Deep Learning

Deep Learning pdf epub mobi txt 電子書 下載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:
頁數:800
译者:
出版時間:2016-11-11
價格:USD 72.00
裝幀:Hardcover
isbn號碼:9780262035613
叢書系列:Adaptive Computation and Machine Learning
圖書標籤:
  • 深度學習 
  • 機器學習 
  • DeepLearning 
  • 人工智能 
  • AI 
  • MachineLearning 
  • 計算機 
  • 計算機科學 
  •  
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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.

具體描述

讀後感

評分

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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。 ————————————————————————————————————————————————————————————————————————————————————...  

評分

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...  

評分

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。...  

評分

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...  

用戶評價

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三個星期讀完瞭第一遍,有很多切入角度不錯,有很多地方看不懂,需要讀論文,抽空再刷一遍

评分

三個星期讀完瞭第一遍,有很多切入角度不錯,有很多地方看不懂,需要讀論文,抽空再刷一遍

评分

和PRML比較起來,明顯感覺數學公式少瞭很多,作者也有提到深度學習很多部分沒有很好的數學支持,所以大段文字描述很容易讓人思路跟丟瞭。同時,很多主題也能意識到是很大的獨立主題,肯定隻能帶過式的講解,但是又沒有淺的引入部分,好像直接就比較深入,也是讓人懵逼的一個地方。

评分

a great list of papers

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這書不錯,前麵快200頁基礎,沒有統計和機器學習背景也可以看。我從後來開始看,覺得很不錯,可以入門做阿爾法狗瞭~~~入門

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