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Books > Science & Mathematics > Mathematics > Calculus & mathematical analysis > Functional analysis
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Geometry of Deep Learning - A Signal Processing Perspective (Hardcover, 1st ed. 2022)
Loot Price: R1,130
Discovery Miles 11 300
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Geometry of Deep Learning - A Signal Processing Perspective (Hardcover, 1st ed. 2022)
Series: Mathematics in Industry, 37
Expected to ship within 10 - 15 working days
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The focus of this book is on providing students with insights into
geometry that can help them understand deep learning from a unified
perspective. Rather than describing deep learning as an
implementation technique, as is usually the case in many existing
deep learning books, here, deep learning is explained as an
ultimate form of signal processing techniques that can be imagined.
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.
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