Videos

1. Deep Learning and Neural Networks with Kevin Duh: 
2. NY Course by Yann LeCun: , 
3. NIPS 2015  by Yann LeCun and Yoshua Bengio ()(,)
4. ICML 2013  by Yann Lecun ()
5. Geoffery Hinton’s cousera course on 
6. Stanford 231n : Convolutional Neural Networks for Visual Recognition (, , , , , , )
7. Large Scale Visual Recognition Challenge , arxiv 
8. GTC Deep Learning 
9. Hugo Larochelle Neural Networks , 
10. My youtube 
11. Yaser Abu-Mostafa’s Learning from Data  (youtube )
12. Stanford CS224d: Deep Learning for Natural Language Processing: , youtube , , 
13. Neural Networks for Machine Perception: 
14. Deep Learning for NLP (without magic): , , , , 
15. Introduction to Deep Learning with Python: , , 
16. Machine Learning course with emphasis on Deep Learning by Nando de Freitas (), course , torch 
17. NIPS 2013 Deep Learning for Computer Vision Tutorial – Rob Fergus: , 
18. Tensorflow 

Links

2. NVidia’s Deep Learning 
3. My flipboard 

AMIs, Docker images & Install Howtos

1. Stanford 231n  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: ,  Installed stuffs: Intel MKL, CUDA 7.0, cuDNN v2, theano, pylearn2, CXXNET, Caffe, cuda-convnet2, OverFeat, nnForge, Graphlab Create (GPU), etc.
3.  for installing the 
4. Public EC2 AMI with Torch and Caffe deep learning toolkits (ami-027a4e6a): 
5. Install Theano on AWS (ami-b141a2f5 with CUDA 7): 
6. Running Caffe on AWS Instance via Docker: , , 
7. CVPR 2015 ITorch Tutorial (ami-b36981d8): , , 
8. Torch/iTorch/Ubuntu 14.04 Docker : docker pull kaixhin/torch
9. Torch/iTorch/CUDA 7/Ubuntu 14.04 Docker : 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), 
11. My  at GitHub

Examples and Tutorials

1. IPython Caffe 
2. IPython , arxiv , rcnn , selective 
3.  with Torch 7
4. Deep Learning  with Theano/Python, , 
5. Torch , 
6.  with Caffe
7.  an Object Classifier in Torch-7 on multiple GPUs over 
8. Stanford Deep Learning Matlab based  (, )
9. DIY Deep Learning for Vision: A Hands on tutorial with Caffe ()
10. Tutorial on Deep Learning for Vision CVPR 2014: 
11. Pylearn2 : , 
12. Pylearn2 , 
13. So you wanna try deep learning?  from SnippyHollow
14. Object Detection  from SnippyHollow
15. Filter Visualization  from SnippyHollow
16.  on CNN and DBN, and 
17. CVPR 2015 Caffe 
18.  on Amazon EC2 GPU with Python and nolearn
19.  build and run your first deep learning network (, behind paywall)
20. Tensorflow 
21. Illia Polosukhin’s Getting Started with Tensorflow – , , 
22.  at NIPS 2015
23. CNTK: , , , 
24. CNTK  and 

People

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

Datasets

2. MNIST (), 
3. Kaggle 
4.  Vision Benchmark Suite
5. Ford Campus Vision and Lidar 
6. PCL Lidar 
7. Pylearn2 

Frameworks and Libraries

1. Caffe: , , 
2. Torch: , , , 
3. Theano: , 
4. Tensorflow: , , , 
5. CNTK: , , 
6. CuDNN: 
7. PaddlePaddle: , , , 
8. fbcunn: 
9. pylearn2: , 
10. cuda-convnet2: , , 
11. nnForge: 
12. Deep Learning software 
13. Torch vs. Theano 
14. Overfeat: , , , , 
15. Keras: , , 
16. Deeplearning4j: , 
17. Lasagne: , 

Topics

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

Reddit

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

Books

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

Papers

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

Companies

1. Kaggle: 
2. Microsoft 

Conferences

2. PAMITC 
3. :