蓝图
计算机科学
人工智能
形式
深度学习
认知科学
直觉
相关性(法律)
集合(抽象数据类型)
学习理论
认识论
数学教育
数学
心理学
工程类
哲学
机械工程
语言学
程序设计语言
法学
政治学
作者
Daniel A. Roberts,Sho Yaida,Boris Hanin
出处
期刊:arXiv: Learning
日期:2022-05-05
被引量:148
标识
DOI:10.1017/9781009023405
摘要
This textbook establishes a theoretical framework for understanding deep learning models of practical relevance. With an approach that borrows from theoretical physics, Roberts and Yaida provide clear and pedagogical explanations of how realistic deep neural networks actually work. To make results from the theoretical forefront accessible, the authors eschew the subject's traditional emphasis on intimidating formality without sacrificing accuracy. Straightforward and approachable, this volume balances detailed first-principle derivations of novel results with insight and intuition for theorists and practitioners alike. This self-contained textbook is ideal for students and researchers interested in artificial intelligence with minimal prerequisites of linear algebra, calculus, and informal probability theory, and it can easily fill a semester-long course on deep learning theory. For the first time, the exciting practical advances in modern artificial intelligence capabilities can be matched with a set of effective principles, providing a timeless blueprint for theoretical research in deep learning.
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