Deep Reinforcement Learning Hands-On: Apply modern RL methods to practical problems of chatbots, robotics, discrete optimization, web automation, and more
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Deep Reinforcement Learning Hands-On: Apply modern RL methods to practical problems of chatbots, robotics, discrete optimization, web automation, and more
Deep Reinforcement Learning Hands-On is an updated and expanded version of the bestselling guide to the latest reinforcement learning tools and techniques.
Deep Reinforcement Learning Hands-On: Apply modern RL methods to practical problems of chatbots, robotics, discrete optimization, web automation, and more
商品＃: 52769558

Deep Reinforcement Learning Hands-On: Apply modern RL methods to practical problems of chatbots, robotics, discrete optimization, web automation, and

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商品＃: 52769558

JPY 16997

JPY 17518

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今すぐ注文すると 頃に届きます 水曜日, 10月 21
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Deep Reinforcement Learning Hands-On is an updated and expanded version of the bestselling guide to the latest reinforcement learning tools and techniques.
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この商品の利点

Practical Applications
Focuses on real-world problems like chatbots and robotics, ensuring learners can apply theoretical knowledge to practical scenarios, improving relevance in today's technology-driven landscape.
Modern Techniques
Covers contemporary reinforcement learning methods, equipping readers with cutting-edge skills that enhance their ability to tackle complex challenges across various domains.
Hands-On Approach
Emphasizes experiential learning through hands-on projects, which cultivates a deeper understanding of deep reinforcement learning concepts while encouraging experimentation and innovation.

製品詳細

Apply modern RL methods to practical problems of chatbots, robotics, discrete optimization, web automation, and more. Available at Ubuy 日本, a 日本.
  • New edition of the bestselling guide to deep reinforcement learning with expanded content
  • Revised and expanded to include multi-agent methods, discrete optimization, and RL in robotics
  • Covers advanced exploration techniques and practical applications of RL including chatbots, robotics, and web automation
  • Includes six new chapters on up-to-the-minute developments in RL
  • Provides actionable insights into deep Q-networks, policy gradient methods, and continuous control problems
  • Assumes fluency in Python and offers an introduction to deep RL with no background in RL required
Publisher Packt Publishing
Publication date 31 Jan. 2020
Edition 2nd Revised edition
Language English
Print length 826 pages
ISBN-10 1838826998
ISBN-13 978-1838826994
Item weight 1.51 kg
Dimensions 19.05 x 4.75 x 23.5 cm

どんな人にお勧めですか？

Suitable For
  • Aspiring Developers

    Those new to AI and machine learning seeking practical knowledge in reinforcement learning techniques for real-world applications.

  • Industry Professionals

    Professionals looking to enhance their skill set by applying modern reinforcement learning methods in fields like robotics or automation.

  • Students and Learners

    Students pursuing studies in computer science or AI who want hands-on experience with practical reinforcement learning problems.

Not Suitable For
  • Beginner Programmers

    Individuals with no prior programming or AI knowledge may find the content challenging and overwhelming without foundational skills.

製品説明書

About This Item

Experience the power of deep reinforcement learning with the second edition of "Deep Reinforcement Learning Hands-On." This comprehensive guide allows you to apply modern RL methods to practical problems across a range of industries, including chatbots, robotics, discrete optimization, web automation, and more. With this book, you'll dive into the world of deep reinforcement learning and gain a deep understanding of its concepts and techniques. You'll explore topics such as value-based methods, policy-based methods, and model-based methods to develop robust and efficient RL models. Whether you're interested in creating sophisticated chatbots, optimizing complex systems, or automating web processes, this book provides you with the knowledge and tools you need. It offers hands-on exercises and real-world examples to help you apply what you've learned and build practical solutions. The large print edition ensures that you can easily read and follow along with the content, making it accessible for readers of all skill levels.

The book is also up-to-date, featuring the latest advancements and techniques in deep reinforcement learning. Don't miss out on the opportunity to master deep reinforcement learning and revolutionize your approach to problem-solving. Get your copy of "Deep Reinforcement Learning Hands-On" today and take your skills to the next level.

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AI & Machine Learning Editorial Review

Deep Reinforcement Learning Hands-On: Apply modern RL methods to practical problems of chatbots, robotics, discrete optimization, web automation, and more is an exceptional resource for anyone interested in practical applications of Reinforcement Learning. This book stands out for its clear engineering descriptions and practical implementations, guiding readers from foundational concepts to complex applications. Reviewers highlight its structured approach, using real projects for a better grasp of the material, and its comprehensive coverage of various RL techniques. Although the illustrations are in black and white, the content quality and practical insights make it a primary reference, especially for those focused on coding in Python and Pytorch.

お客様のレビュー＆評価

4.5
175 カスタマー評価
  • 5 星
    71%
  • 4 星
    17%
  • 3 星
    7%
  • 2 星
    2%
  • 1 星
    3%

この商品のレビュー

お考えをお聞かせください

長所

  • Clear and concise explanations of core RL components
  • Practical implementations based on real projects
  • Excellent resource for both novices and experts
  • Comprehensive coverage of reinforcement learning techniques
  • Structured lessons that build on each other

短所

  • Diagrams are in black and white, which may be less engaging

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JPY 16997

JPY 17518

今すぐ注文すると 頃に届きます 水曜日, 10月 21

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特徴と利点

  • Learn the fundamentals of RL and implement complex deep learning models
  • Discover advanced exploration techniques like network distillation methods and build a practical hardware robot
  • Explore deep RL and its application to real-world problems such as cheap hardware robotics platforms
  • Evaluate RL methods such as cross-entropy, DQN, actor-critic, and others
  • Understand machine learning concept of RL and solve complex problems including discrete optimization and web automation

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