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Deep Learning in Science by Pierre Baldi (2021, Hardcover)

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Deep Learning in Science, Hardcover by Baldi, Pierre, ISBN 1108845355, ISBN-13 9781108845359, Brand New, Free shipping in the US

About this product

Product Identifiers

PublisherCambridge University Press

ISBN-101108845355

ISBN-139781108845359

eBay Product ID (ePID)17050080357

Product Key Features

Number of Pages450 Pages

Publication NameDeep Learning in Science

LanguageEnglish

SubjectGeneral, Computer Vision & Pattern Recognition

Publication Year2021

TypeTextbook

Subject AreaComputers, Science

AuthorPierre Baldi

FormatHardcover

Dimensions

Item Height0.9 in

Item Weight32.6 Oz

Item Length9.9 in

Item Width6.8 in

Additional Product Features

Intended AudienceScholarly & Professional

Dewey Edition23

Reviews'A splendid and timely contribution to the oeuvre in a rapidly burgeoning field. The text takes the reader on a brisk tour of deep learning from its origins in neuroscience to the current state of the art and future directions, all buttressed with a palette of rich applications in the natural sciences. A noteworthy feature of the exposition is the focus on not only the mechanisms themselves but on the explication of the guiding principles behind them. This text will support readers of various persuasions, from students who wish to absorb the basic principles informing the current approaches to deep learning, to practitioners in the natural sciences who wish to explore what deep learning has to offer in a panoply of complex problems.' Santosh S. Venkatesh, Professor of Electrical and Systems Engineering, University of Pennsylvania, 'This comprehensive work from a leader in the field undoubtedly belongs in the library of every institution that teaches courses touching on deep learning. Highly recommended.' R. Bharath, Choice Connect

IllustratedYes

Dewey Decimal006.31

Table Of Content1. Introduction; 2. Basic Concepts; 3. Shallow Networks and Shallow Learning; 4. Two-Layer Networks and Universal Approximation; 5. Autoencoders; 6. Deep Networks and Backpropagation; 7. The Local Learning Principle; 8. The Deep Learning Channel; 9. Recurrent Networks; 10. Recursive Networks; 11. Applications in Physics; 12. Applications in Chemistry; 13. Applications in Biology and Medicine; 14. Conclusion; Appendix A. Reinforcement Learning and Deep Reinforcement Learning; Appendix B. Hints and Remarks for Selected Exercises; References; Index.

SynopsisThis is the first rigorous, self-contained treatment of the theory of deep learning. Starting with the foundations of the theory and building it up, this is essential reading for any scientists, instructors, and students interested in artificial intelligence and deep learning. It provides guidance on how to think about scientific questions, and leads readers through the history of the field and its fundamental connections to neuroscience. The author discusses many applications to beautiful problems in the natural sciences, in physics, chemistry, and biomedicine. Examples include the search for exotic particles and dark matter in experimental physics, the prediction of molecular properties and reaction outcomes in chemistry, and the prediction of protein structures and the diagnostic analysis of biomedical images in the natural sciences. The text is accompanied by a full set of exercises at different difficulty levels and encourages out-of-the-box thinking., This is the first rigorous, self-contained treatment of the theory of deep learning. Aimed at scientists, instructors, and students interested in artificial intelligence and deep learning, it demonstrates many applications in physics, chemistry, and biomedicine. It includes a full set of exercises and encourages out-of-the-box thinking.

LC Classification NumberQ325.5