Table of Contents

• Courses

  • Bay area DL school
  • Bay area DL school
  • Deep Learning Course
  • Bay area DL school
  • Bay area DL school
  • Bay area DL school
  • Bay area DL school
  • Bay area DL school
  • Bay area DL school
  • Bay area DL school
  • Bay area DL school
  • Bay area DL school
  • Machine Learning - Stanford - 2014)
  • Machine Learning - Carnegie Mellon
  • Neural Networks for Machine Learning
  • A.I - MIT
  • Vision and learning - computers and brains
  • Convolutional Neural Networks for Visual Recognition - Stanford - Fei Li, Andrej Karpathy (2015)
  • Convolutional Neural Networks for Visual Recognition - Stanford - Fei Li, Andrej Karpathy (2016)
  • Deep Learning for Natural Language Processing - Stanford
  • Neural Networks - usherbrooke
  • Machine Learning - Oxford - 2015)
  • Deep Learning - Udacity/Google
  • Bay area DL school
  • MIT 6.S094: Deep Learning for Self-Driving Cars
  • MIT 6.S191: Introduction to Deep Learning
  • Keras in Motion video course
  • Bay area DL school
  • Bay area DL school
  • Bay area DL school
  • Bay area DL school
  • Bay area DL school
  • Bay area DL school
  • Bay area DL school
  • Bay area DL school
  • Deep Learning - UWaterloo
  • Bay area DL school
  • Bay area DL school
  • Bay area DL school
  • Bay area DL school
  • Bay area DL school
  • Bay area DL school
  • Bay area DL school
  • Bay area DL school
  • Bay area DL school
  • Bay area DL school

• Tutorials

  • A Deep Learning Tutorial: From Perceptrons to Deep Networks
  • UFLDL Tutorial 1
  • UFLDL Tutorial 2
  • Deep Learning for NLP (without Magic)
  • Deep Learning from the Bottom up
  • Neural Networks for Matlab
  • Torch7 Tutorials

• Videos and Lectures

  • A beginners Guide to Deep Neural Networks
  • How To Create A Mind
  • Deep Learning, Self-Taught Learning and Unsupervised Feature Learning
  • The Unreasonable Effectiveness of Deep Learning
  • Deep Learning of Representations
  • Principles of Hierarchical Temporal Memory
  • Making Sense of the World with Deep Learning
  • Demystifying Unsupervised Feature Learning
  • Visual Perception with Deep Learning
  • The Next Generation of Neural Networks
  • Unsupervised Deep Learning - Stanford
  • Natural Language Processing
  • Deep Learning: Intelligence from Big Data
  • Introduction to Artificial Neural Networks and Deep Learning
  • NIPS 2016 lecture and workshop videos - NIPS 2016
  • Github Tutorial For Beginners

• Miscellaneous

  • Caffe Webinar
  • Caffe DockerFile
  • Torch7 Cheat sheet
  • Misc from MIT's 'Machine Learning' course
  • An efficient, batched LSTM.
  • Emotion Recognition API Demo - Microsoft
  • AlphaGo - A replication of DeepMind's 2016 Nature publication, "Mastering the game of Go with deep neural networks and tree search"
  • Machine Learning is Fun!

• Free Online Books

  • neuraltalk - based RNN/LSTM implementation
  • Deep Learning Tutorial
  • Deep Learning
  • Neural Networks and Deep Learning
  • An introduction to genetic algorithms
  • Artificial Intelligence: A Modern Approach
  • Deep Learning in Neural Networks: An Overview

• Papers

  • Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
  • here
  • Using Very Deep Autoencoders for Content Based Image Retrieval
  • Learning Deep Architectures for AI
  • CMU’s list of papers
  • Training tricks by YB
  • Geoff Hinton's reading list (all papers)
  • Supervised Sequence Labelling with Recurrent Neural Networks
  • Statistical Language Models based on Neural Networks
  • Training Recurrent Neural Networks
  • Recursive Deep Learning for Natural Language Processing and Computer Vision
  • Bi-directional RNN
  • LSTM
  • GFRNN - supp.pdf)
  • LSTM: A Search Space Odyssey
  • A Critical Review of Recurrent Neural Networks for Sequence Learning
  • Visualizing and Understanding Recurrent Networks
  • Wojciech Zaremba, Ilya Sutskever, An Empirical Exploration of Recurrent Network Architectures
  • Recurrent Neural Network based Language Model
  • Extensions of Recurrent Neural Network Language Model
  • Recurrent Neural Network based Language Modeling in Meeting Recognition
  • Deep Neural Networks for Acoustic Modeling in Speech Recognition
  • Speech Recognition with Deep Recurrent Neural Networks
  • Microsoft - Jointly Modeling Embedding and Translation to Bridge Video and Language
  • Neural Turing Machines
  • Ask Me Anything: Dynamic Memory Networks for Natural Language Processing
  • Mastering the Game of Go with Deep Neural Networks and Tree Search
  • Batch Normalization
  • Residual Learning
  • MobileNets by Google
  • Cross Audio-Visual Recognition in the Wild Using Deep Learning
  • Reinforcement Learning Neural Turing Machines
  • Memory Networks
  • Policy Learning with Continuous Memory States for Partially Observed Robotic Control
  • Recursive Deep Learning for Natural Language Processing and Computer Vision
  • Berkeley AI Research (BAIR) Laboratory

• Datasets

  • The AR Face Database - Contains over 4,000 color images corresponding to 126 people's faces (70 men and 56 women). Frontal views with variations in facial expressions, illumination, and occlusions. (Formats: RAW (RGB 24-bit))
  • Efficient Content-based Retrieval Group
  • MNIST
  • Google House Numbers
  • CIFAR-10 and CIFAR-100
  • Tiny Images
  • Flickr Data
  • Berkeley Segmentation Dataset 500
  • Flickr 8k
  • Flickr 30k
  • Microsoft COCO
  • Image QA
  • AT&T Laboratories Cambridge face database
  • AVHRR Pathfinder
  • Amsterdam Library of Object Images - ALOI is a color image collection of one-thousand small objects, recorded for scientific purposes. In order to capture the sensory variation in object recordings, we systematically varied viewing angle, illumination angle, and illumination color for each object, and additionally captured wide-baseline stereo images. We recorded over a hundred images of each object, yielding a total of 110,250 images for the collection. (Formats: png)
  • Annotated face, hand, cardiac & meat images - Most images & annotations are supplemented by various ASM/AAM analyses using the AAM-API. (Formats: bmp,asf)
  • Image Analysis and Computer Graphics
  • Brown University Stimuli - A variety of datasets including geons, objects, and "greebles". Good for testing recognition algorithms. (Formats: pict)
  • CCITT Fax standard images - 8 images (Formats: gif)
  • CMU VASC Image Database - Images, sequences, stereo pairs (thousands of images) (Formats: Sun Rasterimage)
  • Caltech Image Database - about 20 images - mostly top-down views of small objects and toys. (Formats: GIF)
  • Columbia-Utrecht Reflectance and Texture Database - Texture and reflectance measurements for over 60 samples of 3D texture, observed with over 200 different combinations of viewing and illumination directions. (Formats: bmp)
  • Computational Colour Constancy Data - A dataset oriented towards computational color constancy, but useful for computer vision in general. It includes synthetic data, camera sensor data, and over 700 images. (Formats: tiff)
  • Computational Vision Lab
  • Densely Sampled View Spheres - Densely sampled view spheres - upper half of the view sphere of two toy objects with 2500 images each. (Formats: tiff)
  • Digital Embryos - Digital embryos are novel objects which may be used to develop and test object recognition systems. They have an organic appearance. (Formats: various formats are available on request)
  • Univerity of Minnesota Vision Lab
  • FG-NET Facial Aging Database - Database contains 1002 face images showing subjects at different ages. (Formats: jpg)
  • FVC2000 Fingerprint Databases - FVC2000 is the First International Competition for Fingerprint Verification Algorithms. Four fingerprint databases constitute the FVC2000 benchmark (3520 fingerprints in all).
  • Biometric Systems Lab - University of Bologna
  • German Fingerspelling Database - The database contains 35 gestures and consists of 1400 image sequences that contain gestures of 20 different persons recorded under non-uniform daylight lighting conditions. (Formats: mpg,jpg)
  • Language Processing and Pattern Recognition
  • Groningen Natural Image Database - 4000+ 1536x1024 (16 bit) calibrated outdoor images (Formats: homebrew)
  • ICG Testhouse sequence - 2 turntable sequences from ifferent viewing heights, 36 images each, resolution 1000x750, color (Formats: PPM)
  • IEN Image Library - 1000+ images, mostly outdoor sequences (Formats: raw, ppm)
  • INRIA's Syntim images database - 15 color image of simple objects (Formats: gif)
  • INRIA's Syntim stereo databases - 34 calibrated color stereo pairs (Formats: gif)
  • Image Analysis Laboratory - Images obtained from a variety of imaging modalities -- raw CFA images, range images and a host of "medical images". (Formats: homebrew)
  • Image Database - An image database including some textures
  • JAFFE Facial Expression Image Database - The JAFFE database consists of 213 images of Japanese female subjects posing 6 basic facial expressions as well as a neutral pose. Ratings on emotion adjectives are also available, free of charge, for research purposes. (Formats: TIFF Grayscale images.)
  • ATR Research, Kyoto, Japan
  • MIT Vision Texture - Image archive (100+ images) (Formats: ppm)
  • Machine Vision - Images from the textbook by Jain, Kasturi, Schunck (20+ images) (Formats: GIF TIFF)
  • Mammography Image Databases - 100 or more images of mammograms with ground truth. Additional images available by request, and links to several other mammography databases are provided. (Formats: homebrew)
  • Middlebury Stereo Data Sets with Ground Truth - Six multi-frame stereo data sets of scenes containing planar regions. Each data set contains 9 color images and subpixel-accuracy ground-truth data. (Formats: ppm)
  • Middlebury Stereo Vision Research Page - Middlebury College
  • Modis Airborne simulator, Gallery and data set - High Altitude Imagery from around the world for environmental modeling in support of NASA EOS program (Formats: JPG and HDF)
  • National Design Repository - Over 55,000 3D CAD and solid models of (mostly) mechanical/machined engineerign designs. (Formats: gif,vrml,wrl,stp,sat)
  • Geometric & Intelligent Computing Laboratory
  • Otago Optical Flow Evaluation Sequences - Synthetic and real sequences with machine-readable ground truth optical flow fields, plus tools to generate ground truth for new sequences. (Formats: ppm,tif,homebrew)
  • Photometric 3D Surface Texture Database - This is the first 3D texture database which provides both full real surface rotations and registered photometric stereo data (30 textures, 1680 images). (Formats: TIFF)
  • SEQUENCES FOR OPTICAL FLOW ANALYSIS (SOFA) - 9 synthetic sequences designed for testing motion analysis applications, including full ground truth of motion and camera parameters. (Formats: gif)
  • Computer Vision Group
  • Stereo Images with Ground Truth Disparity and Occlusion - a small set of synthetic images of a hallway with varying amounts of noise added. Use these images to benchmark your stereo algorithm. (Formats: raw, viff (khoros), or tiff)
  • Stuttgart Range Image Database - A collection of synthetic range images taken from high-resolution polygonal models available on the web (Formats: homebrew)
  • The AR Face Database - Contains over 4,000 color images corresponding to 126 people's faces (70 men and 56 women). Frontal views with variations in facial expressions, illumination, and occlusions. (Formats: RAW (RGB 24-bit))
  • The MIT-CSAIL Database of Objects and Scenes - Database for testing multiclass object detection and scene recognition algorithms. Over 72,000 images with 2873 annotated frames. More than 50 annotated object classes. (Formats: jpg)
  • The RVL SPEC-DB (SPECularity DataBase) - A collection of over 300 real images of 100 objects taken under three different illuminaiton conditions (Diffuse/Ambient/Directed). -- Use these images to test algorithms for detecting and compensating specular highlights in color images. (Formats: TIFF )
  • Robot Vision Laboratory
  • The Xm2vts database - The XM2VTSDB contains four digital recordings of 295 people taken over a period of four months. This database contains both image and video data of faces.
  • Centre for Vision, Speech and Signal Processing
  • Traffic Image Sequences and 'Marbled Block' Sequence - thousands of frames of digitized traffic image sequences as well as the 'Marbled Block' sequence (grayscale images) (Formats: GIF)
  • IAKS/KOGS
  • U Oulu wood and knots database - Includes classifications - 1000+ color images (Formats: ppm)
  • UCID - an Uncompressed Colour Image Database - a benchmark database for image retrieval with predefined ground truth. (Formats: tiff)
  • UMass Vision Image Archive - Large image database with aerial, space, stereo, medical images and more. (Formats: homebrew)
  • USF Range Image Data with Segmentation Ground Truth - 80 image sets (Formats: Sun rasterimage)
  • University of Oulu Physics-based Face Database - contains color images of faces under different illuminants and camera calibration conditions as well as skin spectral reflectance measurements of each person.
  • Machine Vision and Media Processing Unit
  • University of Oulu Texture Database - Database of 320 surface textures, each captured under three illuminants, six spatial resolutions and nine rotation angles. A set of test suites is also provided so that texture segmentation, classification, and retrieval algorithms can be tested in a standard manner. (Formats: bmp, ras, xv)
  • Machine Vision Group
  • View Sphere Database - Images of 8 objects seen from many different view points. The view sphere is sampled using a geodesic with 172 images/sphere. Two sets for training and testing are available. (Formats: ppm)
  • PRIMA, GRAVIR
  • Wiry Object Recognition Database - Thousands of images of a cart, ladder, stool, bicycle, chairs, and cluttered scenes with ground truth labelings of edges and regions. (Formats: jpg)
  • 3D Vision Group
  • Yale Face Database - 165 images (15 individuals) with different lighting, expression, and occlusion configurations.
  • Yale Face Database B - 5760 single light source images of 10 subjects each seen under 576 viewing conditions (9 poses x 64 illumination conditions). (Formats: PGM)
  • Center for Computational Vision and Control
  • YouTube-8M Dataset - YouTube-8M is a large-scale labeled video dataset that consists of 8 million YouTube video IDs and associated labels from a diverse vocabulary of 4800 visual entities.
  • CMU PIE Database - A database of 41,368 face images of 68 people captured under 13 poses, 43 illuminations conditions, and with 4 different expressions.

• WebSites

  • Deep Learning News
  • deeplearning.net
  • deeplearning.stanford.edu
  • ai-junkie.com
  • eecs.umich.edu/ai
  • www-aig.jpl.nasa.gov
  • cgi.cse.unsw.edu.au/~aishare
  • isi.edu/AI/isd.htm
  • nrl.navy.mil/itd/aic
  • stat.ucla.edu
  • deeplearning.cs.toronto.edu
  • jeffdonahue.com/lrcn/
  • Comprehensive Repository of Data Science and ML Resources
  • cs.brown.edu/research/ai

• Frameworks

  • Caffe
  • Torch7
  • Theano
  • RNNLM Toolkit
  • SyntaxNet - Google's syntactic parser - A TensorFlow dependency library

• Sources

  • Awesome Deep Learning