developer for more than 20 years and was involved in various areas: distributed scientific computing, distributed systems and big data processing. Since 2014 he is actively using machine and deep learning to solve practical industrial tasks, such as NLP problems, RL for web crawling and web pages analysis. He has been living in Germany with his family.
Read more about Maxim Lapan

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You're reading from Deep Reinforcement Learning Hands-On. - Second Edition

Gradients

Unlock this book and the full library FREE for 7 days

Author (1)

TensorFlow Reinforcement Learning Quick Start Guide

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