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

"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

1、推荐了很多书籍，关乎如何提升学习力 2、其中重大的方法就是远离社交网络，对此方法如下：1.完全脱离网络2.一周或一月设置几天或几周深度学习；不接触网络3.一天之中，设计可使用网络的时间4.一天置之中规划每一分钟 3、深入学习可以提升生产力：在一段时间内全然投入到一件...  

标准的国内高校出书，拉几个学生翻译，自己改一改就出版了。这个翻译真的是直译，比机翻好一些，有的语序都是英文原版的，看的非常费劲。内容方面倒是还行，相对来说比较容易入门。更推荐机械工业出版社的《神经网络与机器学习》这本书，在数学和公式推导方面更清楚，讲的也比...  

标准的国内高校出书，拉几个学生翻译，自己改一改就出版了。这个翻译真的是直译，比机翻好一些，有的语序都是英文原版的，看的非常费劲。内容方面倒是还行，相对来说比较容易入门。更推荐机械工业出版社的《神经网络与机器学习》这本书，在数学和公式推导方面更清楚，讲的也比...  

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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用户评价

他们起草的时候指出他们一些公式错误，所以上面有我的名字，哈哈

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

非常好的一本书，每个从业者都该看看

他们起草的时候指出他们一些公式错误，所以上面有我的名字，哈哈

读了前九章，内容虽然非常好，但是作者对内容的表达远不如prml清晰，很多地方跳跃性太强，需要猜测他的意图或者查阅其他资料才能搞明白他要表达什么。prml在数学和表达上的严谨度比他好的多。