(转) Deep Learning Resources
转自:http://www.jeremydjacksonphd.com/category/deep-learning/
Deep Learning Resources
Videos
- Deep Learning and Neural Networks with Kevin Duh: course page
- NY Course by Yann LeCun: 2014 version, 2015 version
- NIPS 2015 Deep Learning Tutorial by Yann LeCun and Yoshua Bengio (slides)(mp4,wmv)
- ICML 2013 Deep Learning Tutorial by Yann Lecun (slides)
- Geoffery Hinton’s cousera course on Neural Networks for Machine Learning
- Stanford 231n Class: Convolutional Neural Networks for Visual Recognition (videos, github, syllabus, subreddit, project, final reports, twitter)
- Large Scale Visual Recognition Challenge 2014, arxiv paper
- GTC Deep Learning 2015
- Hugo Larochelle Neural Networks class, slides
- My youtube playlist
- Yaser Abu-Mostafa’s Learning from Data course (youtube playlist)
- Stanford CS224d: Deep Learning for Natural Language Processing: syllabus, youtube playlist, reddit, longer playlist
- Neural Networks for Machine Perception: vimeo
- Deep Learning for NLP (without magic): page, better page, video1, video2, youtube playlist
- Introduction to Deep Learning with Python: video, slides, code
- Machine Learning course with emphasis on Deep Learning by Nando de Freitas (youtube playlist), course page, torch practicals
- NIPS 2013 Deep Learning for Computer Vision Tutorial – Rob Fergus: video, slides
- Tensorflow Udacity mooc
Links
- Deeplearning.net
- NVidia’s Deep Learning portal
- My flipboard page
AMIs, Docker images & Install Howtos
- 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.
- 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.
- Chef cookbook for installing the Caffe deep learning framework
- Public EC2 AMI with Torch and Caffe deep learning toolkits (ami-027a4e6a): page
- Install Theano on AWS (ami-b141a2f5 with CUDA 7): page
- Running Caffe on AWS Instance via Docker: page, docs, image
- CVPR 2015 ITorch Tutorial (ami-b36981d8): page, github, cheatsheet
- Torch/iTorch/Ubuntu 14.04 Docker image: docker pull kaixhin/torch
- Torch/iTorch/CUDA 7/Ubuntu 14.04 Docker image: docker pull kaixhin/cuda-torch
- AMI containing Caffe, Python, Cuda 7, CuDNN, and all dependencies. Its id is ami-763a311e (disk min 8G,system is 4.6G), howto
- My Dockerfiles at GitHub
Examples and Tutorials
- IPython Caffe Classification
- IPython Detection, arxiv paper, rcnn github, selective search
- Machine Learning with Torch 7
- Deep Learning Tutorials with Theano/Python, CNN, github
- Torch tutorials, tutorial&demos from Clement Fabaret
- Brewing Imagenet with Caffe
- Training an Object Classifier in Torch-7 on multiple GPUs over ImageNet
- Stanford Deep Learning Matlab based Tutorial (github, data)
- DIY Deep Learning for Vision: A Hands on tutorial with Caffe (google doc)
- Tutorial on Deep Learning for Vision CVPR 2014: page
- Pylearn2 tutorials: convolutional network, getthedata
- Pylearn2 quickstart, docs
- So you wanna try deep learning? post from SnippyHollow
- Object Detection ipython nb from SnippyHollow
- Filter Visualization ipython nb from SnippyHollow
- Specifics on CNN and DBN, and more
- CVPR 2015 Caffe Tutorial
- Deep Learning on Amazon EC2 GPU with Python and nolearn
- How to build and run your first deep learning network (video, behind paywall)
- Tensorflow examples
- Illia Polosukhin’s Getting Started with Tensorflow – Part 1, Part 2, Part 3
- CNTK Tutorial at NIPS 2015
- CNTK: FFN, CNN, LSTM, RNN
- CNTK Introduction and Book
People
- Geoffery Hinton: Homepage, Reddit AMA (11/10/2014)
- Yann LeCun: Homepage, NYU Research Page, Reddit AMA (5/15/2014)
- Yoshua Bengio: Homepage, Reddit AMA (2/27/2014)
- Clement Fabaret: Scene Parsing (paper), github, code page
- Andrej Karpathy: Homepage, twitter, github, blog
- Michael I Jordan: Homepage, Reddit AMA (9/10/2014)
- Andrew Ng: Homepage, Reddit AMA (4/15/2015)
- Jurden Schmidhuber: Homepage, Reddit AMA (3/4/2015)
- Nando de Freitas: Homepage, YouTube, Reddit AMA (12/26/2015)
Datasets
- ImageNet
- MNIST (Wikipedia), database
- Kaggle datasets
- Kitti Vision Benchmark Suite
- Ford Campus Vision and Lidar Dataset
- PCL Lidar Datasets
- Pylearn2 list
Frameworks and Libraries
- Caffe: homepage, github, google group
- Torch: homepage, cheatsheet, github, google group
- Theano: homepage, google group
- Tensorflow: homepage, github, google group, skflow
- CNTK: homepage, github, wiki
- CuDNN: homepage
- PaddlePaddle: homepage, github, docs, quick start
- fbcunn: github
- pylearn2: github, docs
- cuda-convnet2: homepage, cuda-convnet, matlab
- nnForge: homepage
- Deep Learning software links
- Torch vs. Theano post
- Overfeat: page, github, paper, slides, google group
- Keras: github, docs, google group
- Deeplearning4j: page, github
- Lasagne: docs, github
Topics
- Scene Understanding (CVPR 2013, Lecun) (slides), Scene Parsing (paper)
- Overfeat: Integrated Recognition, Localization and Detection using Convolutional Networks (arxiv)
- Parsing Natural Scenes and Natural Language with Recursive Neural Networks: page, ICML 2011 paper
- Machine Learning Reddit page
- Computer Vision Reddit page
- Reddit: Neural Networks: new, relevant
- Reddit: Deep Learning: new, relevant
Books
- Learning Deep Architectures for AI, Bengio (pdf)
- Neural Nets and Deep Learning (html, github)
- Deep Learning, Bengio, Goodfellow, Courville (html)
- Neural Nets and Learning Machines, Haykin, 2008 (amazon)
Papers
- ImageNet Classification with Deep Convolutional Neural Networks, Alex Krizhevsky, Ilya Sutskever, Geoffrey E Hinton, NIPS 2012 (paper)
- Why does unsupervised pre-training help deep learning? (paper)
- Hinton06 – Autoencoders (paper)
- Deep Learning using Linear Support Vector machines (paper)
Companies
- Kaggle: homepage
- Microsoft Deep Learning Technology Center
Conferences
- ICML
- PAMITC Sponsored Conferences
- NIPS: 2015
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