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.

具体描述

读后感

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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...  

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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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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...  

用户评价

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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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这本书写得不仅用心,也良心(网上免费发布)。这三个月几乎所有业余时间都耗这书上,系统学习的感觉就是爽。终于可以开始动手了,我的键盘早已饥渴难耐。哈哈~

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三个星期读完了第一遍,有很多切入角度不错,有很多地方看不懂,需要读论文,抽空再刷一遍

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等重读吧,过于系统,不适合初学

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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。

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