深度学习入门与综述资料

更新于 2015年6月4日 问答
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1 2015年6月4日

深度学习

contributors: @自觉自愿来看老婆微博 @邓侃 @星空下的巫师

created: 2014-09-16

初学入门

http://en.wikipedia.org/wiki/Deep_learning Deep learning is a set of algorithms in machine learning that attempt to model high-level abstractions in data by using model architectures composed of multiple non-linear transformations.

### 科普短文

http://cacm.acm.org/magazines/2013/6/164601-deep-learning-comes-of-age/abstract Deep Learning Comes of Age * Gary Anthes. 2013. Commun. ACM 56, 6 (June 2013),下载PDF http://phdtree.org/pdf/29093526-deep-learning-comes-of-age/ * @星空下的巫师 @自觉自愿来看老婆微博 共同推荐

http://www.datarobot.com/blog/a-primer-on-deep-learning/ A Primer on Deep Learning (2014)

### 基于编程语言的实战入门 * http://deeplearning.net/tutorial/gettingstarted.html Getting Started (通过python编程学习基本概念) * http://karpathy.github.io/neuralnets/ 以独特视角讲NN(Javascript ConvNetJS )

### 入门指南

http://deeplearning.net/tutorial/ Deep Learning Tutorials * 600+ star on github

http://neuralnetworksanddeeplearning.com/index.html Michael Nielsen (2014) 概念讲得很细致 * @自觉自愿来看老婆微博 共同推荐

邓侃 Deep Learning 系列 * http://blog.sina.com.cn/s/blog_46d0a3930101fswl.html Deep Learning 和 Knowledge Graph 引爆大数据革命 * http://blog.sina.com.cn/s/blog_46d0a3930101gs5h.html Deep Learning 【2,3】 * http://blog.sina.com.cn/s/blog_46d0a3930101h6nf.html Deep Learning 教程翻译

http://www.reddit.com/r/MachineLearning/comments/2fxi6v/ama_michael_i_jordan/ckdqtpe 伯克利Michael Jordan教授论深度学习, 附上学习笔记 1. layer,parallel,ensemble有用,不能限于模拟人脑思维 2. backpropagation是关键, 本质是supervised learning 3. 很多成功案例是大规模样本+监督学习 4. 很少用在工业界咨询,不少其它问题(7个例子) 5. 机器学习不止是AI,还要接近system与数据库

综述与分支

注意Vision、Text、Speech都用DL,用法不尽相同

http://research.microsoft.com/pubs/204048/APSIPA-Trans2013-revised-final.pdf Li Deng, A Tutorial Survey of Architectures, Algorithms, and Applications for Deep Learning , in APSIPA Transactions on Signal and Information Processing, Cambridge University Press, 2014 * 还有一个大部头 http://research.microsoft.com/pubs/209355/DeepLearning-NowPublishing-Vol7-SIG-039.pdf Deep Learning Methods and Applications, Li Deng and Dong Yu

### Text 文本 NLP http://nlp.stanford.edu/courses/NAACL2013/ Deep Learning for Natural Language Processing (without Magic) * 自然语言处理 NLP 方向(文本为主)

### Speech 语音 NLP http://research.microsoft.com/pubs/217165/ICASSP_DeepTextLearning_v07.pdf Deep learning for natural language processing and related applications (Tutorial at ICASSP) * Xiaodong He, Jianfeng Gao, and Li Deng * 自然语言处理 NLP 方向 (语音为主,也包括文本) * spoken language understanding (SLU), machine translation (MT), and semantic information retrieval (IR) from text.

### Computer Vision 视觉 https://sites.google.com/site/deeplearningcvpr2014/ TUTORIAL ON DEEP LEARNING FOR VISION * Computer vision, CVPR 2014 Tutorial * 计算机视觉 方向 * cardbox http://bigdata.memect.com/?tag=cvpr2014+vision

Yann LeCun’s Lecture on Computer Perception with Deep Learning in Course 9.S912: “Vision and learning – computers and brains”, Nov 12, 2013: * Part1: http://techtv.mit.edu/videos/26739-yann-lecun-computer-perception-with-deep-learning-part-1 * Part2: http://techtv.mit.edu/videos/26740-yann-lecun-computer-perception-with-deep-learning-part-2 * 计算机视觉 方向

过去的相关推荐

计算工具

###theano

### caffe

### Torch-7

### matlab deeplearning toolbox

[]: http://forum.ai100.com.cn/hashtag/%e6%b7%b1%e5%ba%a6%e5%ad%a6%e4%b9%a0/

回复

半年来发展日新月异,这个整理可能已经过时了。更新进展请看《机器学习日报》上的深度学习条目,已有1200条之多 

http://ml.memect.com/search/?q=tag:深度学习

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