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商品＃:
90328104
Grokking Deep Learning
87% of respondents would recommend this to a friend
商品＃:
90328104
TWD 2490
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立即訂購並活動它 週五, 十月 09
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Grokking Deep Learning teaches you to build deep learning neural networks from scratch!
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What Stands Out
Comprehensive Guide
Offers a thorough exploration of deep learning concepts, making complex topics accessible for readers with varying expertise levels, from beginners to advanced practitioners.
Hands-On Approach
Emphasizes practical applications and real-world examples, allowing readers to effectively implement deep learning techniques and reinforce learning through active engagement.
Updated Content
Includes insights into the latest advancements in deep learning, ensuring readers stay current with industry trends and technologies, enhancing the book's relevance and applicability.
產品詳情
- Introduces Artificial Intelligence and Deep Learning as revolutionary technologies
- Explains the behavior of neural networks based on human brains
- Suitable for readers with high school-level math and intermediate programming skills
- Covers building neural networks for image recognition, language translation, and gaming
- Provides insights into the impact of Deep Learning on various industries
- Includes guidance on understanding algorithms and the principles of Deep Learning
| Publisher | Manning Publications |
| Publication date | 22 May 2019 |
| Edition | 1st |
| Language | English |
| Print length | 336 pages |
| ISBN-10 | 1617293709 |
| ISBN-13 | 978-1617293702 |
| Item weight | 572 g |
| Dimensions | 18.75 x 1.78 x 23.5 cm |
Who Should Buy?
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Beginners in AI
Ideal for individuals new to artificial intelligence, offering clear explanations and foundational concepts in deep learning.
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Self-learners
Perfect for self-directed learners seeking a structured yet accessible approach to understanding deep learning principles.
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Educators and Tutors
Beneficial for teachers looking to incorporate deep learning concepts into their curriculum or enhance their instructional materials.
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Advanced Researchers
Less suitable for expert researchers, as it may not cover the latest advanced topics or cutting-edge research.
產品描述
Grokking Deep Learning
有疑問？ 和我們聊天
客戶問題與解答
-
問題:
What programming language is used in the book?
Answer: The book only uses Python and its math-supporting library, NumPy. -
問題:
Is the eBook version included with the print book purchase?
Answer: Yes, the purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. -
問題:
Who is the book suitable for?
Answer: The book is suitable for readers with high school-level math and intermediate programming skills.
AI & Machine Learning Editorial Review
Grokking Deep Learning, published by Manning Publications on May 22, 2019, is a revolutionary book that simplifies the complexities of deep learning, making it easy for readers regardless of their technical background. Many readers commend the author's ability to distill difficult concepts into digestible formats, noting that the writing is engaging and straightforward, creating an enjoyable learning experience. However, some critiques suggest that the book could benefit from a stronger emphasis on mathematical foundations alongside the Python coding examples. This comprehensive 336-page guide serves as both an introductory resource and a practical tool for anyone keen on exploring the world of deep learning.
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優點
- Engaging and easy to understand writing style
- Great for beginners and non-technical readers
- Practical examples and coding notebooks included
- Distills complex concepts into simpler terms
- Encourages further exploration of deep learning
缺點
- Could benefit from more mathematical explanations
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TWD 2490
立即訂購並活動它 週五, 十月 09
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特色和優勢
- Learn to build deep learning neural networks from scratch
- Teaches you the science behind neural network training
- Only uses Python and NumPy library
- Suitable for readers with high school-level math and intermediate programming skills
- Includes a free eBook with the purchase of print book
- Introduces readers to federated learning
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