Mathematical Engineering of Deep Leaing (Chapman & Hall/CRC Data Science Series)

by: Benoit Liquet (Author),Sarat Moka(Author),Yoni Nazarathy(Author)&0more

Publisher: Chapman and Hall/CRC

Edition: 1st

Publication Date: 2024/10/3

Language: English

Print Length: 402 pages

ISBN-10: 1032288280

ISBN-13: 9781032288284

Book Description

Mathematical Engineering of Deep Leaing provides a complete and concise overview of deep leaing using the language of mathematics. The book provides a self-contained background on machine leaing and optimization algorithms and progresses through the key ideas of deep leaing. These ideas and architectures include deep neural networks, convolutional models, recurrent models, long/short-term memory, the attention mechanism, transformers, variational auto-encoders, diffusion models, generative adversarial networks, reinforcement leaing, and graph neural networks. Concepts are presented using simple mathematical equations together with a concise description of relevant tricks of the trade. The content is the foundation for state-of-the-art artificial intelligence applications, involving images, sound, large language models, and other domains. The focus is on the basic mathematical description of algorithms and methods and does not require computer programming. The presentation is also agnostic to neuroscientific relationships, historical perspectives, and theoretical research. The benefit of such a concise approach is that a mathematically equipped reader can quickly grasp the essence of deep leaing.Key Features:A perfect summary of deep leaing not tied to any computer language, or computational framework.An ideal handbook of deep leaing for readers that feel comfortable with mathematical notation.An up-to-date description of the most influential deep leaing ideas that have made an impact on vision, sound, natural language understanding, and scientific domains.The exposition is not tied to the historical development of the field or to neuroscience, allowing the reader to quickly grasp the essentials.Deep leaing is easily described through the language of mathematics at a level accessible to many professionals. Readers from fields such as engineering, statistics, physics, pure mathematics, econometrics, operations research, quantitative management, quantitative biology, applied machine leaing, or applied deep leaing will quickly gain insights into the key mathematical engineering components of the field.

About the Author

Mathematical Engineering of Deep Leaing provides a complete and concise overview of deep leaing using the language of mathematics. The book provides a self-contained background on machine leaing and optimization algorithms and progresses through the key ideas of deep leaing. These ideas and architectures include deep neural networks, convolutional models, recurrent models, long/short-term memory, the attention mechanism, transformers, variational auto-encoders, diffusion models, generative adversarial networks, reinforcement leaing, and graph neural networks. Concepts are presented using simple mathematical equations together with a concise description of relevant tricks of the trade. The content is the foundation for state-of-the-art artificial intelligence applications, involving images, sound, large language models, and other domains. The focus is on the basic mathematical description of algorithms and methods and does not require computer programming. The presentation is also agnostic to neuroscientific relationships, historical perspectives, and theoretical research. The benefit of such a concise approach is that a mathematically equipped reader can quickly grasp the essence of deep leaing.Key Features:A perfect summary of deep leaing not tied to any computer language, or computational framework.An ideal handbook of deep leaing for readers that feel comfortable with mathematical notation.An up-to-date description of the most influential deep leaing ideas that have made an impact on vision, sound, natural language understanding, and scientific domains.The exposition is not tied to the historical development of the field or to neuroscience, allowing the reader to quickly grasp the essentials.Deep leaing is easily described through the language of mathematics at a level accessible to many professionals. Readers from fields such as engineering, statistics, physics, pure mathematics, econometrics, operations research, quantitative management, quantitative biology, applied machine leaing, or applied deep leaing will quickly gain insights into the key mathematical engineering components of the field.

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