Deep Learning

Deep Learning pdf epub mobi txt 电子书 下载 2025

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
出品人:
页数:800
译者:
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 
  • AI 
  • MachineLearning 
  • 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.

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这本书写的是比较有深度的,堪称深度学习的圣经。只是中文版翻译的比较一般,part1和part2尚且可以一读,至于part3,不知道译者自己有没有理解原文内容,像是逐词直译,非常拗口。part3有时间的话拿英文版的出来看一看。 其中第一部分的数学和机器学习可以用来复习忘记的基础知...  

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和PRML比较起来,明显感觉数学公式少了很多,作者也有提到深度学习很多部分没有很好的数学支持,所以大段文字描述很容易让人思路跟丢了。同时,很多主题也能意识到是很大的独立主题,肯定只能带过式的讲解,但是又没有浅的引入部分,好像直接就比较深入,也是让人懵逼的一个地方。

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六星推荐。应该会二刷。期望有点大……读到后面感觉有点乱。四星吧……20170415

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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

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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????

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等重读吧,过于系统,不适合初学

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