《Deep Learning : Adaptive Computation and Machine Learning series》由美國計算機科學家Ian Goodfellow、Yoshua Bengio（約書亞·本吉奧）和Aaron Courville合著，The MIT Press於2016年出版，屬深度學習領域奠基性教材，隸屬Adaptive Computation and Machine Learning叢書。作者Ian Goodfellow因提出生成對抗網路（GAN）而知名，曾任職於谷歌大腦和蘋果公司。
該書分為三大部分：第1部分（第2-5章）系統闡述線性代數、機率論、資訊理論等數學基礎及機器學習基本概念；第2部分（第6-12章）解析深度前饋網路、正則化、最佳化算法、卷積網路及序列建模等技術方法；第3部分（第13-20章）探討線性因子模型、自編碼器、深度生成模型等前沿研究方向。內容覆蓋自然語言處理、語音識別、計算機視覺等套用場景，配套網站提供教學補充材料。該書適用於計算機科學專業本科生、研究生及需套用深度學習技術的軟體工程師。
基本介紹
- 外文名：Deep Learning : Adaptive Computation and Machine Learning series
- 作者： Ian Goodfellow、 Yoshua Bengio、Aaron Courville、約書亞·本吉奧
- 出版時間：2016年11月11日
- 出版社：The MIT Press
- 頁數：800 頁
- ISBN：9780262035613
- 裝幀：Hardcover
- 叢書：Adaptive Computation and Machine Learning
內容簡介
"Written by three experts in the field, Deep Learning is the only comprehensive book on the subject." -- Elon Musk, co-chair of OpenAI; co-founder and CEO of Tesla and SpaceX
Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning.
The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models.
Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.
