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Deep Reinforcement Learning Hands-On: Apply modern RL methods to practical problems of chatbots, robotics, discrete optimization, web automation, and
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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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Produkt detaljer
- 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 |
Hvem passer produktet for?
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Aspiring Developers
Those new to AI and machine learning seeking practical knowledge in reinforcement learning techniques for real-world applications.
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Industry Professionals
Professionals looking to enhance their skill set by applying modern reinforcement learning methods in fields like robotics or automation.
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Students and Learners
Students pursuing studies in computer science or AI who want hands-on experience with practical reinforcement learning problems.
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Beginner Programmers
Individuals with no prior programming or AI knowledge may find the content challenging and overwhelming without foundational skills.
PRODUKTBESKRIVELSE
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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Er Deep Reinforcement Learning Hands-On: Apply modern tilgjengelig for nettbutikk i Norway?
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AI & Machine Learning Editorial Review
The second edition of "Deep Reinforcement Learning Hands-On" has received a mix of positive and negative reviews from customers. Many users found the book to be informative and helpful, particularly praising its clear explanations of complex concepts in plain English. They appreciated the practical approach of the book, which focuses on real-world problem-solving using reinforcement learning techniques. The inclusion of illustrations and code examples using Python libraries was also highlighted as a positive aspect. Some readers appreciated the coverage of both theoretical aspects and practical implementations of reinforcement learning, making it a valuable resource for those looking to apply RL algorithms in various fields. The division of content between theory and programming was seen as beneficial by many, allowing for a deeper understanding of the concepts presented. However, some users pointed out issues with obtaining the code download and mentioned difficulties in satisfying Python module requirements. Additionally, a few readers noted that the quality of content seemed to decline towards the end of the book. Overall, "Deep Reinforcement Learning Hands-On: Apply modern RL methods to practical problems of chatbots, robotics, discrete optimization, web automation, and more, 2nd Edition" is recommended for those interested in practical implementations of reinforcement learning algorithms, although readers should be prepared to potentially encounter some challenges. **
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Fordeler
- Clear explanations in plain English
- Practical approach to real-world problem-solving
- Inclusion of illustrations and code examples
- Coverage of both theoretical and practical aspects of reinforcement learning
- Division of content between theory and programming
Ulemper
- Difficulties in obtaining code download
Produktets prishistorikk
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NOK 1189
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Funksjoner og fordeler
- 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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