Item #:
104271379
Deep Learning (Adaptive Computation and Machine Learning series)
87% of respondents would recommend this to a friend
Item #:
104271379
PYG 928695
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“Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” — Elon Musk
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Product Details
- Comprehensive introduction to deep learning
- Covers mathematical and conceptual background
- Describes deep learning techniques used in industry
- Surveys applications in natural language processing, computer vision, and more
- Offers research perspectives on theoretical topics in deep learning
- Suitable for undergraduate and graduate students, as well as software engineers
| Publisher | The MIT Press |
| Publication date | 18 Nov. 2016 |
| Language | English |
| Print length | 800 pages |
| ISBN-10 | 0262035618 |
| ISBN-13 | 978-0262035613 |
| Reading age | 18 years and up |
| Dimensions | 23.11 x 18.29 x 2.79 cm |
Product Description
Deep Learning (Adaptive Computation and Machine Learning series)
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Editorial Review
**** The book "Deep Learning," part of the Adaptive Computation and Machine Learning series, has garnered attention for its comprehensive treatment of the subject despite some criticisms regarding its mathematical rigor. The authors, recognized as leading figures in the field, offer insights that blend practical applications with the theoretical underpinnings of deep learning. Reviewers appreciate the emphasis on real-world applicability, noting that the book steers clear of excessive mathematical abstractions that often obscurate practical understanding. Instead, it focuses on actionable insights crucial for practitioners in areas like image and speech recognition. Readers have highlighted the extensive and relevant literature provided, which enhances the reader's ability to delve deeper into specific topics of interest without necessitating prior comprehension of all referenced materials. However, while the book excels in providing a robust overview and practical advice—especially beneficial for both novices and experienced practitioners—it has been noted that some sections may lack the in-depth theoretical formulations expected in a scientific text. Some customers indicate that while the book is an excellent resource for those looking to understand deep learning concepts deeply, it may not fully equip one for the complexities found in advanced academic literature. Moreover, a solid foundation in mathematics, particularly linear algebra and numerical calculus, is highly suggested for optimal comprehension. Overall, the book receives high praise for its clarity, depth, and practical orientation, making it a valuable addition for anyone keen on mastering deep learning techniques. **
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Pros
- Comprehensive and up-to-date overview of deep learning.
- Strong emphasis on practical applications over purely theoretical concepts.
- Valuable insights from leading experts in the field.
- Extensive literature for further study is included.
- Suitable for both beginners and experienced practitioners.
Cons
- Some chapters may lack solid mathematical formulation.
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Features & Benefits
- Comprehensive introduction to deep learning.
- Covers mathematical and conceptual foundations.
- Includes industry techniques like convolutional networks.
- Surveys applications in NLP, speech recognition, and more.
- Suitable for undergraduate, graduate, and software engineers.
- Supplementary materials available online.
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