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Grokking Deep Reinforcement Learning
87% of respondents would recommend this to a friend
商品＃:
90332320
TWD 2662
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立即訂購並活動它 週五, 十月 09
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This common pattern is the foundation of deep reinforcement learning: building machine learning systems that explore and learn based on the responses of the environment.
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What Stands Out
Comprehensive Coverage
Offers an in-depth exploration of reinforcement learning concepts, algorithms, and applications, making it suitable for both beginners and experienced practitioners in artificial intelligence.
Practical Exercises
Includes hands-on coding exercises and real-world examples to solidify concepts, enabling readers to apply theoretical knowledge to practical scenarios effectively.
Accessible Language
Written in an easy-to-understand style, this book breaks down complex topics, ensuring that readers from diverse backgrounds can grasp key ideas and advance their learning.
產品詳情
- Written for developers with some understanding of deep learning algorithms. Experience with reinforcement learning is not required.Grokking Deep Reinforcement Learning introduces this powerful machine learning approach, using examples, illustrations, exercises, and crystal-clear teaching. You'll love the perfectly paced teaching and the clever, engaging writing style as you dig into this awesome exploration of reinforcement learning fundamentals, effective deep learning techniques, and practical applications in this emerging field.We all learn through trial and error. We avoid the things that cause us to experience pain and failure. We embrace and build on the things that give us reward and success. This common pattern is the foundation of deep reinforcement learning: building machine learning systems that explore and learn based on the responses of the environment.• Foundational reinforcement learning concepts and methods • The most popular deep reinforcement learning agents solving high-dimensional environments • Cutting-edge agents that emulate human-like behavior and techniques for artificial general intelligence Deep reinforcement learning is a form of machine learning in which AI agents learn optimal behavior on their own from raw sensory input. The system perceives the environment, interprets the results of its past decisions and uses this information to optimize its behavior for maximum long-term return.
| Publisher | Manning Publications |
| Publication date | 1 Jan. 2021 |
| Edition | 1st |
| Language | English |
| Print length | 465 pages |
| ISBN-10 | 1617295450 |
| ISBN-13 | 978-1617295454 |
| Item weight | 771 g |
| Dimensions | 18.73 x 2.54 x 23.5 cm |
Who Should Buy?
-
Aspiring Data Scientists
Individuals seeking to understand deep reinforcement learning concepts with practical examples and applications will benefit significantly.
-
AI Enthusiasts
Those passionate about artificial intelligence and wishing to deepen their knowledge in reinforcement learning will find this book valuable.
-
Graduate Students
Students pursuing advanced studies in AI and machine learning will find comprehensive insights and exercises beneficial for their learning.
-
Beginners in ML
Newcomers to machine learning without foundational knowledge may struggle with the advanced topics presented in this book.
產品描述
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客戶問題與解答
-
問題:
Who is the author of Grokking Deep Reinforcement Learning?
Answer: Miguel Morales. -
問題:
What type of readers is the book aimed at?
Answer: Developers with basic deep learning experience. -
問題:
What formats are included with the purchase of the print book?
Answer: PDF, Kindle, and ePub formats from Manning Publications.
AI & Machine Learning Editorial Review
Grokking Deep Reinforcement Learning offers a clear introduction to AI algorithms, featuring original numpy and pytorch code alongside an engaging narrative. Readers appreciate its structured approach to Policy Iteration and Policy Gradient algorithms, making it a solid resource for understanding and implementing reinforcement learning concepts. However, users have expressed dissatisfaction with the Kindle formatting, noting issues with code readability due to poor formatting and small text size. Despite these minor drawbacks, the swift responsiveness of the author to code-related queries enhances the reading experience, making it a recommended resource for anyone venturing into reinforcement learning.
Customer Reviews & Ratings
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優點
- Clear introduction to reinforcement learning algorithms
- Original numpy and pytorch code provided
- Engaging narrative accompanying technical content
- Solid foundation for understanding complex concepts
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TWD 2662
立即訂購並活動它 週五, 十月 09
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特色和優勢
- Introduces powerful machine learning approach
- Covers reinforcement learning fundamentals
- Teaches effective deep learning techniques
- Offers practical applications in the emerging field
- Includes engaging exercises and annotated Python code
- Provides crystal-clear teaching style
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