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This important work describes recent theoretical advances in the study of artificial neural networks. It explores probabilistic models of supervised learning problems, and addresses the key statistical and computational questions. Chapters survey research on pattern classification with binaryoutput networks, including a discussion of the relevance of the Vapnik Chervonenkis dimension, and of estimates of the dimension for several neural network models. In addition, Anthony and Bartlett develop a model of classification by realoutput networks, and demonstrate the usefulness of classification with a large margin. The authors explain the role of scalesensitive versions of the Vapnik Chervonenkis dimension in large margin classification, and in real prediction. Key chapters also discuss the computational complexity of neural network learning, describing a variety of hardness results, and outlining two efficient, constructive learning algorithms. The book is selfcontained and accessible to researchers and graduate students in computer science, engineering, and mathematics.
⚠️ WARNING (California Proposition 65):
This product may contain chemicals known to the State of California to cause cancer,
birth defects, or other reproductive harm.
This book really breaks down the theoretical foundations of neural networks in a way that’s approachable. I’m a grad student, and I found the explanations clear and helpful for my research. Highly recommend if you're into AI!
F
Fatima Al-Amin
Excellent for theoretical insights
This book is a goldmine for anyone serious about neural networks. The depth of theoretical insights is impressive, and the authors do a great job of connecting ideas. This is a must-have for advanced learners!
E
Emily Tran
Good but a bit dense
I appreciate the content, but I felt some sections were quite technical and hard to digest. As someone who's new to machine learning, I struggled a bit. Still, it’s a valuable resource if you’re committed to understanding the theory behind neural networks.
J
John Smithson
Came damaged, but good content
The book arrived with a bent corner, which was disappointing, but I still decided to read it. The content is great for understanding the foundations of neural networks, but I expected better packaging from the seller.
M
Marcus Lefevre
Needed more examples
While the theoretical aspects are well covered, I wish there were more practical examples. It’s a bit lacking for someone looking to apply the concepts in real-world scenarios. But for pure theory, it’s solid.
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⚠️ California Proposition 65 Warning: Some products sold on this website may expose you to chemicals known to the State of California to cause cancer, birth defects, or other reproductive harm. For more information, visit www.P65Warnings.ca.gov.
