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Die Wissenschaft des Deep Learning - Drori, Iddo-
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Standort: Simi Valley, California, USA
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eBay-Artikelnr.:388413171825
Artikelmerkmale
- Artikelzustand
- Book Title
- The Science of Deep Learning
- ISBN
- 9781108835084
Über dieses Produkt
Product Identifiers
Publisher
Cambridge University Press
ISBN-10
1108835082
ISBN-13
9781108835084
eBay Product ID (ePID)
26057267301
Product Key Features
Number of Pages
450 Pages
Publication Name
Science of Deep Learning
Language
English
Subject
General, Computer Vision & Pattern Recognition
Publication Year
2022
Type
Textbook
Subject Area
Computers, Science
Format
Hardcover
Dimensions
Item Height
0.8 in
Item Length
9.8 in
Item Width
6.9 in
Additional Product Features
Intended Audience
College Audience
Reviews
'Drori's textbook goes under the hood of deep learning, covering a broad swath of modern techniques in optimization that are useful for efficiently training neural networks. The book also covers regularization methods to avoid overfitting, a common issue when working with deep learning models. Overall, this is an excellent textbook for students and practitioners who want to gain a deeper understanding of deep learning.' Madeleine Udell, Stanford University, 'This textbook provides an excellent introduction to contemporary methods and models in deep learning. I expect this book to become a key resource in data science education for students and researchers.' Nakul Verma, Columbia University, 'This book offers a fascinating tour of the field of deep learning, which in only ten years has come to revolutionize almost every area of computing. Drori provides concise descriptions of many of the most important developments, combining unified mathematical notation and ample figures to form an essential resource for students and practitioners alike.' Jonathan Ventura, Cal Poly, 'Drori's textbook makes the learning curve for deep learning a whole lot easier to climb. It follows a rigid scientific narrative, accompanied by a trove of code examples and visualizations. These enable a truly multi-modal approach to learning that will allow many students to understand the material better and sets them on a path of exploration.' Joaquin Vanschoren, Assistant Professor of Machine Learning, Eindhoven University of Technology, 'This book covers an impressive breadth of foundational concepts and algorithms behind modern deep learning. By reading this book, readers will quickly but thoroughly learn and appreciate foundations and advances of modern deep learning.' Kyunghyun Cho, New York University, 'Drori's book covers deep learning, from fundamentals to applications. The fundamentals are covered with clear figures and examples, making the underlying algorithms easy to understand for non-specialists. The multidisciplinary applications are thoughtfully selected to illustrate the broad applications of deep neural networks to specialized domains while highlighting the common themes and architectures between them.' Tonio Buonassisi, Professor of Mechanical Engineering, Massachusetts Institute of Technology, 'This new book by Professor Drori brings fresh insights from his experience teaching thousands of students at Columbia, MIT, and NYU during the past several years. The book is a unique resource and opportunity for educators and researchers worldwide to build on his highly successful deep learning course.' Claudio Silva, New York University, 'In the avalanche of books on Deep Learning, this one stands out. Iddo Drori has mastered reinforcement learning - in its technical meaning and in his successful, commonsense approach to teaching and understanding.' Gilbert Strang, Massachusetts Institute of Technology
TitleLeading
The
Dewey Edition
23
Illustrated
Yes
Dewey Decimal
006.31
Table Of Content
Preface; Notation; Part I. Foundations: 1. Introduction; 2. Forward and backpropagation; 3. Optimization; 4. Regularization; Part II. Architectures: 5. Convolutional neural networks; 6. Sequence models; 7. Graph neural networks; 8. Transformers; Part III. Generative Models: 9. Generative adversarial networks; 10. Variational autoencoders; Part IV. Reinforcement Learning: 11. Reinforcement learning; 12. Deep reinforcement learning; Part V. Applications: 13. Applications; Appendices; References; Index.
Synopsis
This up-to-date guide to deep learning is intended for students in computer science, electrical engineering, data science, statistics, and OR as well as researchers in academia and industry. State-of-the-art topics include transformers, GNNs, VAEs, and deep RL. Complementary code and hundreds of exercises with solutions are provided on the website., The Science of Deep Learning emerged from courses taught by the author that have provided thousands of students with training and experience for their academic studies, and prepared them for careers in deep learning, machine learning, and artificial intelligence in top companies in industry and academia. The book begins by covering the foundations of deep learning, followed by key deep learning architectures. Subsequent parts on generative models and reinforcement learning may be used as part of a deep learning course or as part of a course on each topic. The book includes state-of-the-art topics such as Transformers, graph neural networks, variational autoencoders, and deep reinforcement learning, with a broad range of applications. The appendices provide equations for computing gradients in backpropagation and optimization, and best practices in scientific writing and reviewing. The text presents an up-to-date guide to the field built upon clear visualizations using a unified notation and equations, lowering the barrier to entry for the reader. The accompanying website provides complementary code and hundreds of exercises with solutions.
LC Classification Number
Q325.73
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CRN: 202013333
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