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The Science of Deep Learning
Buch von Iddo Drori
Sprache: Englisch

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Beschreibung
Up-to-date guide to deep learning with unique content, rigorous math, unified notation, comprehensive algorithms, and high-quality figures.
Up-to-date guide to deep learning with unique content, rigorous math, unified notation, comprehensive algorithms, and high-quality figures.
Über den Autor
Iddo Drori is a faculty member and associate professor at Boston University, a lecturer at MIT, and adjunct associate professor at Columbia University. He was a visiting associate professor at Cornell University in operations research and information engineering, and research scientist and adjunct professor at NYU Center for Data Science, Courant Institute, and NYU Tandon. He holds a PhD in computer science and was a postdoctoral research fellow at Stanford University in statistics. He also holds an MBA in organizational behavior and entrepreneurship and has a decade of industry research and leadership experience. His main research is in machine learning, AI, and computer vision, with 70 publications and over 5,100 citations, and he has taught over 35 courses in computer science. He has won multiple competitions in computer vision conferences and received multiple best paper awards in machine learning conferences.
Inhaltsverzeichnis
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.
Details
Erscheinungsjahr: 2022
Genre: Informatik
Rubrik: Naturwissenschaften & Technik
Medium: Buch
Inhalt: Gebunden
ISBN-13: 9781108835084
ISBN-10: 1108835082
Sprache: Englisch
Einband: Gebunden
Autor: Drori, Iddo
Hersteller: Cambridge University Pr.
Abbildungen: Worked examples or Exercises
Maße: 248 x 171 x 21 mm
Von/Mit: Iddo Drori
Erscheinungsdatum: 18.08.2022
Gewicht: 0,82 kg
Artikel-ID: 121612565
Über den Autor
Iddo Drori is a faculty member and associate professor at Boston University, a lecturer at MIT, and adjunct associate professor at Columbia University. He was a visiting associate professor at Cornell University in operations research and information engineering, and research scientist and adjunct professor at NYU Center for Data Science, Courant Institute, and NYU Tandon. He holds a PhD in computer science and was a postdoctoral research fellow at Stanford University in statistics. He also holds an MBA in organizational behavior and entrepreneurship and has a decade of industry research and leadership experience. His main research is in machine learning, AI, and computer vision, with 70 publications and over 5,100 citations, and he has taught over 35 courses in computer science. He has won multiple competitions in computer vision conferences and received multiple best paper awards in machine learning conferences.
Inhaltsverzeichnis
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.
Details
Erscheinungsjahr: 2022
Genre: Informatik
Rubrik: Naturwissenschaften & Technik
Medium: Buch
Inhalt: Gebunden
ISBN-13: 9781108835084
ISBN-10: 1108835082
Sprache: Englisch
Einband: Gebunden
Autor: Drori, Iddo
Hersteller: Cambridge University Pr.
Abbildungen: Worked examples or Exercises
Maße: 248 x 171 x 21 mm
Von/Mit: Iddo Drori
Erscheinungsdatum: 18.08.2022
Gewicht: 0,82 kg
Artikel-ID: 121612565
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