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      Reinforcement Learning: With Open AI, TensorFlow and Keras Using Python

      1 in stock

      Firm sale: non returnable item
      SKU 9781484232842 Categories ,
      Select Guide Rating
      Master reinforcement learning, a popular area of machine learning, starting with the basics: discover how agents and the environment evolve and then gain a clear picture of how they are inter-related. You’ll then work with theories related to reinforcement learning and see t...

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      Description

      Product ID:9781484232842
      Product Form:Paperback / softback
      Country of Manufacture:GB
      Title:Reinforcement Learning
      Subtitle:With Open AI, TensorFlow and Keras Using Python
      Authors:Author: Abhishek Nandy, Manisha Biswas
      Page Count:167
      Subjects:Programming and scripting languages: general, Programming & scripting languages: general, Artificial intelligence, Artificial intelligence
      Description:Select Guide Rating
      Master reinforcement learning, a popular area of machine learning, starting with the basics: discover how agents and the environment evolve and then gain a clear picture of how they are inter-related. You’ll then work with theories related to reinforcement learning and see the concepts that build up the reinforcement learning process. Reinforcement Learning discusses algorithm implementations important for reinforcement learning, including Markov’s Decision process and Semi Markov Decision process. The next section shows you how to get started with Open AI  before looking at Open AI Gym. You’ll then learn about Swarm Intelligence with Python in terms of reinforcement learning. The last part of the book starts with the TensorFlow environment and gives an outline of how reinforcement learning can be applied to TensorFlow. There’s also coverage of Keras, a framework that can be used with reinforcement learning. Finally, you'll delve into Google’s Deep Mind and see scenarios where reinforcement learning can be used. What You'll Learn Absorb the core concepts of the reinforcement learning processUse advanced topics of deep learning and AIWork with Open AI Gym, Open AI, and Python Harness reinforcement learning with TensorFlow and Keras using PythonWho This Book Is ForData scientists, machine learning and deep learning professionals, developers who want to adapt and learn reinforcement learning.
      Master reinforcement learning, a popular area of machine learning, starting with the basics: discover how agents and the environment evolve and then gain a clear picture of how they are inter-related. You''ll then work with theories related to reinforcement learning and see the concepts that build up the reinforcement learning process. 

      Reinforcement Learning discusses algorithm implementations important for reinforcement learning, including Markov''s Decision process and Semi Markov Decision process. The next section shows you how to get started with Open AI  before looking at Open AI Gym. You''ll then learn about Swarm Intelligence with Python in terms of reinforcement learning.
       
      The last part of the book starts with the TensorFlow environment and gives an outline of how reinforcement learning can be applied to TensorFlow. There''s also coverage of Keras, a framework that can be used with reinforcement learning. Finally, you''ll delve into Google''s Deep Mind and see scenarios where reinforcement learning can be used. 

      What You''ll Learn 
      • Absorb the core concepts of the reinforcement learning process
      • Use advanced topics of deep learning and AI
      • Work with Open AI Gym, Open AI, and Python 
      • Harness reinforcement learning with TensorFlow and Keras using Python

      Who This Book Is For

      Data scientists, machine learning and deep learning professionals, developers who want to adapt and learn reinforcement learning.



      Imprint Name:APress
      Publisher Name:APress
      Country of Publication:GB
      Publishing Date:2017-12-08

      Additional information

      Weight308 g
      Dimensions156 × 239 × 14 mm