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90332320
Grokking Deep Reinforcement Learning
86% of respondents would recommend this to a friend
Item #:
90332320
ANG 140
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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.
Product Details
| 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.
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AI Enthusiasts
Those passionate about artificial intelligence and wishing to deepen their knowledge in reinforcement learning will find this book valuable.
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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.
Product Description
Grokking Deep Reinforcement Learning
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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.
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Pros
- Clear introduction to reinforcement learning algorithms
- Original numpy and pytorch code provided
- Engaging narrative accompanying technical content
- Solid foundation for understanding complex concepts
Product Price History
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Features & Benefits
- 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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