Deep Learning: Foundations and Concepts
Product ID: 599525501
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This book offers a comprehensive introduction to the central ideas that underpin deep learning. It is intended both for newcomers to machine learning and for those already experienced in the field. Covering key concepts relating to contemporary architectures and techniques, this essential book equips readers with a robust foundation for potential future specialization. The field of deep learning is undergoing rapid evolution, and therefore this book focusses on ideas that are likely to endure the test of time. The book is organized into numerous bite-sized chapters, each exploring a distinct topic, and the narrative follows a linear progression, with each chapter building upon content from its predecessors. This structure is well-suited to teaching a two-semester undergraduate or postgraduate machine learning course, while remaining equally relevant to those engaged in active research or in self-study. A full understanding of machine learning requires some mathematical background and so the book includes a self-contained introduction to probability theory. However, the focus of the book is on conveying a clear understanding of ideas, with emphasis on the real-world practical value of techniques rather than on abstract theory. Complex concepts are therefore presented from multiple complementary perspectives including textual descriptions, diagrams, mathematical formulae, and pseudo-code. Chris Bishop is a Technical Fellow at Microsoft and is the Director of Microsoft Research AI4Science. He is a Fellow of Darwin College Cambridge, a Fellow of the Royal Academy of Engineering, and a Fellow of the Royal Society. Hugh Bishop is an Applied Scientist at Wayve, a deep learning autonomous driving company in London, where he designs and trains deep neural networks. He completed his MPhil in Machine Learning and Machine Intelligence at Cambridge University. “Chris Bishop wrote a terrific textbook on neural networks in 1995 and has a deep knowledge of the field and its core ideas. His many years of experience in explaining neural networks have made him extremely skillful at presenting complicated ideas in the simplest possible way and it is a delight to see these skills applied to the revolutionary new developments in the field.” -- Geoffrey Hinton " With the recent explosion of deep learning and AI as a research topic, and the quickly growing importance of AI applications, a modern textbook on the topic was badly needed. The "New Bishop" masterfully fills the gap, covering algorithms for supervised and unsupervised learning, modern deep learning architecture families, as well as how to apply all of this to various application areas." – Yann LeCun “This excellent and very educational book will bring the reader up to date with the main concepts and advances in deep learning with a solid anchoring in probability. Theseconcepts are powering current industrial AI systems and are likely to form the basis of further advances towards artificial general intelligence.” -- Yoshua Bengio Review: The best book on Deep Learning out there - I'm feeling lucky. This is incredibly, unspeakably, impossibly good book. I've seen the book at the CVPR conference, and hesitated a bit before buying it. $90 after all. But I can't be happier with my purchase. It's simply the best book on Deep Learning - and on Machine Learning - I've seen in 20 years. It gives a very accessible and intuitive introduction in many topics - and, at the same time, preserves the mathematical rigor. For example, the chapter on transformers is the very, very best description of a topic I've seen so far. The book also contains all the info on the ML basics - from multivariate distributions to EM algorithm. Again, it is written very candidly - and, at the same time, preserves theoretical foundations. It is probably slightly less "mathy" than the "Pattern Recognition and Machine Learning" book by Bishop - which is a previous iteration of this text. But much, much more accessible as well. It explains "narrow" topics in plain English. You may read it in this textbook, and then go back to "Pattern Recognition" if you want to get a slightly deeper math for the same topic. Also the book is very recent and covers the hottest and most recent topics, like Diffusion Models. Saved me lots, lots of time and effort - instead of digging through a heap of research papers I can nos just read a chapter from this book. Many thanks to Chris Bishop for writing it! Review: Lots of topics, not very hands-on - Deep Learning: Foundations and Concepts by Christopher M. Bishop and Hugh Bishop is a comprehensive and accessible introduction to the world of deep learning. The book effectively balances theoretical depth with practical insights, making it suitable for both beginners and experienced practitioners. Key strengths of the book include: Clear and concise explanations: The authors do an excellent job of breaking down complex concepts into easily understandable terms, making the material accessible to a wide range of readers. Strong mathematical foundation: The book provides a solid mathematical foundation for understanding deep learning algorithms, but it avoids excessive mathematical formalism, making it engaging for readers with varying levels of mathematical background. Practical applications: The book covers a wide range of real-world applications, such as computer vision, natural language processing, and speech recognition, providing practical examples to illustrate the concepts. Up-to-date coverage: The book covers the latest advancements in deep learning, including attention mechanisms, transformer models, and generative adversarial networks. Potential areas for improvement: More hands-on exercises: While the book provides theoretical explanations, it could benefit from more practical exercises and coding examples to reinforce learning. Deeper dives into specific topics: For readers who want to delve deeper into specific topics, such as reinforcement learning or unsupervised learning, additional resources or references could be helpful. Overall, Deep Learning: Foundations and Concepts is an excellent resource for anyone interested in learning about deep learning. It provides a clear and comprehensive introduction to the field, making it a valuable addition to any machine learning enthusiast's library.
| Best Sellers Rank | #44,325 in Books ( See Top 100 in Books ) #9 in Database Storage & Design #16 in Statistics (Books) #30 in Artificial Intelligence (Books) |
| Customer Reviews | 4.7 out of 5 stars 269 Reviews |
R**R
The best book on Deep Learning out there
I'm feeling lucky. This is incredibly, unspeakably, impossibly good book. I've seen the book at the CVPR conference, and hesitated a bit before buying it. $90 after all. But I can't be happier with my purchase. It's simply the best book on Deep Learning - and on Machine Learning - I've seen in 20 years. It gives a very accessible and intuitive introduction in many topics - and, at the same time, preserves the mathematical rigor. For example, the chapter on transformers is the very, very best description of a topic I've seen so far. The book also contains all the info on the ML basics - from multivariate distributions to EM algorithm. Again, it is written very candidly - and, at the same time, preserves theoretical foundations. It is probably slightly less "mathy" than the "Pattern Recognition and Machine Learning" book by Bishop - which is a previous iteration of this text. But much, much more accessible as well. It explains "narrow" topics in plain English. You may read it in this textbook, and then go back to "Pattern Recognition" if you want to get a slightly deeper math for the same topic. Also the book is very recent and covers the hottest and most recent topics, like Diffusion Models. Saved me lots, lots of time and effort - instead of digging through a heap of research papers I can nos just read a chapter from this book. Many thanks to Chris Bishop for writing it!
S**.
Lots of topics, not very hands-on
Deep Learning: Foundations and Concepts by Christopher M. Bishop and Hugh Bishop is a comprehensive and accessible introduction to the world of deep learning. The book effectively balances theoretical depth with practical insights, making it suitable for both beginners and experienced practitioners. Key strengths of the book include: Clear and concise explanations: The authors do an excellent job of breaking down complex concepts into easily understandable terms, making the material accessible to a wide range of readers. Strong mathematical foundation: The book provides a solid mathematical foundation for understanding deep learning algorithms, but it avoids excessive mathematical formalism, making it engaging for readers with varying levels of mathematical background. Practical applications: The book covers a wide range of real-world applications, such as computer vision, natural language processing, and speech recognition, providing practical examples to illustrate the concepts. Up-to-date coverage: The book covers the latest advancements in deep learning, including attention mechanisms, transformer models, and generative adversarial networks. Potential areas for improvement: More hands-on exercises: While the book provides theoretical explanations, it could benefit from more practical exercises and coding examples to reinforce learning. Deeper dives into specific topics: For readers who want to delve deeper into specific topics, such as reinforcement learning or unsupervised learning, additional resources or references could be helpful. Overall, Deep Learning: Foundations and Concepts is an excellent resource for anyone interested in learning about deep learning. It provides a clear and comprehensive introduction to the field, making it a valuable addition to any machine learning enthusiast's library.
J**N
Stunning and well organized, serious introduction to the field
Great introduction to the realm of ML and very very well organized survey of fundamentals. I really do appreciate how perfectly this book is written and effort put into wording. Everything fits properly and is systematically built like back in my university courses. Exactly what I was looking for: textbook and reference at the same time. Not overhelmingly rigorous and definitely not too vague and sloppy. Affordable price tag is also a huge bonus. Highly recomended!
R**N
Review
Great book!
C**U
Mathematically rigorous Intuitive-based Deep Learning (More for researchers and theorists)
This is quite mathematically rigorous so make sure you're comfortable with your vector calculus and derivations. Otherwise, this is a phenomenal book as it combines it with the mathematical rigor with amazing diagrams, plots and images that demonstrate the intuition behind these common deep learning topics (which is quite rare). For example, he showed a derivation of RMSProp and it's close intuition from physics principles. If you're into research or theory and want to contribute to the field, this is a wonderful entry. If you're in industry and just implementing models that are based on pre-existing theory, you may want to try a more hands-on book.
P**S
This books is not about Deep Learning
I bought this books to acquaint myself on deep learning, but the 2 chapters "Convolutional networks" and "Transformers" are written badly and did not get anything out of this other than a general architecture. How the information is passed and used from one layer to the other is completely missed. Besides those 2 chapters on deep learning, the rest of the book is on topics related to machine learning is easy to follow (if you have some decent background on matrix analysis and statistics).
C**Q
Excellent AI Book
An excellent book on neural networks, deep learning and their applications to AI and computer vision. Extremely well organized with well-connected chapters which makes it easy to follow and get the BIG picture, while diving deep into the specifics using elaborate math language and yet not lacking the intuitive approach. The book can be used either as a one stop-reference for subject matter experts, or simply turned into a self-learning or university level textbook. This book also enjoyed the exceptional and unique style of Prof. Christopher M Bishop , who has been a researcher, a public speaker, and a distinguished leader in the field of NN, DL, ML and AI for decades. Dr. Cazhaow Qazaz VP of Advanced Analytics and AI Knowledge Square LLC
J**.
Clearly written and comprehensive
I was looking for a recently written book on this subject that provides a reasonably complete and digestible presentation. I also wanted to find a book that's supportive for self-learners and I applaud the authors' plan to publish solutions to all the chapter problems. (At the time of this review the first 10 chapter solutions have been placed on the book's website.) From what I've read thus far, this book will be a good reference.
A**N
Great book!
Great book! Book is available online to read, but it makes it difficult to study and learn from. Arrived on time in pristine condition.
R**Y
Building blocks - Solid Recommendation
If you liked Pattern Recognition and Machine Learning for the same author, Deep Learning brings a fresh perspective of foundational theory behind Deep Learning. Not for beginners since contains hard math (tip: begin with Why Machines Learn by Anil Ananthaswamy). Great explanations on Gradient, Backpropagation and CNN. Superb Springer hardcover edition, hq paper, full colors and comfortly diagrammed
I**N
If you only choose one deep learning book, this is it.
This book continues the Bishops' tradition of writing accessible and clear yet rigorous treatment of machine learning/deep learning content. The transformers and diffusion chapters fantastic, and exactly what I was looking for. The authors share solutions to end of chapter questions which is perfect for students and self- learners alike. You can scan the book on their site, but this one is a *must have* for your bookshelf. If you need any more reason to purchase this book immediately, LeCun, Bengio and Hinton all wrote highly supportive reviews (see site).
H**N
iyi bir textbook
Goodfellow'un 2016 basimli DL kitabi uzerine alanda cok fazla gelisme oldu. DL alaninda teori-matematik bilgileri tazelemek-guncellemek icin iyi bir textbook.
S**.
Really bad print and book quality
Really poor printing quality and book assembly. Some pages in one book look faded and out-of-focus. There is a page that has a damaged corner due to the printing. The spine looks bad, and the cover has dust on it. Is this really a Springer printed book or was I scammed?
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