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Deep Learning with TensorFlow

You're reading from   Deep Learning with TensorFlow Explore neural networks and build intelligent systems with Python

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Product type Paperback
Published in Mar 2018
Publisher Packt
ISBN-13 9781788831109
Length 484 pages
Edition 2nd Edition
Languages
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Authors (2):
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Giancarlo Zaccone Giancarlo Zaccone
Author Profile Icon Giancarlo Zaccone
Giancarlo Zaccone
Md. Rezaul Karim Md. Rezaul Karim
Author Profile Icon Md. Rezaul Karim
Md. Rezaul Karim
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Table of Contents (13) Chapters Close

Preface 1. Getting Started with Deep Learning 2. A First Look at TensorFlow FREE CHAPTER 3. Feed-Forward Neural Networks with TensorFlow 4. Convolutional Neural Networks 5. Optimizing TensorFlow Autoencoders 6. Recurrent Neural Networks 7. Heterogeneous and Distributed Computing 8. Advanced TensorFlow Programming 9. Recommendation Systems Using Factorization Machines 10. Reinforcement Learning Other Books You May Enjoy Index

OpenAI Gym


OpenAI Gym is an open source Python framework developed by OpenAI, a non-profit AI research company, as a toolkit for developing and evaluating RL algorithms. It gives us a set of test problems, known as environments, that we can write RL algorithms to solve. This enables us to dedicate more of our time to implementing and improving the learning algorithm instead of spending a lot of time simulating the environment. In addition, it provides a medium for people to compare and review the algorithms of others.

OpenAI environments

OpenAI Gym has a collection of environments. At the time of writing this book, the following environments are available:

  • Classic control and toy text: Small-scale tasks from the RL literature.

  • Algorithmic: Performs computations such as adding multi-digit numbers and reversing sequences. Most of these tasks require memory, and their difficulty can be changed by varying the sequence length.

  • Atari: Classic Atari games, with screen images or RAM as input, using...

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