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To support this claim, an overview of classical kernel machine learning approaches is presented, and their advantages and limitations are explained. Following a detailed explanation of the basic building blocks of deep neural networks from a biological and algorithmic point of view, the latest tools such as attention, normalization, Transformer, BERT, GPT-3, and others are described. Here, too, the focus is on the fact that in these heuristic approaches, there is an important, beautiful geometric structure behind the intuition that enables a systematic understanding. A unified geometric analysis to understand the working mechanism of deep learning from high-dimensional geometry is offered. Then, different forms of generative models like GAN, VAE, normalizing flows, optimal transport, and so on are described from a unified geometric perspective, showing that they actually come from statistical distance-minimization problems.
Because this book contains up-to-date information from both a practical and theoretical point of view, it can be used as an advanced deep learning textbook in universities or as a reference source for researchers interested in acquiring the latest deep learning algorithms and their underlying principles. In addition, the book has been prepared for a codeshare course for both engineering and mathematics students, thus much of the content is interdisciplinary and will appeal to students from both disciplines.
To support this claim, an overview of classical kernel machine learning approaches is presented, and their advantages and limitations are explained. Following a detailed explanation of the basic building blocks of deep neural networks from a biological and algorithmic point of view, the latest tools such as attention, normalization, Transformer, BERT, GPT-3, and others are described. Here, too, the focus is on the fact that in these heuristic approaches, there is an important, beautiful geometric structure behind the intuition that enables a systematic understanding. A unified geometric analysis to understand the working mechanism of deep learning from high-dimensional geometry is offered. Then, different forms of generative models like GAN, VAE, normalizing flows, optimal transport, and so on are described from a unified geometric perspective, showing that they actually come from statistical distance-minimization problems.
Because this book contains up-to-date information from both a practical and theoretical point of view, it can be used as an advanced deep learning textbook in universities or as a reference source for researchers interested in acquiring the latest deep learning algorithms and their underlying principles. In addition, the book has been prepared for a codeshare course for both engineering and mathematics students, thus much of the content is interdisciplinary and will appeal to students from both disciplines.
Covers recent developments in deep learning and a wide spectrum of issues, with exercise problems for students
Employs unified mathematical approaches with illustrative graphics to present various techniques and their results
Closes the gap between the purely mathematical and implementation-oriented treatments of deep learning
| Erscheinungsjahr: | 2022 |
|---|---|
| Fachbereich: | Analysis |
| Genre: | Mathematik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Buch |
| Seiten: | 348 |
| Reihe: | Mathematics in Industry |
| Inhalt: |
xvi
330 S. 1 s/w Illustr. 330 p. 1 illus. |
| ISBN-13: | 9789811660450 |
| ISBN-10: | 981166045X |
| Sprache: | Englisch |
| Ausstattung / Beilage: | HC runder Rücken kaschiert |
| Einband: | Gebunden |
| Autor: | Ye, Jong Chul |
| Auflage: | 1st ed. 2022 |
| Hersteller: |
Springer Singapore
Springer Nature Singapore Mathematics in Industry |
| Maße: | 241 x 160 x 25 mm |
| Von/Mit: | Jong Chul Ye |
| Erscheinungsdatum: | 06.01.2022 |
| Gewicht: | 0,688 kg |
Covers recent developments in deep learning and a wide spectrum of issues, with exercise problems for students
Employs unified mathematical approaches with illustrative graphics to present various techniques and their results
Closes the gap between the purely mathematical and implementation-oriented treatments of deep learning
| Erscheinungsjahr: | 2022 |
|---|---|
| Fachbereich: | Analysis |
| Genre: | Mathematik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Buch |
| Seiten: | 348 |
| Reihe: | Mathematics in Industry |
| Inhalt: |
xvi
330 S. 1 s/w Illustr. 330 p. 1 illus. |
| ISBN-13: | 9789811660450 |
| ISBN-10: | 981166045X |
| Sprache: | Englisch |
| Ausstattung / Beilage: | HC runder Rücken kaschiert |
| Einband: | Gebunden |
| Autor: | Ye, Jong Chul |
| Auflage: | 1st ed. 2022 |
| Hersteller: |
Springer Singapore
Springer Nature Singapore Mathematics in Industry |
| Maße: | 241 x 160 x 25 mm |
| Von/Mit: | Jong Chul Ye |
| Erscheinungsdatum: | 06.01.2022 |
| Gewicht: | 0,688 kg |
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