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Deep Learning with TensorFlow

Deep Learning with TensorFlow

By : Zaccone, Karim
3 (4)
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Deep Learning with TensorFlow

Deep Learning with TensorFlow

3 (4)
By: Zaccone, Karim

Overview of this book

Deep learning is a branch of machine learning algorithms based on learning multiple levels of abstraction. Neural networks, which are at the core of deep learning, are being used in predictive analytics, computer vision, natural language processing, time series forecasting, and to perform a myriad of other complex tasks. This book is conceived for developers, data analysts, machine learning practitioners and deep learning enthusiasts who want to build powerful, robust, and accurate predictive models with the power of TensorFlow, combined with other open source Python libraries. Throughout the book, you’ll learn how to develop deep learning applications for machine learning systems using Feedforward Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Autoencoders, and Factorization Machines. Discover how to attain deep learning programming on GPU in a distributed way. You'll come away with an in-depth knowledge of machine learning techniques and the skills to apply them to real-world projects.
Table of Contents (13 chapters)
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12
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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