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Mastering Reinforcement Learning Python by Bilgin Enes (18 results)
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Mastering Reinforcement Learning with Python: Build next-generation, self-learning models using reinforcement learning techniques and best practices
Published by Packt Publishing (edition ), 2020
ISBN 10: 1838644148 ISBN 13: 9781838644147
Language: English
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US$ 39.80
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Mastering Reinforcement Learning with Python: Build next-generation, self-learning models using reinforcement learning techniques and best practices
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paperback. Condition: Very Good. Cover is in excellent condition. The binding is in good shape. The pages of this book are clean and unmarked. See photos. FAST SHIPPING & FREE TRACKING!
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Mastering Reinforcement Learning with Python: Build next-generation, self-learning models using reinforcement learning techniques and best practices (Paperback or Softback)
Published by Packt Publishing 12/18/2020, 2020
ISBN 10: 1838644148 ISBN 13: 9781838644147
Language: English
Paperback or Softback. Condition: New. Mastering Reinforcement Learning with Python: Build next-generation, self-learning models using reinforcement learning techniques and best practices 1.99. Book.
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Mastering Reinforcement Learning with Python: Build next-generation, self-learning models using reinforcement learning techniques and best practices
Published by Packt Publishing 2020-12, 2020
ISBN 10: 1838644148 ISBN 13: 9781838644147
Language: English
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Mastering Reinforcement Learning with Python: Build next-generation, self-learning models using reinforcement learning techniques and best practices
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Mastering Reinforcement Learning with Python: Build next-generation, self-learning models using reinforcement learning techniques and best practices
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Mastering Reinforcement Learning with Python: Build next-generation, self-learning models using reinforcement learning techniques and best practices
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US$ 64.53
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Add to basketCondition: New. This book focuses on expert-level explanations and implementations of scalable reinforcement learning algorithms and approaches. Starting with the fundamentals, the book covers state-of-the-art methods from bandit problems to meta-reinforcement learning. Yo.
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Mastering Reinforcement Learning with Python
Published by Packt Publishing Limited, 2020
ISBN 10: 1838644148 ISBN 13: 9781838644147
Language: English
PAP. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
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Mastering Reinforcement Learning with Python Build nextgeneration, selflearning models using reinforcement learning techniques and best practices
Published by Packt Publishing Limited, 2020
ISBN 10: 1838644148 ISBN 13: 9781838644147
Language: English
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Mastering Reinforcement Learning with Python: Build next-generation self-learning models using reinforcement learning techniques and best practices
Published by Packt Publishing Limited, 2020
ISBN 10: 1838644148 ISBN 13: 9781838644147
Language: English
Seller: THE SAINT BOOKSTORE, Southport, United Kingdom
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Add to basketPaperback / softback. Condition: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days 222.
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Add to basketTaschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Get hands-on experience in creating state-of-the-art reinforcement learning agents using TensorFlow and RLlib to solve complex real-world business and industry problems with the help of expert tips and best practicesKey Features:Understand how large-scale state-of-the-art RL algorithms and approaches workApply RL to solve complex problems in marketing, robotics, supply chain, finance, cybersecurity, and moreExplore tips and best practices from experts that will enable you to overcome real-world RL challengesBook Description:Reinforcement learning (RL) is a field of artificial intelligence (AI) used for creating self-learning autonomous agents. Building on a strong theoretical foundation, this book takes a practical approach and uses examples inspired by real-world industry problems to teach you about state-of-the-art RL.Starting with bandit problems, Markov decision processes, and dynamic programming, the book provides an in-depth review of the classical RL techniques, such as Monte Carlo methods and temporal-difference learning. After that, you will learn about deep Q-learning, policy gradient algorithms, actor-critic methods, model-based methods, and multi-agent reinforcement learning. Then, you'll be introduced to some of the key approaches behind the most successful RL implementations, such as domain randomization and curiosity-driven learning.As you advance, you'll explore many novel algorithms with advanced implementations using modern Python libraries such as TensorFlow and Ray's RLlib package. You'll also find out how to implement RL in areas such as robotics, supply chain management, marketing, finance, smart cities, and cybersecurity while assessing the trade-offs between different approaches and avoiding common pitfalls.By the end of this book, you'll have mastered how to train and deploy your own RL agents for solving RL problems.What You Will Learn:Model and solve complex sequential decision-making problems using RLDevelop a solid understanding of how state-of-the-art RL methods workUse Python and TensorFlow to code RL algorithms from scratchParallelize and scale up your RL implementations using Ray's RLlib packageGet in-depth knowledge of a wide variety of RL topicsUnderstand the trade-offs between different RL approachesDiscover and address the challenges of implementing RL in the real worldWho This Book Is For:This book is for expert machine learning practitioners and researchers looking to focus on hands-on reinforcement learning with Python by implementing advanced deep reinforcement learning concepts in real-world projects. Reinforcement learning experts who want to advance their knowledge to tackle large-scale and complex sequential decision-making problems will also find this book useful. Working knowledge of Python programming and deep learning along with prior experience in reinforcement learning is required.
