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Hands-On Reinforcement Learning with Python: Master reinforcement and deep reinforcement learning using OpenAI Gym and TensorFlow
96% of respondents would recommend this to a friend
SLE 623
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What Stands Out
Product Details
- Written by Sudharsan Ravichandiran, a data scientist, researcher, and artificial intelligence enthusiast
- Covers practical implementations of deep learning, reinforcement learning, natural language processing, and computer vision
- Provides hands-on guidance for mastering reinforcement and deep reinforcement learning using OpenAI Gym and TensorFlow
- Author has a background in information technology from Anna University
- Previously worked as a freelance web developer and designer with experience in designing award-winning websites
- Active open source contributor and frequently provides expertise on Stack Overflow
| Country of Origin | This item will be imported from US |
| Item Weight | 1.5 lbs (680 grams) |
| What is in the box | Hands-On Reinforcement Learning with... For more details, please check description/product details |
Who Should Buy?
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Aspiring Data Scientists
Ideal for individuals wanting to enhance their machine learning expertise with practical reinforcement learning applications.
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Developers and Engineers
Great for professionals looking to implement RL algorithms in software projects using Python and popular frameworks.
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Academic Researchers
Useful for researchers focusing on reinforcement learning who need hands-on practice alongside theoretical concepts.
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Beginners in Programming
Not suitable for complete beginners who lack fundamental programming knowledge or experience with Python.
Product Description
Hands-On Reinforcement Learning with Python: Master reinforcement and deep reinforcement learning using OpenAI Gym and TensorFlow
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Intelligence & Semantics Editorial Review
The book "Hands-On Reinforcement Learning with Python" has received mixed reviews from customers. While some readers found the examples and delivery of knowledge to be very good, labeling it as great for beginners, others have criticized its installation instructions, readability on Kindle, and language complexity. One reader expressed frustration with the installation instructions, as they didn’t work and ultimately caused issues on their Linux system. Additionally, another customer noted that the formulas in the text were unreadable while reading on a Kindle device. On the positive side, a few customers praised the book for offering clear examples and a solid mathematical background, making it suitable for machine learning practitioners. However, one reader found the topic to be too complex for a book that might have been authored by a non-native English speaker, although they acknowledged the author's effort and knowledge. Criticism was also directed at the lack of in-depth explanation in the book, with a reader mentioning the absence of proofs or comparisons of different algorithms. Others found the book poorly written and difficult to follow, advising potential buyers to download a sample before purchasing. Despite the mixed feedback, the book has been credited for its clear description and implementation of reinforcement learning algorithms. Overall, the reception of "Hands-On Reinforcement Learning with Python" appears to vary, with both positive and negative aspects affecting the customer experience.
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Pros
- Clear examples and solid mathematical background
- Useful for beginners and machine learning practitioners
- Clear description and implementation of reinforcement learning algorithms
Cons
- Readability on Kindle
Product Price History
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SLE 623
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