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Item #:
170400393
Understanding Deep Learning
85% of respondents would recommend this to a friend
Item #:
170400393
€ 67
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An authoritative, accessible, and up-to-date treatment of deep learning that strikes a pragmatic middle ground between theory and practice.
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What Stands Out
Comprehensive Coverage
Covers fundamental and advanced concepts in deep learning, providing a thorough understanding tailored for both beginners and experienced practitioners.
Practical Examples
Includes numerous real-world applications and case studies, making theoretical concepts relatable and easier to grasp for readers in various fields.
Accessible Format
Available in Kindle edition for convenient access on multiple devices, allowing learners to study deep learning anytime and anywhere.
Product Details
- An authoritative, accessible, and up-to-date treatment of deep learning that strikes a pragmatic middle ground between theory and practice.Deep learning is a fast-moving field with sweeping relevance in today’s increasingly digital world. Understanding Deep Learning provides an authoritative, accessible, and up-to-date treatment of the subject, covering all the key topics along with recent advances and cutting-edge concepts. Many deep learning texts are crowded with technical details that obscure fundamentals, but Simon Prince ruthlessly curates only the most important ideas to provide a high density of critical information in an intuitive and digestible form. From machine learning basics to advanced models, each concept is presented in lay terms and then detailed precisely in mathematical form and illustrated visually. The result is a lucid, self-contained textbook suitable for anyone with a basic background in applied mathematics.Up-to-date treatment of deep learning covers cutting-edge topics not found in existing texts, such as transformers and diffusion modelsShort, focused chapters progress in complexity, easing students into difficult concepts Pragmatic approach straddling theory and practice gives readers the level of detail required to implement naive versions of modelsStreamlined presentation separates critical ideas from background context and extraneous detailMinimal mathematical prerequisites, extensive illustrations, and practice problems make challenging material widely accessible Programming exercises offered in accompanying Python Notebooks
| Publisher | The MIT Press |
| Publication date | December 5, 2023 |
| Language | English |
| File size | 42.8 MB |
| Enhanced typesetting | Enabled |
| X-Ray | Not Enabled |
| Word Wise | Not Enabled |
| Print length | 986 pages |
| ISBN-13 | 978-0262377102 |
| Page Flip | Enabled |
| Item Weight | 1.5 lbs (680 grams) |
Who Should Buy?
-
Aspiring Data Scientists
Perfect for individuals seeking foundational knowledge in deep learning and its practical applications in data science.
-
AI Researchers
Offers advanced insights and methodologies valuable for researchers looking to deepen their understanding of deep learning techniques.
-
Students in Technology
Ideal for students pursuing courses in AI, machine learning, or computer science, providing comprehensive learning materials.
-
Complete Beginners
May not cater well to those without any background in programming or machine learning concepts.
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AI & Semantics Editorial Review
Understanding Deep Learning is an essential resource published by The MIT Press, featuring a comprehensive 544 pages of deep learning concepts. The book balances theoretical foundations with practical applications, making it suitable for both beginners and experienced practitioners. Readers appreciate the clear exposition, well-structured chapters, and helpful diagrams that facilitate understanding of advanced topics like transformers and GANs. Many users highlight how the book's exercises effectively solidify learning, making it an invaluable tool for anyone serious about mastering deep learning.
Customer Reviews & Ratings
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Pros
- Clear exposition of complex deep learning concepts
- Excellent balance between theory and practice
- Includes exercises for better comprehension
- Well-structured chapters with great diagrams
- Approachable for both beginners and experienced readers
Cons
- Some advanced topics may not be as strong
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€ 67
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Features & Benefits
- Provides authoritative treatment of deep learning
- Strikes a pragmatic middle ground between theory and practice
- Covers cutting-edge topics not found in existing texts
- Straddles theory and practice, giving the level of detail required to implement models
- Separates critical ideas from background context and extraneous detail
- Minimal mathematical prerequisites, extensive illustrations, and practice problems
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