awesome-deep-learning

A curated list of awesome Deep Learning tutorials, projects and communities.

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A curated list of awesome Machine Learning frameworks, libraries and software.

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:book: A curated list of resources dedicated to Natural Language Processing (NLP)

awesome-deep-learning-papers

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awesome-python

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Quick Overview

The ChristosChristofidis/awesome-deep-learning repository is a curated list of resources for Deep Learning. It serves as a comprehensive collection of free courses, books, videos, papers, software, datasets, and other materials related to deep learning and neural networks. This repository aims to be a one-stop reference for both beginners and experienced practitioners in the field of deep learning.

Pros

Extensive collection of resources covering various aspects of deep learning
Regularly updated with new and relevant content
Well-organized structure, making it easy to find specific topics
Community-driven project with contributions from multiple experts in the field

Cons

May be overwhelming for absolute beginners due to the vast amount of information
Some links may become outdated over time
Lacks detailed explanations or summaries for each resource
Limited curation of resources, potentially including some lower-quality materials

Code Examples

This repository is not a code library, so code examples are not applicable.

Getting Started

This repository is not a code library, so getting started instructions are not applicable. However, users can simply visit the GitHub repository and browse through the categorized list of resources to find materials that suit their needs and interests in deep learning.

Competitor Comparisons

ML-YouTube-Courses

15,878

📺 Discover the latest machine learning / AI courses on YouTube.

Pros of ML-YouTube-Courses

Focuses specifically on video courses, making it easier for visual learners
Organizes content by topic and difficulty level, aiding in course selection
Includes estimated time commitments for each course

Cons of ML-YouTube-Courses

Limited to YouTube content, potentially missing valuable resources from other platforms
May not cover as wide a range of deep learning topics as Awesome Deep Learning
Less frequently updated compared to Awesome Deep Learning

Code Comparison

While both repositories primarily consist of curated lists, ML-YouTube-Courses uses a more structured markdown format:

# ML-YouTube-Courses
| ML Course | Institution | Instructor | Year | Lectures | Duration |
|-----------|-------------|------------|------|----------|----------|
| [Course Name](link) | University | Prof. Name | 2023 | 20 | 40 hours |

Awesome Deep Learning uses a simpler list format:

## Courses

- [Course Name](link)
- [Another Course](link)

Both repositories serve as valuable resources for deep learning enthusiasts, with ML-YouTube-Courses offering a more structured approach to video-based learning, while Awesome Deep Learning provides a broader overview of various deep learning resources.

awesome-machine-learning

65,690

A curated list of awesome Machine Learning frameworks, libraries and software.

Pros of awesome-machine-learning

Broader scope covering various ML topics beyond deep learning
More extensive list of resources and tools
Better organization with clear categorization by programming language and topic

Cons of awesome-machine-learning

Less focused on deep learning specifically
May be overwhelming for beginners due to the sheer volume of information
Some links may be outdated or less relevant due to the rapidly evolving field

Code comparison

While both repositories primarily consist of curated lists rather than code, here's a comparison of how they structure their content:

awesome-machine-learning:

## Python

#### Computer Vision

* [SimpleCV](http://simplecv.org/) - An open source computer vision framework that gives access to several high-powered computer vision libraries, such as OpenCV. Written on Python and runs on Mac, Windows, and Ubuntu Linux.

awesome-deep-learning:

## Frameworks

* [Caffe](http://caffe.berkeleyvision.org/)  
* [Torch7](http://torch.ch/)
* [Theano](http://deeplearning.net/software/theano/)

Both repositories use markdown formatting, but awesome-machine-learning tends to provide more detailed descriptions for each resource, while awesome-deep-learning often lists items more concisely.

awesome-nlp

16,566

:book: A curated list of resources dedicated to Natural Language Processing (NLP)

Pros of awesome-nlp

More focused on Natural Language Processing, providing specialized resources
Includes sections on NLP-specific tasks like text classification and named entity recognition
Offers a curated list of NLP datasets and corpora

Cons of awesome-nlp

Less comprehensive coverage of general deep learning topics
Fewer resources on neural network architectures and optimization techniques
Limited information on deep learning frameworks and tools

Code comparison

While both repositories primarily consist of curated lists rather than code, here's a hypothetical comparison of how they might structure their content:

awesome-deep-learning:

## Neural Networks
- Feedforward Neural Networks
- Convolutional Neural Networks
- Recurrent Neural Networks

awesome-nlp:

## Text Classification
- Naive Bayes
- Support Vector Machines
- Deep Learning Approaches

Both repositories serve as valuable resources for their respective domains. awesome-deep-learning offers a broader overview of deep learning concepts, while awesome-nlp provides more specialized information for NLP practitioners. The choice between them depends on whether you're looking for general deep learning knowledge or specific NLP resources.

awesome-deep-learning-papers

25,439

The most cited deep learning papers

Pros of awesome-deep-learning-papers

Focuses specifically on research papers, providing a curated list of influential publications
Organizes papers by year, making it easy to track the evolution of deep learning research
Includes a "Top 100" section highlighting the most impactful papers

Cons of awesome-deep-learning-papers

Limited to research papers, lacking resources for practical implementation and tools
May be overwhelming for beginners due to its academic focus
Less frequently updated compared to awesome-deep-learning

Code comparison

While both repositories primarily consist of curated lists rather than code, awesome-deep-learning includes some code snippets for certain resources. For example:

awesome-deep-learning:

import tensorflow as tf
hello = tf.constant('Hello, TensorFlow!')
sess = tf.Session()
print(sess.run(hello))

awesome-deep-learning-papers does not include code snippets, focusing solely on paper references.

Both repositories use Markdown for organization and formatting, with awesome-deep-learning-papers utilizing more detailed categorization:

## 2012
- Alexnet [pdf] [code]
- Dropout [pdf]

awesome-python

221,658

An opinionated list of awesome Python frameworks, libraries, software and resources.

Pros of awesome-python

Broader scope, covering the entire Python ecosystem
More comprehensive, with a larger number of curated resources
Regularly updated with new libraries and tools

Cons of awesome-python

Less focused on a specific domain, which may be overwhelming for beginners
Requires more time to navigate and find relevant resources for specific tasks

Code comparison

While both repositories are primarily curated lists, awesome-python includes some code snippets for certain libraries. For example:

# awesome-python example (requests library)
import requests
r = requests.get('https://api.github.com/user', auth=('user', 'pass'))
print(r.status_code)
print(r.headers['content-type'])

awesome-deep-learning doesn't typically include code snippets, focusing instead on categorized links to resources.

Summary

awesome-python is a more comprehensive resource for the entire Python ecosystem, while awesome-deep-learning is focused specifically on deep learning. The former offers a wider range of topics and resources, making it suitable for general Python developers, while the latter is more targeted towards those interested in deep learning specifically.

TensorFlow-Examples

43,354

TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)

Pros of TensorFlow-Examples

Provides hands-on, practical examples of TensorFlow implementations
Offers a wide range of machine learning and deep learning models
Includes both basic and advanced TensorFlow concepts

Cons of TensorFlow-Examples

Focuses solely on TensorFlow, limiting exposure to other frameworks
May not cover the latest deep learning research and techniques
Less comprehensive in terms of general deep learning resources

Code Comparison

TensorFlow-Examples:

import tensorflow as tf

# Create a constant tensor
hello = tf.constant('Hello, TensorFlow!')

# Start a TensorFlow session
sess = tf.Session()

# Run the op
print(sess.run(hello))

Awesome-deep-learning doesn't provide code examples directly, as it's a curated list of resources.

Summary

TensorFlow-Examples is an excellent resource for practical, hands-on learning of TensorFlow, offering a wide range of examples and implementations. However, it's limited to TensorFlow and may not cover the breadth of deep learning topics found in Awesome-deep-learning.

Awesome-deep-learning, on the other hand, provides a comprehensive list of resources covering various deep learning frameworks, research papers, and tutorials. It's an excellent starting point for exploring the field but lacks the direct, practical examples found in TensorFlow-Examples.

Choose TensorFlow-Examples for focused TensorFlow learning, or Awesome-deep-learning for a broader understanding of deep learning concepts and resources.

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README

ï»¿# Awesome Deep Learning

Books
Courses
Videos and Lectures
Papers
Tutorials
Researchers
Websites
Datasets
Conferences
Frameworks
Tools
Miscellaneous
Contributing

Books

Deep Learning by Yoshua Bengio, Ian Goodfellow and Aaron Courville (05/07/2015)
Neural Networks and Deep Learning by Michael Nielsen (Dec 2014)
Deep Learning by Microsoft Research (2013)
Deep Learning Tutorial by LISA lab, University of Montreal (Jan 6 2015)
neuraltalk by Andrej Karpathy : numpy-based RNN/LSTM implementation
An introduction to genetic algorithms
Artificial Intelligence: A Modern Approach
Deep Learning in Neural Networks: An Overview
Artificial intelligence and machine learning: Topic wise explanation
Grokking Deep Learning for Computer Vision
Dive into Deep Learning - numpy based interactive Deep Learning book
Practical Deep Learning for Cloud, Mobile, and Edge - A book for optimization techniques during production.
Math and Architectures of Deep Learning - by Krishnendu Chaudhury
TensorFlow 2.0 in Action - by Thushan Ganegedara
Deep Learning for Natural Language Processing - by Stephan Raaijmakers
Deep Learning Patterns and Practices - by Andrew Ferlitsch
Inside Deep Learning - by Edward Raff
Deep Learning with Python, Second Edition - by FranÃ§ois Chollet
Evolutionary Deep Learning - by Micheal Lanham
Engineering Deep Learning Platforms - by Chi Wang and Donald Szeto
Deep Learning with R, Second Edition - by FranÃ§ois Chollet with Tomasz Kalinowski and J. J. Allaire
Regularization in Deep Learning - by Liu Peng
Jax in Action - by Grigory Sapunov

Courses

Machine Learning - Stanford by Andrew Ng in Coursera (2010-2014)
Machine Learning - Caltech by Yaser Abu-Mostafa (2012-2014)
Machine Learning - Carnegie Mellon by Tom Mitchell (Spring 2011)
Neural Networks for Machine Learning by Geoffrey Hinton in Coursera (2012)
Deep Learning Course by CILVR lab @ NYU (2014)
A.I - Berkeley by Dan Klein and Pieter Abbeel (2013)
A.I - MIT by Patrick Henry Winston (2010)
Vision and learning - computers and brains by Shimon Ullman, Tomaso Poggio, Ethan Meyers @ MIT (2013)
Convolutional Neural Networks for Visual Recognition - Stanford by Fei-Fei Li, Andrej Karpathy (2017)
Deep Learning for Natural Language Processing - Stanford
Neural Networks - usherbrooke
Machine Learning - Oxford (2014-2015)
Deep Learning - Nvidia (2015)
Graduate Summer School: Deep Learning, Feature Learning by Geoffrey Hinton, Yoshua Bengio, Yann LeCun, Andrew Ng, Nando de Freitas and several others @ IPAM, UCLA (2012)
Deep Learning - Udacity/Google by Vincent Vanhoucke and Arpan Chakraborty (2016)
Deep Learning - UWaterloo by Prof. Ali Ghodsi at University of Waterloo (2015)
Statistical Machine Learning - CMU by Prof. Larry Wasserman
Deep Learning Course by Yann LeCun (2016)
Designing, Visualizing and Understanding Deep Neural Networks-UC Berkeley
UVA Deep Learning Course MSc in Artificial Intelligence for the University of Amsterdam.
MIT 6.S094: Deep Learning for Self-Driving Cars
MIT 6.S191: Introduction to Deep Learning
Berkeley CS 294: Deep Reinforcement Learning
Keras in Motion video course
Practical Deep Learning For Coders by Jeremy Howard - Fast.ai
Introduction to Deep Learning by Prof. Bhiksha Raj (2017)
AI for Everyone by Andrew Ng (2019)
MIT Intro to Deep Learning 7 day bootcamp - A seven day bootcamp designed in MIT to introduce deep learning methods and applications (2019)
Deep Blueberry: Deep Learning - A free five-weekend plan to self-learners to learn the basics of deep-learning architectures like CNNs, LSTMs, RNNs, VAEs, GANs, DQN, A3C and more (2019)
Spinning Up in Deep Reinforcement Learning - A free deep reinforcement learning course by OpenAI (2019)
Deep Learning Specialization - Coursera - Breaking into AI with the best course from Andrew NG.
Deep Learning - UC Berkeley | STAT-157 by Alex Smola and Mu Li (2019)
Machine Learning for Mere Mortals video course by Nick Chase
Machine Learning Crash Course with TensorFlow APIs -Google AI
Deep Learning from the Foundations Jeremy Howard - Fast.ai
Deep Reinforcement Learning (nanodegree) - Udacity a 3-6 month Udacity nanodegree, spanning multiple courses (2018)
Grokking Deep Learning in Motion by Beau Carnes (2018)
Face Detection with Computer Vision and Deep Learning by Hakan Cebeci
Deep Learning Online Course list at Classpert List of Deep Learning online courses (some are free) from Classpert Online Course Search
AWS Machine Learning Machine Learning and Deep Learning Courses from Amazon's Machine Learning university
Intro to Deep Learning with PyTorch - A great introductory course on Deep Learning by Udacity and Facebook AI
Deep Learning by Kaggle - Kaggle's free course on Deep Learning
Yann LeCunâs Deep Learning Course at CDS - DS-GA 1008 Â· SPRING 2021
Neural Networks and Deep Learning - COMP9444 19T3
Deep Learning A.I.Shelf

Videos and Lectures

How To Create A Mind By Ray Kurzweil
Deep Learning, Self-Taught Learning and Unsupervised Feature Learning By Andrew Ng
Recent Developments in Deep Learning By Geoff Hinton
The Unreasonable Effectiveness of Deep Learning by Yann LeCun
Deep Learning of Representations by Yoshua bengio
Principles of Hierarchical Temporal Memory by Jeff Hawkins
Machine Learning Discussion Group - Deep Learning w/ Stanford AI Lab by Adam Coates
Making Sense of the World with Deep Learning By Adam Coates
Demystifying Unsupervised Feature Learning By Adam Coates
Visual Perception with Deep Learning By Yann LeCun
The Next Generation of Neural Networks By Geoffrey Hinton at GoogleTechTalks
The wonderful and terrifying implications of computers that can learn By Jeremy Howard at TEDxBrussels
Unsupervised Deep Learning - Stanford by Andrew Ng in Stanford (2011)
Natural Language Processing By Chris Manning in Stanford
A beginners Guide to Deep Neural Networks By Natalie Hammel and Lorraine Yurshansky
Deep Learning: Intelligence from Big Data by Steve Jurvetson (and panel) at VLAB in Stanford.
Introduction to Artificial Neural Networks and Deep Learning by Leo Isikdogan at Motorola Mobility HQ
NIPS 2016 lecture and workshop videos - NIPS 2016
Deep Learning Crash Course: a series of mini-lectures by Leo Isikdogan on YouTube (2018)
Deep Learning Crash Course By Oliver Zeigermann
Deep Learning with R in Motion: a live video course that teaches how to apply deep learning to text and images using the powerful Keras library and its R language interface.
Medical Imaging with Deep Learning Tutorial: This tutorial is styled as a graduate lecture about medical imaging with deep learning. This will cover the background of popular medical image domains (chest X-ray and histology) as well as methods to tackle multi-modality/view, segmentation, and counting tasks.
Deepmind x UCL Deeplearning: 2020 version
Deepmind x UCL Reinforcement Learning: Deep Reinforcement Learning
CMU 11-785 Intro to Deep learning Spring 2020 Course: 11-785, Intro to Deep Learning by Bhiksha Raj
Machine Learning CS 229 : End part focuses on deep learning By Andrew Ng
What is Neural Structured Learning by Andrew Ferlitsch
Deep Learning Design Patterns by Andrew Ferlitsch
Architecture of a Modern CNN: the design pattern approach by Andrew Ferlitsch
Metaparameters in a CNN by Andrew Ferlitsch
Multi-task CNN: a real-world example by Andrew Ferlitsch
A friendly introduction to deep reinforcement learning by Luis Serrano
What are GANs and how do they work? by Edward Raff
Coding a basic WGAN in PyTorch by Edward Raff
Training a Reinforcement Learning Agent by Miguel Morales
Understand what is Deep Learning

Papers

You can also find the most cited deep learning papers from here

Tutorials

Researchers

Websites

Datasets

MNIST Handwritten digits
Google House Numbers from street view
CIFAR-10 and CIFAR-100
IMAGENET
Tiny Images 80 Million tiny images6.
Flickr Data 100 Million Yahoo dataset
Berkeley Segmentation Dataset 500
UC Irvine Machine Learning Repository
Flickr 8k
Flickr 30k
Microsoft COCO
VQA
Image QA
AT&T Laboratories Cambridge face database
AVHRR Pathfinder
Air Freight - The Air Freight data set is a ray-traced image sequence along with ground truth segmentation based on textural characteristics. (455 images + GT, each 160x120 pixels). (Formats: PNG)
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)
CAVIAR video sequences of mall and public space behavior - 90K video frames in 90 sequences of various human activities, with XML ground truth of detection and behavior classification (Formats: MPEG2 & JPEG)
Machine Vision Unit
CCITT Fax standard images - 8 images (Formats: gif)
CMU CIL's Stereo Data with Ground Truth - 3 sets of 11 images, including color tiff images with spectroradiometry (Formats: gif, tiff)
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.
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
Content-based image retrieval database - 11 sets of color images for testing algorithms for content-based retrieval. Most sets have a description file with names of objects in each image. (Formats: jpg)
Efficient Content-based Retrieval Group
Densely Sampled View Spheres - Densely sampled view spheres - upper half of the view sphere of two toy objects with 2500 images each. (Formats: tiff)
Computer Science VII (Graphical Systems)
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
El Salvador Atlas of Gastrointestinal VideoEndoscopy - Images and Videos of his-res of studies taken from Gastrointestinal Video endoscopy. (Formats: jpg, mpg, gif)
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
Face and Gesture images and image sequences - Several image datasets of faces and gestures that are ground truth annotated for benchmarking
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 different viewing heights, 36 images each, resolution 1000x750, color (Formats: PPM)
Institute of Computer Graphics and Vision
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
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 Analysis Laboratory
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
JISCT Stereo Evaluation - 44 image pairs. These data have been used in an evaluation of stereo analysis, as described in the April 1993 ARPA Image Understanding Workshop paper ``The JISCT Stereo Evaluation'' by R.C.Bolles, H.H.Baker, and M.J.Hannah, 263--274 (Formats: SSI)
MIT Vision Texture - Image archive (100+ images) (Formats: ppm)
MIT face images and more - hundreds of images (Formats: homebrew)
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)
ftp://ftp.cps.msu.edu/pub/prip - many images (Formats: unknown)
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)
NIST Fingerprint and handwriting - datasets - thousands of images (Formats: unknown)
NIST Fingerprint data - compressed multipart uuencoded tar file
NLM HyperDoc Visible Human Project - Color, CAT and MRI image samples - over 30 images (Formats: jpeg)
National Design Repository - Over 55,000 3D CAD and solid models of (mostly) mechanical/machined engineering designs. (Formats: gif,vrml,wrl,stp,sat)
Geometric & Intelligent Computing Laboratory
OSU (MSU) 3D Object Model Database - several sets of 3D object models collected over several years to use in object recognition research (Formats: homebrew, vrml)
OSU (MSU/WSU) Range Image Database - Hundreds of real and synthetic images (Formats: gif, homebrew)
OSU/SAMPL Database: Range Images, 3D Models, Stills, Motion Sequences - Over 1000 range images, 3D object models, still images and motion sequences (Formats: gif, ppm, vrml, homebrew)
Signal Analysis and Machine Perception 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)
Vision Research Group
ftp://ftp.limsi.fr/pub/quenot/opflow/testdata/piv/ - Real and synthetic image sequences used for testing a Particle Image Velocimetry application. These images may be used for the test of optical flow and image matching algorithms. (Formats: pgm (raw))
LIMSI-CNRS/CHM/IMM/vision
LIMSI-CNRS
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
Sequences for Flow Based Reconstruction - synthetic sequence for testing structure from motion algorithms (Formats: pgm)
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)
Department Image Understanding
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))
Purdue Robot Vision Lab
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 Bern Face images - hundreds of images (Formats: Sun rasterfile)
U Michigan textures (Formats: compressed raw)
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)
UNC's 3D image database - many images (Formats: GIF)
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
Usenix face database - Thousands of face images from many different sites (circa 994)
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
Vision-list Imagery Archive - Many images, many formats
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
DeepMind QA Corpus - Textual QA corpus from CNN and DailyMail. More than 300K documents in total. Paper for reference.
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.
Open Images dataset - Open Images is a dataset of ~9 million URLs to images that have been annotated with labels spanning over 6000 categories.
Visual Object Classes Challenge 2012 (VOC2012) - VOC2012 dataset containing 12k images with 20 annotated classes for object detection and segmentation.
Fashion-MNIST - MNIST like fashion product dataset consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grayscale image, associated with a label from 10 classes.
Large-scale Fashion (DeepFashion) Database - Contains over 800,000 diverse fashion images. Each image in this dataset is labeled with 50 categories, 1,000 descriptive attributes, bounding box and clothing landmarks
FakeNewsCorpus - Contains about 10 million news articles classified using opensources.co types
LLVIP - 15488 visible-infrared paired images (30976 images) for low-light vision research, Project_Page
MSDA - Over over 5 million images from 5 different domains for multi-source ocr/text recognition DA research, Project_Page
SANAD: Single-Label Arabic News Articles Dataset for Automatic Text Categorization - SANAD Dataset is a large collection of Arabic news articles that can be used in different Arabic NLP tasks such as Text Classification and Word Embedding. The articles were collected using Python scripts written specifically for three popular news websites: AlKhaleej, AlArabiya and Akhbarona.
Referit3D - Two large-scale and complementary visio-linguistic datasets (aka Nr3D and Sr3D) for identifying fine-grained 3D objects in ScanNet scenes. Nr3D contains 41.5K natural, free-form utterances, and Sr3d contains 83.5K template-based utterances.
SQuAD - Stanford released ~100,000 English QA pairs and ~50,000 unanswerable questions
FQuAD - ~25,000 French QA pairs released by Illuin Technology
GermanQuAD and GermanDPR - deepset released ~14,000 German QA pairs
SberQuAD - Sberbank released ~90,000 Russian QA pairs
ArtEmis - Contains 450K affective annotations of emotional responses and linguistic explanations for 80,000 artworks of WikiArt.

Conferences

Frameworks

Tools

Nebullvm - Easy-to-use library to boost deep learning inference leveraging multiple deep learning compilers.
Netron - Visualizer for deep learning and machine learning models
Jupyter Notebook - Web-based notebook environment for interactive computing
TensorBoard - TensorFlow's Visualization Toolkit
Visual Studio Tools for AI - Develop, debug and deploy deep learning and AI solutions
TensorWatch - Debugging and visualization for deep learning
ML Workspace - All-in-one web-based IDE for machine learning and data science.
dowel - A little logger for machine learning research. Log any object to the console, CSVs, TensorBoard, text log files, and more with just one call to logger.log()
Neptune - Lightweight tool for experiment tracking and results visualization.
CatalyzeX - Browser extension (Chrome and Firefox) that automatically finds and links to code implementations for ML papers anywhere online: Google, Twitter, Arxiv, Scholar, etc.
Determined - Deep learning training platform with integrated support for distributed training, hyperparameter tuning, smart GPU scheduling, experiment tracking, and a model registry.
DAGsHub - Community platform for Open Source ML â Manage experiments, data & models and create collaborative ML projects easily.
hub - Fastest unstructured dataset management for TensorFlow/PyTorch by activeloop.ai. Stream & version-control data. Converts large data into single numpy-like array on the cloud, accessible on any machine.
DVC - DVC is built to make ML models shareable and reproducible. It is designed to handle large files, data sets, machine learning models, and metrics as well as code.
CML - CML helps you bring your favorite DevOps tools to machine learning.
MLEM - MLEM is a tool to easily package, deploy and serve Machine Learning models. It seamlessly supports a variety of scenarios like real-time serving and batch processing.

Miscellaneous

Caffe Webinar
100 Best Github Resources in Github for DL
Word2Vec
Caffe DockerFile
TorontoDeepLEarning convnet
gfx.js
Torch7 Cheat sheet
Misc from MIT's 'Advanced Natural Language Processing' course
Misc from MIT's 'Machine Learning' course
Misc from MIT's 'Networks for Learning: Regression and Classification' course
Misc from MIT's 'Neural Coding and Perception of Sound' course
Implementing a Distributed Deep Learning Network over Spark
A chess AI that learns to play chess using deep learning.
Reproducing the results of "Playing Atari with Deep Reinforcement Learning" by DeepMind
Wiki2Vec. Getting Word2vec vectors for entities and word from Wikipedia Dumps
The original code from the DeepMind article + tweaks
Google deepdream - Neural Network art
An efficient, batched LSTM.
A recurrent neural network designed to generate classical music.
Memory Networks Implementations - Facebook
Face recognition with Google's FaceNet deep neural network.
Basic digit recognition neural network
Emotion Recognition API Demo - Microsoft
Proof of concept for loading Caffe models in TensorFlow
YOLO: Real-Time Object Detection
YOLO: Practical Implementation using Python
AlphaGo - A replication of DeepMind's 2016 Nature publication, "Mastering the game of Go with deep neural networks and tree search"
Machine Learning for Software Engineers
Machine Learning is Fun!
Siraj Raval's Deep Learning tutorials
Dockerface - Easy to install and use deep learning Faster R-CNN face detection for images and video in a docker container.
Awesome Deep Learning Music - Curated list of articles related to deep learning scientific research applied to music
Awesome Graph Embedding - Curated list of articles related to deep learning scientific research on graph structured data at the graph level.
Awesome Network Embedding - Curated list of articles related to deep learning scientific research on graph structured data at the node level.
Microsoft Recommenders contains examples, utilities and best practices for building recommendation systems. Implementations of several state-of-the-art algorithms are provided for self-study and customization in your own applications.
The Unreasonable Effectiveness of Recurrent Neural Networks - Andrej Karpathy blog post about using RNN for generating text.
Ladder Network - Keras Implementation of Ladder Network for Semi-Supervised Learning
toolbox: Curated list of ML libraries
CNN Explainer
AI Expert Roadmap - Roadmap to becoming an Artificial Intelligence Expert
Awesome Drug Interactions, Synergy, and Polypharmacy Prediction

Contributing

Have anything in mind that you think is awesome and would fit in this list? Feel free to send a pull request.

License

To the extent possible under law, Christos Christofidis has waived all copyright and related or neighboring rights to this work.

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