Deep Learning

Ian Goodfellow is Research Scientist at OpenAI. Yoshua Bengio is Professor of Computer Science at the Université de Montréal. Aaron Courville is Assistant Professor of Computer Science at the Université de Montréal.

出版者:The MIT Press

作者:Ian Goodfellow

页数:800

出版时间:2016-11-11

价格:USD 72.00

装帧:Hardcover

isbn号码:9780262035613

丛书系列:Adaptive Computation and Machine Learning

图书标签:深度学习机器学习DeepLearning人工智能AIMachineLearning计算机计算机科学

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"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.

读后感

完整读了原版第一、二部分和翻译版的五到七章，始终觉得翻译版少了点什么东西。不否认译者团队的专业，也不否认译者团队的用心，但还是推荐阅读英文版。 仔细想了一下，这本书的特点不在于简练精确的罗列知识，而在于作者用凝神严谨的语言将自己对各个知识点深刻独到的见解表达...  

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翻译的人翻译完有自己读过么，什么可以可以。每看一句还要先想他在说啥，很难受，已扔垃圾桶。 ————————————————————————————————————————————————————————————————————————————————————...  

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终于磕磕绊绊读完了，是我读的最纠结的书，总结一下感受。 第一个是书里面的推导真心不知道是给谁看的，有的时候很简单的步骤写上去然后跳跃几个比较难的步骤，基本没法跟下去。 第二个是逻辑不太通顺，这可能和翻译有关系，再就是缺乏必要的背景介绍，内容之间的连接比较少。...  

用户评价

读的是开源中文版，https://github.com/exacity/deeplearningbook-chinese

非常好的教材，可以结合GitHub上的中文译版看。

这书不错，前面快200页基础，没有统计和机器学习背景也可以看。我从后来开始看，觉得很不错，可以入门做阿尔法狗了~~~入门

读的中文版：https://github.com/exacity/deeplearningbook-chinese 第三部分还没读下去，深觉数学不够 含金量台高，7,8,11三章真是调参的人森经验了

读完第一部分和最后部分无监督学习的章节。读了一年终于读完了????