(turn) deep learning Resources

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Deep learning Resources

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Videos

2. Deep learning and neural Networks with Kevin duh:course page
4. NY Course by Yann lecun:2014 version, version
6. NIPS Deep Learning Tutorial by Yann LeCun and Yoshua Bengio (slides) (mp4,wmv)
8. ICML Learning Tutorial by Yann lecun (slides)
10. Geoffery Hinton ' s Cousera course on neural Networks for machine learning
12. Stanford 231n class:convolutional Neural Networks for Visual recognition (videos, GitHub, syllabus, subreddit, project, F Inal reports, Twitter)
14. Large scale Visual Recognition challenge, arxiv paper
16. GTC Deep Learning 2015
18. Hugo Larochelle Neural Networks class, slides
20. My YouTube Playlist
22. Yaser Abu-mostafa ' s learning from Data course (YouTube playlist)
24. Stanford Cs224d:deep Learning for Natural Language Processing:syllabus, YouTube playlist, Reddit, longer playlist
26. Neural Networks for Machine Perception:vimeo
28. Deep Learning for NLP (without Magic): page, better page, Video1, Video2, YouTube playlist
30. Introduction to deep learning with python:video, slides, code
32. Machine learning course with emphasis on deep learning by Nando de Freitas (YouTube Playlist), course page, torch Practica Ls
34. NIPS Learning for computer Vision tutorial–rob Fergus:video, slides
36. TensorFlow Udacity Mooc

Links

1. Deeplearning.net
2. NVidia ' s deep learning Portal
3. My Flipboard Page

AMIs, Docker images & Install HowTos

2. Stanford 231n AWS ami:image cs231n_caffe_torch7_keras_lasagne_v2 is, 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.
4. 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), E Tc.
6. Chef Cookbook for installing the Caffe deep Learning framework
8. Public EC2 AMI with Torch and Caffe deep learning toolkits (AMI-027A4E6A): page
10. Install Theano on AWS (ami-b141a2f5 with CUDA 7): page
12. Running Caffe on AWS Instance via Docker:page, docs, image
14. CVPR itorch Tutorial (ami-b36981d8): page, GitHub, Cheatsheet
16. Torch/itorch/ubuntu 14.04 Docker Image:docker Pull Kaixhin/torch
18. Torch/itorch/cuda 7/ubuntu 14.04 Docker image:docker Pull Kaixhin/cuda-torch
20. AMI containing Caffe, Python, Cuda 7, CuDNN, and all dependencies. Its ID is ami-763a311e (disk min 8g,system is 4.6G), howto
22. My Dockerfiles at GitHub

Examples and Tutorials

2. IPython Caffe Classification
4. IPython Detection, arxiv paper, rcnn GitHub, selective search
6. Machine learning with Torch 7
8. Deep learning tutorials with Theano/python, CNN, GitHub
10. Torch tutorials, Tutorial&demos from Clement Fabaret
12. Brewing Imagenet with Caffe
14. Training an Object Classifier in Torch-7 on multiple GPUs over ImageNet
16. Stanford Deep Learning Matlab based Tutorial (GitHub, data)
18. DIY Deep Learning for vision:a hands in tutorial with Caffe (Google doc)
20. Tutorial on deep learning for Vision CVPR 2014:page
22. PYLEARN2 tutorials:convolutional Network, Getthedata
24. PYLEARN2 QuickStart, Docs
26. So, wanna try deep learning? Post from Snippyhollow
28. Object Detection Ipython nb from Snippyhollow
30. Filter visualization Ipython nb from Snippyhollow
32. Specifics on CNN and DBN, and more
34. CVPR Caffe Tutorial
36. Deep learning on Amazon EC2 GPUs with Python and Nolearn
38. How to build and run your first deep learning network (video, behind paywall)
40. TensorFlow examples
42. Illia Polosukhin ' s Getting Started with Tensorflow–part 1, Part 2, Part 3
44. CNTK Tutorial at NIPS 2015
46. CNTK:FFN, CNN, LSTM, RNN
48. CNTK Introduction and book

People

1. Geoffery Hinton:homepage, Reddit AMA (11/10/2014)
2. Yann lecun:homepage, NYU, 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, 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 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, (Amazon)

Papers

1. ImageNet classification with deep convolutional neural Networks, Alex Krizhevsky, Ilya sutskever, Geoffrey E Hinton, NIPS (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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(turn) deep learning Resources