(转) Deep Learning Resources

转自：http://www.jeremydjacksonphd.com/category/deep-learning/

Deep Learning Resources

Posted on May 13, 2015

 

Videos

1. Deep Learning and Neural Networks with Kevin Duh: course page
2. NY Course by Yann LeCun: 2014 version, 2015 version
3. NIPS 2015 Deep Learning Tutorial by Yann LeCun and Yoshua Bengio (slides)(mp4,wmv)
4. ICML 2013 Deep Learning Tutorial by Yann Lecun (slides)
5. Geoffery Hinton’s cousera course on Neural Networks for Machine Learning
6. Stanford 231n Class: Convolutional Neural Networks for Visual Recognition (videos, github, syllabus, subreddit, project, final reports, twitter)
7. Large Scale Visual Recognition Challenge 2014, arxiv paper
8. GTC Deep Learning 2015
9. Hugo Larochelle Neural Networks class, slides
10. My youtube playlist
11. Yaser Abu-Mostafa’s Learning from Data course (youtube playlist)
12. Stanford CS224d: Deep Learning for Natural Language Processing: syllabus, youtube playlist, reddit, longer playlist
13. Neural Networks for Machine Perception: vimeo
14. Deep Learning for NLP (without magic): page, better page, video1, video2, youtube playlist
15. Introduction to Deep Learning with Python: video, slides, code
16. Machine Learning course with emphasis on Deep Learning by Nando de Freitas (youtube playlist), course page, torch practicals
17. NIPS 2013 Deep Learning for Computer Vision Tutorial – Rob Fergus: video, slides
18. Tensorflow Udacity mooc

Links

1. Deeplearning.net
2. NVidia’s Deep Learning portal
3. My flipboard page

AMIs, Docker images & Install Howtos

1. Stanford 231n AWS AMI:  image is cs231n_caffe_torch7_keras_lasagne_v2, AMI ID: ami-125b2c72, Caffe, Torch7, Theano, Keras and Lasagne are pre-installed. Python bindings of caffe are available. It has CUDA 7.5 and CuDNN v3.
2. AMI for AWS EC2 (g2.2xlarge): ubuntu14.04-mkl-cuda-dl (ami-03e67874) in Ireland Region: page,  Installed stuffs: Intel MKL, CUDA 7.0, cuDNN v2, theano, pylearn2, CXXNET, Caffe, cuda-convnet2, OverFeat, nnForge, Graphlab Create (GPU), etc.
3. Chef cookbook for installing the Caffe deep learning framework
4. Public EC2 AMI with Torch and Caffe deep learning toolkits (ami-027a4e6a): page
5. Install Theano on AWS (ami-b141a2f5 with CUDA 7): page
6. Running Caffe on AWS Instance via Docker: page, docs, image
7. CVPR 2015 ITorch Tutorial (ami-b36981d8): page, github, cheatsheet
8. Torch/iTorch/Ubuntu 14.04 Docker image: docker pull kaixhin/torch
9. Torch/iTorch/CUDA 7/Ubuntu 14.04 Docker image: docker pull kaixhin/cuda-torch
10. AMI containing Caffe, Python, Cuda 7, CuDNN, and all dependencies. Its id is ami-763a311e (disk min 8G,system is 4.6G), howto
11. My Dockerfiles at GitHub

Examples and Tutorials

1. IPython Caffe Classification
2. IPython Detection, arxiv paper, rcnn github, selective search
3. Machine Learning with Torch 7
4. Deep Learning Tutorials with Theano/Python, CNN, github
5. Torch tutorials, tutorial&demos from Clement Fabaret
6. Brewing Imagenet with Caffe
7. Training an Object Classifier in Torch-7 on multiple GPUs over ImageNet
8. Stanford Deep Learning Matlab based Tutorial (github, data)
9. DIY Deep Learning for Vision: A Hands on tutorial with Caffe (google doc)
10. Tutorial on Deep Learning for Vision CVPR 2014: page
11. Pylearn2 tutorials: convolutional network, getthedata
12. Pylearn2 quickstart, docs
13. So you wanna try deep learning? post from SnippyHollow
14. Object Detection ipython nb from SnippyHollow
15. Filter Visualization ipython nb from SnippyHollow
16. Specifics on CNN and DBN, and more
17. CVPR 2015 Caffe Tutorial
18. Deep Learning on Amazon EC2 GPU with Python and nolearn
19. How to build and run your first deep learning network (video, behind paywall)
20. Tensorflow examples
21. Illia Polosukhin’s Getting Started with Tensorflow – Part 1, Part 2, Part 3
22. CNTK Tutorial at NIPS 2015
23. CNTK: FFN, CNN, LSTM, RNN
24. CNTK Introduction and Book

People

1. Geoffery Hinton: Homepage, Reddit AMA (11/10/2014)
2. Yann LeCun: Homepage, NYU Research Page, Reddit AMA (5/15/2014)
3. Yoshua Bengio: Homepage, Reddit AMA (2/27/2014)
4. Clement Fabaret: Scene Parsing (paper), github, code page
5. Andrej Karpathy: Homepage, twitter, github, blog
6. Michael I Jordan: Homepage, Reddit AMA (9/10/2014)
7. Andrew Ng: Homepage, Reddit AMA (4/15/2015)
8. Jurden Schmidhuber: Homepage, Reddit AMA (3/4/2015)
9. Nando de Freitas: Homepage, YouTube, Reddit AMA (12/26/2015)

Datasets

1. ImageNet
2. MNIST (Wikipedia), database
3. Kaggle datasets
4. Kitti Vision Benchmark Suite
5. Ford Campus Vision and Lidar Dataset
6. PCL Lidar Datasets
7. Pylearn2 list

Frameworks and Libraries

1. Caffe: homepage, github, google group
2. Torch: homepage, cheatsheet, github, google group
3. Theano: homepage, google group
4. Tensorflow: homepage, github, google group, skflow
5. CNTK: homepage, github, wiki
6. CuDNN: homepage
7. PaddlePaddle: homepage, github, docs, quick start
8. fbcunn: github
9. pylearn2: github, docs
10. cuda-convnet2: homepage, cuda-convnet, matlab
11. nnForge: homepage
12. Deep Learning software links
13. Torch vs. Theano post
14. Overfeat: page, github, paper, slides, google group
15. Keras: github, docs, google group
16. Deeplearning4j: page, github
17. Lasagne: docs, github

Topics

1. Scene Understanding (CVPR 2013, Lecun) (slides), Scene Parsing (paper)
2. Overfeat: Integrated Recognition, Localization and Detection using Convolutional Networks (arxiv)
3. Parsing Natural Scenes and Natural Language with Recursive Neural Networks: page, ICML 2011 paper

Reddit

1. Machine Learning Reddit page
2. Computer Vision Reddit page
3. Reddit: Neural Networks: new, relevant
4. Reddit: Deep Learning: new, relevant

Books

1. Learning Deep Architectures for AI, Bengio (pdf)
2. Neural Nets and Deep Learning (html, github)
3. Deep Learning, Bengio, Goodfellow, Courville (html)
4. Neural Nets and Learning Machines, Haykin, 2008 (amazon)

Papers

1. ImageNet Classification with Deep Convolutional Neural Networks, Alex Krizhevsky, Ilya Sutskever, Geoffrey E Hinton, NIPS 2012 (paper)
2. Why does unsupervised pre-training help deep learning? (paper)
3. Hinton06 – Autoencoders (paper)
4. Deep Learning using Linear Support Vector machines (paper)

Companies

1. Kaggle: homepage
2. Microsoft Deep Learning Technology Center

Conferences

1. ICML
2. PAMITC Sponsored Conferences
3. NIPS: 2015

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