Geometry of Deep Learning
A Signal Processing Perspective
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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, GPT3, 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 highdimensional 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 distanceminimization problems.
Because this book contains uptodate 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.
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Utgitt:
2022
Forlag: Springer Verlag, Singapore
Innbinding: Innbundet
Språk: Engelsk
Sider: 330
ISBN: 9789811660450
Format: 24 x 16 cm
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«“This book is based on material that has been prepared for seniorlevel undergraduate classes, this book can be used for onesemester seniorlevel undergraduate and graduatelevel classes.” (Arzu Ahmadova, zbMATH 1493.68003, 2022)»