机器学习（三）深度学习的经典论文、代码、博客文章

Bertha 。 2022-07-17 00:37 219阅读 0赞

# 前言 #

总结了Deep Learning应用相关的经典论文、代码、博客文章之类，包括CNN、RCNN、DQN、RNN等，github上看到。

原文地址：https://github.com/kristjankorjus/applied-deep-learning-resources

因为csdn贴图太麻烦，就把原网站全部截图以便查找。

# CNN+RCNN+DQN+RNN大集合 #

![20160824082231188][]

![20160824082633830][]

![20160824082742213][]

![20160824082834596][]

![20160824082946800][]

# 深度学习入门必看的书和论文 #

## 深度学习书+论文+课程 ##

Deep Learning，是这几年非常火的一个方向，有关入门的书籍和论文，摘自知乎大神回答，备份这里给自己留着，下面入正题。

DL的科普性文章，快速浅显了解深度学习：[http://www.nature.com/nature/journal/v521/n7553/full/nature14539.html][http_www.nature.com_nature_journal_v521_n7553_full_nature14539.html]

深度学习大牛Bengio，下图右2，出版的一本书《Deep Learning》written by Benigio at 2015-10-03：[http://www.deeplearningbook.org/][http_www.deeplearningbook.org]

深度学习在计算机视觉领域打响的第一枪，NIPS'2012 ：[http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf][http_papers.nips.cc_paper_4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf]

Andrew Ng，右一，写的tutorial：[http://deeplearning.stanford.edu/wiki/index.php/UFLDL\_Tutorial][http_deeplearning.stanford.edu_wiki_index.php_UFLDL_Tutorial]

斯坦福大学教授 Li Feifei CS231n课程：[http://cs231n.stanford.edu/][http_cs231n.stanford.edu]

## 深度学习必备的DL库 ##

MatConvNet：[http://www.vlfeat.org/matconvnet/][http_www.vlfeat.org_matconvnet]

Caffe | Deep Learning Framework：[http://caffe.berkeleyvision.org/][http_caffe.berkeleyvision.org]

Torch | Scientific Computing for LuaJIT ：[http://torch.ch/][http_torch.ch]

以上3个库都有自己的mannual甚至是tutorial，手把手教你搭建深度网络。

## 编程语言分类深度学习库 ##

按照编程语言整理出的深度学习库列表，仅供学习：[http://www.teglor.com/b/deep-learning-libraries-language-cm569/][http_www.teglor.com_b_deep-learning-libraries-language-cm569]

## 阿门 ##

本节末祭出深度学习四大神合影图，阿门

![20160905104736055][]

[20160824082231188]: /images/20220717/9cf0c1233f0447c681c1afc873df9b3f.png
[20160824082633830]: /images/20220717/139ca158b74845baa7ff4449cf8a9ffe.png
[20160824082742213]: /images/20220717/a373bc0a647448ab9471155cf0ae2b7f.png
[20160824082834596]: /images/20220717/0a182f053a1b4c57898d8d34018f79d6.png
[20160824082946800]: /images/20220717/e7d8d53d2d5c42f6b6695784a7194c69.png
[http_www.nature.com_nature_journal_v521_n7553_full_nature14539.html]: http://www.nature.com/nature/journal/v521/n7553/full/nature14539.html
[http_www.deeplearningbook.org]: http://www.deeplearningbook.org/
[http_papers.nips.cc_paper_4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf]: http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf
[http_deeplearning.stanford.edu_wiki_index.php_UFLDL_Tutorial]: http://deeplearning.stanford.edu/wiki/index.php/UFLDL_Tutorial
[http_cs231n.stanford.edu]: http://cs231n.stanford.edu/
[http_www.vlfeat.org_matconvnet]: http://www.vlfeat.org/matconvnet/
[http_caffe.berkeleyvision.org]: http://caffe.berkeleyvision.org/
[http_torch.ch]: http://torch.ch/
[http_www.teglor.com_b_deep-learning-libraries-language-cm569]: http://www.teglor.com/b/deep-learning-libraries-language-cm569/
[20160905104736055]: /images/20220717/1286d3c313dc4b37a4d31584aab2afc0.png