深度学习

Awesome Deep Learning Awesome

Books

  1. Deep Learning 通过Yoshua Bengio,Ian Goodfellow和Aaron Courville(05/07/2015)
  2. Neural Networks and Deep Learning 由Michael Nielsen(2014年12月)
  3. Deep Learning 由Microsoft Research(2013)
  4. Deep Learning Tutorial 蒙特利尔大学LISA实验室(2015年1月6日)
  5. neuraltalk 由Andrej Karpathy撰写:基于numpy的RNN / LSTM实现
  6. An introduction to genetic algorithms
  7. Artificial Intelligence: A Modern Approach
  8. Deep Learning in Neural Networks: An Overview
  9. Artificial intelligence and machine learning: Topic wise explanation 10.Grokking Deep Learning for Computer Vision
  10. Dive into Deep Learning -基于numpy的交互式深度学习书
  11. Practical Deep Learning for Cloud, Mobile, and Edge -有关生产过程中的优化技术的书.
  12. Math and Architectures of Deep Learning -作者:Krishnendu Chaudhury
  13. TensorFlow 2.0 in Action -苏珊·加内加达拉

Courses

  1. Machine Learning - Stanford 吴安德(Andrew Ng)在Coursera(2010-2014)
  2. Machine Learning - Caltech 由Yaser Abu-Mostafa(2012-2014)
  3. Machine Learning - Carnegie Mellon 作者:汤姆·米切尔(Tom Mitchell)(2011年春季)
  4. Neural Networks for Machine Learning 由杰弗里·欣顿(Geoffrey Hinton)在Coursera(2012)中
  5. Neural networks class 作者:舍布鲁克大学的雨果·拉罗谢尔(Hugo Larochelle)(2013年)
  6. Deep Learning Course 通过CILVR实验室@纽约大学(2014)
  7. A.I - Berkeley 丹·克莱恩(Dan Klein)和彼得阿比尔(Pieter Abbeel)(2013)
  8. A.I - MIT 帕特里克·亨利·温斯顿(2010)
  9. Vision and learning - computers and brains 由MIT的Shimon Ullman,Tomaso Poggio,Ethan Meyers @麻省理工学院(2013)
  10. Convolutional Neural Networks for Visual Recognition - Stanford by Fei-Fei Li, Andrej Karpathy (2017)
  11. Deep Learning for Natural Language Processing - Stanford
  12. Neural Networks - usherbrooke
  13. Machine Learning - Oxford (2014-2015)
  14. Deep Learning - Nvidia (2015)
  15. Graduate Summer School: Deep Learning, Feature Learning 杰弗里·欣顿(Geoffrey Hinton),约书亚(Yoshua Bengio),扬·勒库恩(Yann LeCun),吴安德(Andrew Ng),南多·德·弗雷塔斯(Nando de Freitas)等@ IPAM,UCLA(2012)
  16. Deep Learning - Udacity/Google Vincent Vanhoucke和Arpan Chakraborty(2016)
  17. Deep Learning - UWaterloo 由滑铁卢大学的Ali Ghodsi教授撰写(2015年)
  18. Statistical Machine Learning - CMU 拉里·瓦瑟曼教授
  19. Deep Learning Course Yann LeCun(2016年)
  20. Designing, Visualizing and Understanding Deep Neural Networks-UC Berkeley
  21. UVA Deep Learning Course 阿姆斯特丹大学人工智能硕士.
  22. MIT 6.S094: Deep Learning for Self-Driving Cars
  23. MIT 6.S191: Introduction to Deep Learning
  24. Berkeley CS 294: Deep Reinforcement Learning
  25. Keras in Motion video course
  26. Practical Deep Learning For Coders 杰里米·霍华德(Jeremy Howard)撰写的文章-Fast.ai
  27. Introduction to Deep Learning 由教授比克莎·拉吉(2017)
  28. AI for Everyone 吴恩达(2019)
  29. MIT Intro to Deep Learning 7 day bootcamp -在麻省理工学院设计的为期7天的训练营,旨在介绍深度学习方法和应用(2019)
  30. Deep Blueberry: Deep Learning -免费的五周计划,供自学者学习深度学习架构的基础,例如CNN,LSTM,RNN,VAE,GAN,DQN,A3C等(2019)
  31. Spinning Up in Deep Reinforcement Learning -OpenAI提供的免费深度强化学习课程(2019)
  32. Deep Learning Specialization - Coursera -通过NG Andrew的最佳课程进入AI.
  33. Deep Learning - UC Berkeley | STAT-157 Alex Smola和Mu Li(2019)
  34. Machine Learning for Mere Mortals video course by Nick Chase
  35. Machine Learning Crash Course with TensorFlow APIs -Google AI
  36. Deep Learning from the Foundations 杰里米·霍华德-Fast.ai
  37. Deep Reinforcement Learning (nanodegree) - Udacity 3至6个月的Udacity纳米学位,涵盖多个课程(2018)
  38. Grokking Deep Learning in Motion 博卡恩斯(2018)
  39. Face Detection with Computer Vision and Deep Learning 由Hakan Cebeci
  40. Deep Learning Online Course list at Classpert Classpert在线课程搜索中的深度学习在线课程列表(有些是免费的)
  41. AWS Machine Learning 来自Amazon的机器学习狂热的机器学习和深度学习课程
  42. Intro to Deep Learning with PyTorch -Udacity和Facebook AI关于深度学习的精彩入门课程
  43. Deep Learning by Kaggle -Kaggle的深度学习免费课程

Videos and Lectures

  1. How To Create A Mind 雷·库兹韦尔(Ray Kurzweil)
  2. Deep Learning, Self-Taught Learning and Unsupervised Feature Learning 吴安德
  3. Recent Developments in Deep Learning 杰夫·欣顿(Geoff Hinton)
  4. The Unreasonable Effectiveness of Deep Learning 由Yann LeCun
  5. Deep Learning of Representations 由Yoshua Bengio
  6. Principles of Hierarchical Temporal Memory 杰夫·霍金斯(Jeff Hawkins)
  7. Machine Learning Discussion Group - Deep Learning w/ Stanford AI Lab 通过亚当科茨
  8. Making Sense of the World with Deep Learning By Adam Coates
  9. Demystifying Unsupervised Feature Learning By Adam Coates
  10. Visual Perception with Deep Learning 扬·勒村(Yann LeCun)
  11. The Next Generation of Neural Networks 作者:Geoffrey Hinton在GoogleTechTalks上
  12. The wonderful and terrifying implications of computers that can learn TEDx布鲁塞尔的杰里米·霍华德(Jeremy Howard)
  13. Unsupervised Deep Learning - Stanford 吴安德(Andrew Ng)在斯坦福大学(2011)
  14. Natural Language Processing 斯坦福大学的克里斯·曼宁
  15. A beginners Guide to Deep Neural Networks 娜塔莉·哈默尔(Natalie Hammel)和洛林·尤申斯基(Lorraine Yurshansky)
  16. Deep Learning: Intelligence from Big Data 由斯坦福大学VLAB的Steve Jurvetson(和小组)撰写.
  17. Introduction to Artificial Neural Networks and Deep Learning 摩托罗拉移动总部的Leo Isikdogan
  18. NIPS 2016 lecture and workshop videos -NIPS 2016
  19. Deep Learning Crash Course:Leo Isikdogan在YouTube上举办的一系列微型讲座(2018)
  20. Deep Learning Crash Course 奥利弗·齐格曼(Oliver Zeigermann)
  21. Deep Learning with R in Motion:现场视频课程,教您如何使用功能强大的Keras库及其R语言界面将深度学习应用于文本和图像.
  22. Medical Imaging with Deep Learning Tutorial :本教程的风格是关于具有深度学习的医学成像的研究生讲座. 这将涵盖流行的医学图像领域(胸部X射线和组织学)的背景,以及解决多模式/视图,分割和计数任务的方法.
  23. Deepmind x UCL Deeplearning:2020版本
  24. Deepmind x UCL Reinforcement Learning:深度强化学习
  25. CMU 11-785 Intro to Deep learning Spring 2020 课程:11-785,Bhiksha Raj撰写的深度学习入门
  26. Machine Learning CS 229 :最终部分着重于深度学习作者:Andrew Ng

Papers

您还可以从以下位置找到引用最多的深度学习论文 here

  1. ImageNet Classification with Deep Convolutional Neural Networks
  2. Using Very Deep Autoencoders for Content Based Image Retrieval
  3. Learning Deep Architectures for AI
  4. CMU’s list of papers
  5. Neural Networks for Named Entity Recognition zip
  6. Training tricks by YB
  7. Geoff Hinton's reading list (all papers)
  8. Supervised Sequence Labelling with Recurrent Neural Networks
  9. Statistical Language Models based on Neural Networks
  10. Training Recurrent Neural Networks
  11. Recursive Deep Learning for Natural Language Processing and Computer Vision
  12. Bi-directional RNN
  13. LSTM
  14. GRU - Gated Recurrent Unit
  15. GFRNN . .
  16. LSTM: A Search Space Odyssey
  17. A Critical Review of Recurrent Neural Networks for Sequence Learning
  18. Visualizing and Understanding Recurrent Networks
  19. Wojciech Zaremba, Ilya Sutskever, An Empirical Exploration of Recurrent Network Architectures
  20. Recurrent Neural Network based Language Model
  21. Extensions of Recurrent Neural Network Language Model
  22. Recurrent Neural Network based Language Modeling in Meeting Recognition
  23. Deep Neural Networks for Acoustic Modeling in Speech Recognition
  24. Speech Recognition with Deep Recurrent Neural Networks
  25. Reinforcement Learning Neural Turing Machines
  26. Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
  27. Google - Sequence to Sequence Learning with Neural Networks
  28. Memory Networks
  29. Policy Learning with Continuous Memory States for Partially Observed Robotic Control
  30. Microsoft - Jointly Modeling Embedding and Translation to Bridge Video and Language
  31. Neural Turing Machines
  32. Ask Me Anything: Dynamic Memory Networks for Natural Language Processing
  33. Mastering the Game of Go with Deep Neural Networks and Tree Search
  34. Batch Normalization
  35. Residual Learning
  36. Image-to-Image Translation with Conditional Adversarial Networks
  37. Berkeley AI Research (BAIR) Laboratory
  38. MobileNets by Google
  39. Cross Audio-Visual Recognition in the Wild Using Deep Learning
  40. Dynamic Routing Between Capsules
  41. Matrix Capsules With Em Routing
  42. Efficient BackProp
  43. Generative Adversarial Nets
  44. Fast R-CNN
  45. FaceNet: A Unified Embedding for Face Recognition and Clustering
  46. Siamese Neural Networks for One-shot Image Recognition
  47. Unsupervised Translation of Programming Languages
  48. Matching Networks for One Shot Learning

Tutorials

  1. UFLDL Tutorial 1
  2. UFLDL Tutorial 2
  3. Deep Learning for NLP (without Magic)
  4. A Deep Learning Tutorial: From Perceptrons to Deep Networks
  5. Deep Learning from the Bottom up
  6. Theano Tutorial
  7. Neural Networks for Matlab
  8. Using convolutional neural nets to detect facial keypoints tutorial
  9. Torch7 Tutorials
  10. The Best Machine Learning Tutorials On The Web
  11. VGG Convolutional Neural Networks Practical
  12. TensorFlow tutorials
  13. More TensorFlow tutorials
  14. TensorFlow Python Notebooks
  15. Keras and Lasagne Deep Learning Tutorials
  16. Classification on raw time series in TensorFlow with a LSTM RNN
  17. Using convolutional neural nets to detect facial keypoints tutorial
  18. TensorFlow-World
  19. Deep Learning with Python
  20. Grokking Deep Learning
  21. Deep Learning for Search
  22. Keras Tutorial: Content Based Image Retrieval Using a Convolutional Denoising Autoencoder
  23. Pytorch Tutorial by Yunjey Choi
  24. Understanding deep Convolutional Neural Networks with a practical use-case in Tensorflow and Keras
  25. Overview and benchmark of traditional and deep learning models in text classification
  26. Hardware for AI: Understanding computer hardware & build your own computer
  27. Programming Community Curated Resources
  28. The Illustrated Self-Supervised Learning
  29. Visual Paper Summary: ALBERT (A Lite BERT)
  30. Semi-Supervised Deep Learning with GANs for Melanoma Detection
  31. Named Entity Recognition using Reformers
  32. Deep N-Gram Models on Shakespeare’s works
  33. Wide Residual Networks
  34. Fashion MNIST using Flax
  35. Fake News Classification (with streamlit deployment)
  36. Regression Analysis for Primary Biliary Cirrhosis
  37. Cross Matching Methods for Astronomical Catalogs
  38. Named Entity Recognition using BiDirectional LSTMs
  39. Image Recognition App using Tflite and Flutter

Researchers

  1. Aaron Courville
  2. Abdel-rahman Mohamed
  3. Adam Coates
  4. Alex Acero
  5. Alex Krizhevsky
  6. Alexander Ilin
  7. Amos Storkey
  8. Andrej Karpathy
  9. Andrew M. Saxe
  10. Andrew Ng
  11. Andrew W. Senior
  12. Andriy Mnih
  13. Ayse Naz Erkan
  14. Benjamin Schrauwen
  15. Bernardete Ribeiro
  16. Bo David Chen
  17. Boureau Y-Lan
  18. Brian Kingsbury
  19. Christopher Manning
  20. Clement Farabet
  21. Dan Claudiu Cireșan
  22. David Reichert
  23. Derek Rose
  24. Dong Yu
  25. Drausin Wulsin
  26. Erik M. Schmidt
  27. Eugenio Culurciello
  28. Frank Seide
  29. Galen Andrew
  30. Geoffrey Hinton
  31. George Dahl
  32. Graham Taylor
  33. Grégoire Montavon
  34. Guido Francisco Montúfar
  35. Guillaume Desjardins
  36. Hannes Schulz
  37. Hélène Paugam-Moisy
  38. Honglak Lee
  39. Hugo Larochelle
  40. Ilya Sutskever
  41. Itamar Arel
  42. James Martens
  43. Jason Morton
  44. Jason Weston
  45. Jeff Dean
  46. Jiquan Mgiam
  47. Joseph Turian
  48. Joshua Matthew Susskind
  49. Jürgen Schmidhuber
  50. Justin A. Blanco
  51. Koray Kavukcuoglu
  52. KyungHyun Cho
  53. Li Deng
  54. Lucas Theis
  55. Ludovic Arnold
  56. Marc'Aurelio Ranzato
  57. Martin Längkvist
  58. Misha Denil
  59. Mohammad Norouzi
  60. Nando de Freitas
  61. Navdeep Jaitly
  62. Nicolas Le Roux
  63. Nitish Srivastava
  64. Noel Lopes
  65. Oriol Vinyals
  66. Pascal Vincent
  67. Patrick Nguyen
  68. Pedro Domingos
  69. Peggy Series
  70. Pierre Sermanet
  71. Piotr Mirowski
  72. Quoc V. Le
  73. Reinhold Scherer
  74. Richard Socher
  75. Rob Fergus
  76. Robert Coop
  77. Robert Gens
  78. Roger Grosse
  79. Ronan Collobert
  80. Ruslan Salakhutdinov
  81. Sebastian Gerwinn
  82. Stéphane Mallat
  83. Sven Behnke
  84. Tapani Raiko
  85. Tara Sainath
  86. Tijmen Tieleman
  87. Tom Karnowski
  88. Tomáš Mikolov
  89. Ueli Meier
  90. Vincent Vanhoucke
  91. Volodymyr Mnih
  92. Yann LeCun
  93. Yichuan Tang
  94. Yoshua Bengio
  95. Yotaro Kubo
  96. Youzhi (Will) Zou
  97. Fei-Fei Li
  98. Ian Goodfellow
  99. Robert Laganière
  100. Merve Ayyüce Kızrak

Websites

  1. deeplearning.net
  2. deeplearning.stanford.edu
  3. nlp.stanford.edu
  4. ai-junkie.com
  5. cs.brown.edu/research/ai
  6. eecs.umich.edu/ai
  7. cs.utexas.edu/users/ai-lab
  8. cs.washington.edu/research/ai
  9. aiai.ed.ac.uk
  10. www-aig.jpl.nasa.gov
  11. csail.mit.edu
  12. cgi.cse.unsw.edu.au/~aishare
  13. cs.rochester.edu/research/ai
  14. ai.sri.com
  15. isi.edu/AI/isd.htm
  16. nrl.navy.mil/itd/aic
  17. hips.seas.harvard.edu
  18. AI Weekly
  19. stat.ucla.edu
  20. deeplearning.cs.toronto.edu
  21. jeffdonahue.com/lrcn/
  22. visualqa.org
  23. www.mpi-inf.mpg.de/departments/computer-vision...
  24. Deep Learning News
  25. Machine Learning is Fun! Adam Geitgey's Blog
  26. Guide to Machine Learning
  27. Deep Learning for Beginners
  28. Machine Learning Mastery blog
  29. ML Compiled
  30. Programming Community Curated Resources
  31. A Beginner's Guide To Understanding Convolutional Neural Networks
  32. ahmedbesbes.com
  33. amitness.com
  34. AI Summer
  35. AI Hub - supported by AAAI, NeurIPS
  36. CatalyzeX: Machine Learning Hub for Builders and Makers
  37. The Epic Code

Datasets

  1. MNIST 手写数字
  2. Google House Numbers 从街景
  3. CIFAR-10 and CIFAR-100
  4. IMAGENET
  5. Tiny Images 8000万个小图像6.
  6. Flickr Data 1亿Yahoo数据集
  7. Berkeley Segmentation Dataset 500
  8. UC Irvine Machine Learning Repository
  9. Flickr 8k
  10. Flickr 30k
  11. Microsoft COCO
  12. VQA
  13. Image QA
  14. AT&T Laboratories Cambridge face database
  15. AVHRR Pathfinder
  16. Air Freight -空运数据集是光线追踪的图像序列,以及基于纹理特征的地面真相分割. (455张图片+ GT,每张160x120像素). (格式:PNG)
  17. Amsterdam Library of Object Images -ALOI是一千个小物体的彩色图像集合,出于科学目的而记录. 为了捕获物体记录中的感官变化,我们系统地改变了每个物体的视角,照明角度和照明颜色,并另外捕获了宽基线立体图像. 我们记录了每个对象的一百多幅图像,总共收集了110,250张图像. (格式:png)
  18. Annotated face, hand, cardiac & meat images -使用AAM-API通过各种ASM / AAM分析补充了大多数图像和注释. (格式:bmp,asf)
  19. Image Analysis and Computer Graphics
  20. Brown University Stimuli -各种数据集,包括geons,对象和“ greebles”. 适合测试识别算法. (格式:pict)
  21. CAVIAR video sequences of mall and public space behavior -包含90种各种人类活动序列的90K视频帧,具有XML地面真相检测和行为分类(格式:MPEG2和JPEG)
  22. Machine Vision Unit
  23. CCITT Fax standard images -8张图像(格式:gif)
  24. CMU CIL's Stereo Data with Ground Truth -3组11张图像,包括具有分光光度法的彩色tiff图像(格式:gif,tiff)
  25. CMU PIE Database -在13种姿势,43种光照条件下,用4种不同表情拍摄的68368人的41368张面部图像数据库.
  26. CMU VASC Image Database -图像,序列,立体声对(数千个图像)(格式:Sun Rasterimage)
  27. Caltech Image Database -约20张图片-大多数是小物件和玩具的俯视图. (格式:GIF)
  28. Columbia-Utrecht Reflectance and Texture Database -对超过60种3D纹理样本的纹理和反射率测量,可以通过200多种观察和照明方向组合进行观察. (格式:bmp)
  29. Computational Colour Constancy Data -面向计算色彩恒定性的数据集,但通常对计算机视觉很有用. 它包括合成数据,相机传感器数据和700多个图像. (格式:tiff)
  30. Computational Vision Lab
  31. 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)
  32. Efficient Content-based Retrieval Group
  33. Densely Sampled View Spheres -密集采样的视角球-两个玩具对象的视角球的上半部分,每个都有2500张图像. (格式:tiff)
  34. Computer Science VII (Graphical Systems)
  35. Digital Embryos -数字胚胎是可用于开发和测试物体识别系统的新颖物体. 它们具有有机外观. (格式:可根据要求提供各种格式)
  36. Univerity of Minnesota Vision Lab
  37. El Salvador Atlas of Gastrointestinal VideoEndoscopy -从胃肠道视频内窥镜检查获得的研究结果的图像和视频. (格式:jpg,mpg,gif)
  38. FG-NET Facial Aging Database -数据库包含1002张面部图像,显示不同年龄的对象. (格式:jpg)
  39. FVC2000 Fingerprint Databases -FVC2000是首个国际指纹验证算法竞赛. 四个指纹数据库构成FVC2000基准(总共3520个指纹).
  40. Biometric Systems Lab -博洛尼亚大学
  41. Face and Gesture images and image sequences -几个面部和手势的图像数据集,标有基本事实,用于基准测试
  42. German Fingerspelling Database -数据库包含35个手势,并且由1400个图像序列组成,这些图像序列包含在不均匀的日光照明条件下记录的20个不同人物的手势. (格式:mpg,jpg)
  43. Language Processing and Pattern Recognition
  44. Groningen Natural Image Database -4000 + 1536x1024(16位)校准的室外图像(格式:自制)
  45. ICG Testhouse sequence -从不同的观看高度出发的2个转盘序列,每个序列36张图像,分辨率1000x750,彩色(格式:PPM)
  46. Institute of Computer Graphics and Vision
  47. IEN Image Library -1000幅以上的图像,主要是室外序列(格式:原始,ppm)
  48. INRIA's Syntim images database -简单对象的15彩色图像(格式:gif)
  49. INRIA
  50. INRIA's Syntim stereo databases -34个校准的彩色立体声对(格式:gif)
  51. Image Analysis Laboratory -从各种成像方式获得的图像-原始CFA图像,范围图像和大量“医学图像”. (格式:自制)
  52. Image Analysis Laboratory
  53. Image Database -包含一些纹理的图像数据库
  54. JAFFE Facial Expression Image Database -JAFFE数据库包含213张日本女性受试者的图像,这些图像构成了6种基本面部表情以及中性姿势. 出于研究目的,还免费提供情绪形容词的评级. (格式:TIFF灰度图像.)
  55. ATR Research, Kyoto, Japan
  56. JISCT Stereo Evaluation -44个图像对. 这些数据已用于立体分析的评估中,如RCBolles,HHBaker和MJHannah(263--274)在1993年4月的ARPA图像理解研讨会论文``JISCT立体声评估''中所述(格式:SSI)
  57. MIT Vision Texture -图片存档(超过100张图片)(格式:ppm)
  58. MIT face images and more -数百张图像(格式:自制)
  59. Machine Vision -Jain,Kasturi,Schunck教科书中的图片(超过20张图片)(格式:GIF TIFF)
  60. Mammography Image Databases -具有地面真相的100幅或更多乳房X线照片. 可根据要求提供其他图像,并提供指向其他几个乳房X线照相术数据库的链接. (格式:自制)
  61. https://github.com/ChristosChristofidis/awesome-deep-learning/blob/master/ftp://ftp.cps.msu.edu/pub/prip -许多图像(格式:未知)
  62. Middlebury Stereo Data Sets with Ground Truth -包含平面区域的场景的六个多帧立体数据集. 每个数据集包含9幅彩色图像和亚像素精度的地面数据. (格式:ppm)
  63. Middlebury Stereo Vision Research Page -米德伯里学院
  64. Modis Airborne simulator, Gallery and data set -来自世界各地的高海拔影像,用于环境建模,以支持NASA EOS程序(格式:JPG和HDF)
  65. NIST Fingerprint and handwriting -数据集-数千张图像(格式:未知)
  66. NIST Fingerprint data -压缩的多部分uuencoded tar文件
  67. NLM HyperDoc Visible Human Project -颜色,CAT和MRI图像样本-超过30张图像(格式:jpeg)
  68. National Design Repository -超过55,000个3D CAD和(主要是)机械/机械工程设计的实体模型. (格式:gif,vrml,wrl,stp,sat)
  69. Geometric & Intelligent Computing Laboratory
  70. OSU (MSU) 3D Object Model Database -几年来收集的几套3D对象模型用于对象识别研究(格式:自制软件,VRML)
  71. OSU (MSU/WSU) Range Image Database -数百张真实和合成图像(格式:gif,自制)
  72. OSU/SAMPL Database: Range Images, 3D Models, Stills, Motion Sequences -超过1000种范围的图像,3D对象模型,静止图像和运动序列(格式:gif,ppm,vrml,自制软件)
  73. Signal Analysis and Machine Perception Laboratory
  74. Otago Optical Flow Evaluation Sequences -具有机器可读的地面真实光流场的合成和真实序列,以及为新序列生成地面真实的工具. (格式:ppm,tif,自制)
  75. Vision Research Group
  76. https://github.com/ChristosChristofidis/awesome-deep-learning/blob/master/ftp://ftp.limsi.fr/pub/quenot/opflow/testdata/piv/ -用于测试“粒子图像测速”应用程序的真实和合成图像序列. 这些图像可用于测试光流和图像匹配算法. (格式:pgm(原始))
  77. LIMSI-CNRS/CHM/IMM/vision
  78. LIMSI-CNRS
  79. Photometric 3D Surface Texture Database -这是第一个3D纹理数据库,提供完整的真实表面旋转和已注册的光度立体数据(30个纹理,1680张图像). (格式:TIFF)
  80. SEQUENCES FOR OPTICAL FLOW ANALYSIS (SOFA) -9个合成序列设计用于测试运动分析应用程序,包括运动的完整地面真相和摄像机参数. (格式:gif)
  81. Computer Vision Group
  82. Sequences for Flow Based Reconstruction -用于从运动算法测试结构的合成序列(格式:pgm)
  83. Stereo Images with Ground Truth Disparity and Occlusion -走廊的一小组合成图像,其中添加了不同数量的噪声. 使用这些图像对您的立体声算法进行基准测试. (格式:原始,viff(khoros)或tiff)
  84. Stuttgart Range Image Database -从网络上可用的高分辨率多边形模型获取的合成范围图像的集合(格式:自制)
  85. Department Image Understanding
  86. The AR Face Database -包含4,000张彩色图像,分别对应126人的面孔(70位男性和56位女性). 具有面部表情,照明和遮挡的正面视图. (格式:RAW(RGB 24-bit))
  87. Purdue Robot Vision Lab
  88. The MIT-CSAIL Database of Objects and Scenes -用于测试多类目标检测和场景识别算法的数据库. 带有7873个带注释帧的超过72,000张图像. 超过50种带注释的对象类. (格式:jpg)
  89. The RVL SPEC-DB (SPECularity DataBase) -在三种不同的照明条件下(漫射/环境/定向)拍摄的100个对象的300多个真实图像的集合. -使用这些图像测试用于检测和补偿彩色图像中镜面反射高光的算法. (格式:TIFF)
  90. Robot Vision Laboratory
  91. The Xm2vts database -XM2VTSDB包含四个数字记录,在四个月的时间内拍摄了295人. 该数据库包含面部的图像和视频数据.
  92. Centre for Vision, Speech and Signal Processing
  93. Traffic Image Sequences and 'Marbled Block' Sequence -数以千计的数字化交通图像序列序列以及“大理石块”序列(灰度图像)(格式:GIF)
  94. IAKS/KOGS
  95. U Bern Face images -数百张图像(格式:Sun rasterfile)
  96. U Michigan textures (格式:原始压缩)
  97. U Oulu wood and knots database -包括分类-1000多种彩色图像(格式:ppm)
  98. UCID - an Uncompressed Colour Image Database -用于使用预定义的地面真实性进行图像检索的基准数据库. (格式:tiff)
  99. UMass Vision Image Archive -具有航空,太空,立体,医学图像等的大型图像数据库. (格式:自制)
  100. UNC's 3D image database -许多图像(格式:GIF)
  101. USF Range Image Data with Segmentation Ground Truth -80个图像集(格式:Sun rasterimage)
  102. University of Oulu Physics-based Face Database -包含在不同光源和相机校准条件下人脸的彩色图像,以及每个人的皮肤光谱反射率测量值.
  103. Machine Vision and Media Processing Unit
  104. University of Oulu Texture Database -320个表面纹理的数据库,每个表面纹理均在三种光源,六种空间分辨率和九种旋转角度下捕获. 还提供了一组测试套件,以便可以以标准方式测试纹理分割,分类和检索算法. (格式:bmp,ras,xv)
  105. Machine Vision Group
  106. Usenix face database -来自许多不同地点的数千张人脸图像(大约994年)
  107. View Sphere Database -从许多不同的角度看到的8个对象的图像. 使用具有172个图像/球的测地线对视球进行采样. 提供两组训练和测试. (格式:ppm)
  108. PRIMA, GRAVIR
  109. Vision-list Imagery Archive -许多图像,多种格式
  110. Wiry Object Recognition Database -成千上万的手推车,梯子,凳子,自行车,椅子和凌乱的场景的图像,边缘和区域带有地面真相标签. (格式:jpg)
  111. 3D Vision Group
  112. Yale Face Database -具有不同光照,表情和遮挡配置的165张图像(15个人).
  113. Yale Face Database B -在576个观看条件下(9个姿势x 64个照明条件)分别观看了10760个对象的5760个单光源图像. (格式:PGM)
  114. Center for Computational Vision and Control
  115. DeepMind QA Corpus -来自CNN和DailyMail的文字质量检查语料库. 总共超过30万个文档. Paper 以供参考.
  116. YouTube-8M Dataset -YouTube-8M是一个带有标签的大规模视频数据集,包含800万个YouTube视频ID和来自4800个视觉实体的多种词汇的相关标签.
  117. Open Images dataset -Open Images是约900万个URL的数据集,这些URL的图像已用6000多个类别的标签进行了注释.
  118. Visual Object Classes Challenge 2012 (VOC2012) -VOC2012数据集包含12k图像和20个带注释的类,用于对象检测和分割.
  119. Fashion-MNIST -MNIST之类的时尚产品数据集,包含60,000个示例的训练集和10,000个示例的测试集. 每个示例都是一个28x28灰度图像,与来自10个类别的标签相关联.
  120. Large-scale Fashion (DeepFashion) Database -包含超过800,000种多样的时尚图片. 该数据集中的每幅图像都标记有50个类别,1,000个描述性属性,边界框和服装地标
  121. FakeNewsCorpus -包含约一千万种使用以下内容分类的新闻文章 opensources.co 类型

Conferences

  1. CVPR - IEEE Conference on Computer Vision and Pattern Recognition
  2. AAMAS - International Joint Conference on Autonomous Agents and Multiagent Systems
  3. IJCAI - International Joint Conference on Artificial Intelligence
  4. ICML - International Conference on Machine Learning
  5. ECML - European Conference on Machine Learning
  6. KDD - Knowledge Discovery and Data Mining
  7. NIPS - Neural Information Processing Systems
  8. O'Reilly AI Conference - O'Reilly Artificial Intelligence Conference
  9. ICDM - International Conference on Data Mining
  10. ICCV - International Conference on Computer Vision
  11. AAAI - Association for the Advancement of Artificial Intelligence
  12. MAIS - Montreal AI Symposium

Frameworks

  1. Caffe
  2. Torch7
  3. Theano
  4. cuda-convnet
  5. convetjs
  6. Ccv
  7. NuPIC
  8. DeepLearning4J
  9. Brain
  10. DeepLearnToolbox
  11. Deepnet
  12. Deeppy
  13. JavaNN
  14. hebel
  15. Mocha.jl
  16. OpenDL
  17. cuDNN
  18. MGL
  19. Knet.jl
  20. Nvidia DIGITS - a web app based on Caffe
  21. Neon - Python based Deep Learning Framework
  22. Keras - Theano based Deep Learning Library
  23. Chainer - A flexible framework of neural networks for deep learning
  24. RNNLM Toolkit
  25. RNNLIB - A recurrent neural network library
  26. char-rnn
  27. MatConvNet: CNNs for MATLAB
  28. Minerva - a fast and flexible tool for deep learning on multi-GPU
  29. Brainstorm - Fast, flexible and fun neural networks.
  30. Tensorflow - Open source software library for numerical computation using data flow graphs
  31. DMTK - Microsoft Distributed Machine Learning Tookit
  32. Scikit Flow - Simplified interface for TensorFlow (mimicking Scikit Learn)
  33. MXnet - Lightweight, Portable, Flexible Distributed/Mobile Deep Learning framework
  34. Veles - Samsung Distributed machine learning platform
  35. Marvin - A Minimalist GPU-only N-Dimensional ConvNets Framework
  36. Apache SINGA - A General Distributed Deep Learning Platform
  37. DSSTNE - Amazon's library for building Deep Learning models
  38. SyntaxNet - Google's syntactic parser - A TensorFlow dependency library
  39. mlpack - A scalable Machine Learning library
  40. Torchnet - Torch based Deep Learning Library
  41. Paddle - PArallel Distributed Deep LEarning by Baidu
  42. NeuPy - Theano based Python library for ANN and Deep Learning
  43. Lasagne - a lightweight library to build and train neural networks in Theano
  44. nolearn - wrappers and abstractions around existing neural network libraries, most notably Lasagne
  45. Sonnet - a library for constructing neural networks by Google's DeepMind
  46. PyTorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
  47. CNTK - Microsoft Cognitive Toolkit
  48. Serpent.AI - Game agent framework: Use any video game as a deep learning sandbox
  49. Caffe2 - A New Lightweight, Modular, and Scalable Deep Learning Framework
  50. deeplearn.js - Hardware-accelerated deep learning and linear algebra (NumPy) library for the web
  51. TVM - End to End Deep Learning Compiler Stack for CPUs, GPUs and specialized accelerators
  52. Coach - Reinforcement Learning Coach by Intel® AI Lab
  53. albumentations - A fast and framework agnostic image augmentation library
  54. Neuraxle - A general-purpose ML pipelining framework
  55. Catalyst: High-level utils for PyTorch DL & RL research. It was developed with a focus on reproducibility, fast experimentation and code/ideas reusing
  56. garage - A toolkit for reproducible reinforcement learning research
  57. Detecto - Train and run object detection models with 5-10 lines of code
  58. Karate Club - An unsupervised machine learning library for graph structured data
  59. Synapses - A lightweight library for neural networks that runs anywhere
  60. TensorForce - A TensorFlow library for applied reinforcement learning
  61. Hopsworks - A Feature Store for ML and Data-Intensive AI
  62. Feast - A Feature Store for ML for GCP by Gojek/Google
  63. PyTorch Geometric Temporal - Representation learning on dynamic graphs
  64. lightly - A computer vision framework for self-supervised learning
  65. Trax — Deep Learning with Clear Code and Speed
  66. Flax - a neural network ecosystem for JAX that is designed for flexibility
  67. QuickVision

Tools

  1. Netron -用于深度学习和机器学习模型的可视化工具
  2. Jupyter Notebook -用于交互式计算的基于Web的笔记本环境
  3. TensorBoard -TensorFlow的可视化工具包
  4. Visual Studio Tools for AI -开发,调试和部署深度学习和AI解决方案
  5. TensorWatch -深度学习的调试和可视化
  6. ML Workspace -用于机器学习和数据科学的基于Web的多合一IDE.
  7. dowel -用于机器学习研究的小记录器. 只需调用一次logger.log(),即可将任何对象记录到控制台,CSV,TensorBoard,文本日志文件等中.
  8. Neptune -用于实验跟踪和结果可视化的轻量级工具.
  9. CatalyzeX -浏览器扩展(ChromeFirefox) that automatically finds 和 links to code implementations for ML papers anywhere online: Google, Twitter, Arxiv, Scholar, etc.
  10. Determined -深度学习培训平台,集成支持分布式培训,超参数调整,智能GPU调度,实验跟踪和模型注册表.
  11. DAGsHub -开源ML社区平台–管理实验,数据和模型,并轻松创建协作ML项目.

Miscellaneous

  1. Google Plus - Deep Learning Community
  2. Caffe Webinar
  3. 100 Best Github Resources in Github for DL
  4. Word2Vec
  5. Caffe DockerFile
  6. TorontoDeepLEarning convnet
  7. gfx.js
  8. Torch7 Cheat sheet
  9. Misc from MIT's 'Advanced Natural Language Processing' course
  10. Misc from MIT's 'Machine Learning' course
  11. Misc from MIT's 'Networks for Learning: Regression and Classification' course
  12. Misc from MIT's 'Neural Coding and Perception of Sound' course
  13. Implementing a Distributed Deep Learning Network over Spark
  14. A chess AI that learns to play chess using deep learning.
  15. Reproducing the results of "Playing Atari with Deep Reinforcement Learning" by DeepMind
  16. Wiki2Vec. Getting Word2vec vectors for entities and word from Wikipedia Dumps
  17. The original code from the DeepMind article + tweaks
  18. Google deepdream - Neural Network art
  19. An efficient, batched LSTM.
  20. A recurrent neural network designed to generate classical music.
  21. Memory Networks Implementations - Facebook
  22. Face recognition with Google's FaceNet deep neural network.
  23. Basic digit recognition neural network
  24. Emotion Recognition API Demo - Microsoft
  25. Proof of concept for loading Caffe models in TensorFlow
  26. YOLO: Real-Time Object Detection
  27. YOLO: Practical Implementation using Python
  28. AlphaGo - A replication of DeepMind's 2016 Nature publication, "Mastering the game of Go with deep neural networks and tree search"
  29. Machine Learning for Software Engineers
  30. Machine Learning is Fun!
  31. Siraj Raval's Deep Learning tutorials
  32. Dockerface -易于安装和使用深度学习对Docker容器中的图像和视频进行更快的R-CNN人脸检测.
  33. Awesome Deep Learning Music -与音乐相关的深度学习科学研究相关文章的精选清单
  34. Awesome Graph Embedding -与图级别的图结构化数据的深度学习科学研究相关的文章的精选清单.
  35. Awesome Network Embedding -与节点级别的图结构化数据的深度学习科学研究相关的文章的精选清单.
  36. Microsoft Recommenders 包含用于构建推荐系统的示例,实用程序和最佳实践. 提供了几种最新算法的实现,可以在您自己的应用程序中进行自学习和自定义.
  37. The Unreasonable Effectiveness of Recurrent Neural Networks -Andrej Karpathy有关使用RNN生成文本的博客文章.
  38. Ladder Network -Keras实现阶梯式网络进行半监督学习
  39. toolbox: Curated list of ML libraries
  40. CNN Explainer
  41. AI Expert Roadmap -成为人工智能专家的路线图

Contributing

您有什么想法很不错,并且适合该列表吗? 随时发送 pull request.


License

CC0

在法律允许的范围内, Christos Christofidis 放弃了此作品的所有版权以及相关或邻近的权利.