深度学习

Awesome Deep Learning Awesome

Free Online Books

  1. Deep Learning 作者:Yoshua Bengio,Ian Goodfellow和Aaron Courville(05/07/2015)
  2. Neural Networks and Deep Learning 作者Michael Nielsen(2014年12月)
  3. Deep Learning 微软研究院(2013年)
  4. Deep Learning Tutorial 蒙特利尔大学LISA实验室(2015年1月6日)
  5. neuraltalk 作者:Andrej Karpathy:基于numpy的RNN / LSTM实施
  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

Courses

  1. Machine Learning - Stanford 作者:Andrew Ng在Coursera(2010-2014)
  2. Machine Learning - Caltech 作者:Yaser Abu-Mostafa(2012-2014)
  3. Machine Learning - Carnegie Mellon 汤姆米切尔(2011年春季)
  4. Neural Networks for Machine Learning 作者:Geoffrey Hinton在Coursera(2012)
  5. Neural networks class 来自舍布鲁克大学的Hugo Larochelle(2013年)
  6. Deep Learning Course 由CILVR lab @ NYU(2014)
  7. A.I - Berkeley 作者:Dan Klein和Pieter Abbeel(2013)
  8. A.I - MIT 作者:Patrick Henry Winston(2010)
  9. Vision and learning - computers and brains 作者:Shimon Ullman,Tomaso Poggio,Ethan Meyers @ MIT(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 Geoffrey Hinton,Yoshua Bengio,Yann LeCun,Andrew Ng,Nando de Freitas和其他几位@ IPAM,UCLA(2012)
  16. Deep Learning - Udacity/Google 作者:Vincent Vanhoucke和Arpan Chakraborty(2016)
  17. Deep Learning - UWaterloo 滑铁卢大学的Ali Ghodsi教授(2015年)
  18. Statistical Machine Learning - CMU 由Larry Wasserman教授撰写
  19. Deep Learning Course 作者:Yann LeCun(2016)
  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 作者:Jeremy Howard - Fast.ai
  27. Introduction to Deep Learning 由教授 Bhiksha Raj(2017)
  28. AI for Everyone 作者:Andrew Ng(2019)
  29. MIT Intro to Deep Learning 7 day bootcamp - 麻省理工学院设计的七天训练营,介绍深度学习方法和应用(2019年)
  30. Deep Blueberry: Deep Learning - 免费的五周末计划,让自学者学习CNN,LSTM,RNN,VAE,GAN,DQN,A3C等深度学习架构的基础知识(2019)
  31. Spinning Up in Deep Reinforcement Learning - OpenAI免费深度强化学习课程(2019年)

Videos and Lectures

  1. How To Create A Mind 作者:Ray Kurzweil
  2. Deep Learning, Self-Taught Learning and Unsupervised Feature Learning 作者:Andrew Ng
  3. Recent Developments in Deep Learning 杰夫辛顿
  4. The Unreasonable Effectiveness of Deep Learning 作者:Yann LeCun
  5. Deep Learning of Representations 作者:Yoshua bengio
  6. Principles of Hierarchical Temporal Memory 杰夫霍金斯
  7. Machine Learning Discussion Group - Deep Learning w/ Stanford AI Lab 亚当科茨
  8. Making Sense of the World with Deep Learning 亚当科茨
  9. Demystifying Unsupervised Feature Learning 亚当科茨
  10. Visual Perception with Deep Learning 作者:Yann LeCun
  11. The Next Generation of Neural Networks 作者:Geoffrey Hinton,GoogleTechTalks
  12. The wonderful and terrifying implications of computers that can learn 作者:杰里米霍华德在TEDxBrussels
  13. Unsupervised Deep Learning - Stanford 作者:Andrew Ng在斯坦福大学(2011年)
  14. Natural Language Processing 克里斯曼宁在斯坦福大学
  15. A beginners Guide to Deep Neural Networks 作者:Natalie Hammel和Lorraine Yurshansky
  16. Deep Learning: Intelligence from Big Data Steve Jurvetson(和小组)在斯坦福大学的VLAB工作.
  17. Introduction to Artificial Neural Networks and Deep Learning 作者: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上的一系列小型讲座(2018年)

Papers

您还可以找到最常被引用的深度学习论文 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

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

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

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

Datasets

  1. MNIST 手写的数字
  2. Google House Numbers 从街景
  3. CIFAR-10 and CIFAR-100
  4. IMAGENET
  5. Tiny Images 8000万张小图片6.
  6. Flickr Data 1亿雅虎数据集
  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 - 空运数据集是光线跟踪的图像序列以及基于纹理特征的地面实况分割. (455张图像+ 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,用于科学目的. 为了捕捉物体记录中的感官变化,我们系统地改变每个物体的视角,照明角度和照明颜色,并另外捕获宽基线立体图像. 我们记录了每个对象的一百多张图像,共收集了110,250张图像. (格式:png)
  18. Annotated face, hand, cardiac & meat images - 使用AAM-API,大多数图像和注释都通过各种ASM / AAM分析进行补充. (格式:bmp,asf)
  19. Image Analysis and Computer Graphics
  20. Brown University Stimuli - 各种数据集,包括geons,objects和“greebles”. 适用于测试识别算法. (格式:pict)
  21. CAVIAR video sequences of mall and public space behavior - 90种不同人类活动序列中的90K视频帧,具有检测和行为分类的XML基础事实(格式:MPEG2和JPEG)
  22. Machine Vision Unit
  23. CCITT Fax standard images - 8张图片(格式:gif)
  24. CMU CIL's Stereo Data with Ground Truth - 3组11幅图像,包括带有光谱辐射的彩色tiff图像(格式:gif,tiff)
  25. CMU PIE Database - 包含13个姿势,43个照明条件和4种不同表情的68人拍摄的41,368张面部图像的数据库.
  26. CMU VASC Image Database - 图像,序列,立体对(数千张图像)(格式:Sun Rasterimage)
  27. Caltech Image Database - 大约20张图片 - 主要是自上而下的小物件和玩具. (格式:GIF)
  28. Columbia-Utrecht Reflectance and Texture Database - 超过60个3D纹理样本的纹理和反射测量,观察和观察和照明方向的200多种不同组合. (格式:bmp)
  29. Computational Colour Constancy Data - 面向计算颜色恒定性的数据集,但通常对计算机视觉有用. 它包括合成数据,相机传感器数据和700多张图像. (格式:tiff)
  30. Computational Vision Lab
  31. Content-based image retrieval database - 用于测试基于内容的检索算法的11组彩色图像. 大多数集都有一个描述文件,其中包含每个图像中对象的名称. (格式:jpg)
  32. Efficient Content-based Retrieval Group
  33. Densely Sampled View Spheres - 密集采样的视图球体 - 两个玩具对象的视图球体的上半部分,每个有2500个图像. (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 - 从胃肠视频内窥镜检查中获得的研究图像和视频. (格式:jpg,mpg,gif)
  38. FG-NET Facial Aging Database - 数据库包含1002个面部图像,显示不同年龄的科目. (格式:jpg)
  39. FVC2000 Fingerprint Databases - FVC2000是第一个国际指纹验证算法竞赛. 四个指纹数据库构成了FVC2000基准测试(总共3520个指纹).
  40. Biometric Systems Lab - 博洛尼亚大学
  41. Face and Gesture images and image sequences - Several image datasets of faces and gestures that are ground truth annotated for benchmarking
  42. German Fingerspelling Database - 该数据库包含35个手势,由1400个图像序列组成,这些图像序列包含在非均匀日光照明条件下记录的20个不同人的手势. (格式:mpg,jpg)
  43. Language Processing and Pattern Recognition
  44. Groningen Natural Image Database - 4000+ 1536x1024(16位)校准室外图像(格式:自制软件)
  45. ICG Testhouse sequence - 来自不同观看高度的2个转盘序列,每个36张图像,分辨率1000x750,颜色(格式:PPM)
  46. Institute of Computer Graphics and Vision
  47. IEN Image Library - 1000多张图像,主要是室外序列(格式:原始,ppm)
  48. INRIA's Syntim images database - 简单物体的15色图像(格式:gif)
  49. INRIA
  50. INRIA's Syntim stereo databases - 34个校准颜色立体声对(格式:gif)
  51. Image Analysis Laboratory - 从各种成像模式获得的图像 - 原始CFA图像,距离图像和一系列“医学图像”. (格式:自制软件)
  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张日本女性主题图像,这些图像构成6种基本面部表情以及中性姿势. 对于研究目的,也可以免费获得情绪形容词的评级. (格式:TIFF灰度图像.)
  55. ATR Research, Kyoto, Japan
  56. JISCT Stereo Evaluation - 44个图像对. 这些数据已被用于评估立体分析,如1993年4月ARPA图像理解研讨会论文“RCCTolles,HHBaker和MJHannah,263--274(格式:SSI)的JISCT立体评估”中所述.
  57. MIT Vision Texture - 图像存档(100多张图像)(格式:ppm)
  58. MIT face images and more - 数百张图片(格式:自制软件)
  59. Machine Vision - 来自Jain,Kasturi,Schunck教科书的图片(20多张图片)(格式:GIF TIFF)
  60. Mammography Image Databases - 100个或更多乳房X线照片与真实情况的图像. 提供了按要求提供的附加图像,以及与其他几个乳房X线照相术数据库的链接. (格式:自制软件)
  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 - 包含平面区域的六个多帧立体数据集. 每个数据集包含9个彩色图像和亚像素精度的地面实况数据. (格式:ppm)
  63. Middlebury Stereo Vision Research Page - 米德尔伯里学院
  64. Modis Airborne simulator, Gallery and data set - 来自世界各地的高海拔影像,用于支持NASA EOS计划的环境建模(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 - 彩色,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)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 - 几年内收集的几组3D对象模型用于对象识别研究(格式:homebrew,vrml)
  71. OSU (MSU/WSU) Range Image Database - 数以百计的真实和合成图像(格式:gif,自制软件)
  72. OSU/SAMPL Database: Range Images, 3D Models, Stills, Motion Sequences - 超过1000个范围的图像,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 - 具有机器可读地面实况光流场的合成和真实序列,以及为新序列生成基础事实的工具. (格式:ppm,tif,自制软件)
  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Particle Image Velocimetry应用程序的真实和合成图像序列. 这些图像可用于光流和图像匹配算法的测试. (格式:pgm(raw))
  77. LIMSI-CNRS/CHM/IMM/vision
  78. LIMSI-CNRS
  79. Photometric 3D Surface Texture Database - 这是第一个提供完整真实表面旋转和注册光度立体数据(30个纹理,1680个图像)的3D纹理数据库. (格式:TIFF)
  80. SEQUENCES FOR OPTICAL FLOW ANALYSIS (SOFA) - 9种用于测试运动分析应用的合成序列,包括运动和相机参数的完整地面实况. (格式:gif)
  81. Computer Vision Group
  82. Sequences for Flow Based Reconstruction - 用于测试运动算法结构的合成序列(格式:pgm)
  83. Stereo Images with Ground Truth Disparity and Occlusion - 添加了不同噪音量的走廊的一小组合成图像. 使用这些图像来对立体算法进行基准测试 (格式:raw,viff(khoros)或tiff)
  84. Stuttgart Range Image Database - 从网上提供的高分辨率多边形模型中采集的合成范围图像集(格式:自制软件)
  85. Department Image Understanding
  86. The AR Face Database - 包含超过4,000张彩色图像,对应126个人的脸(70名男性和56名女性). 正面视图,面部表情,光照和遮挡变化. (格式:RAW(RGB 24位))
  87. Purdue Robot Vision Lab
  88. The MIT-CSAIL Database of Objects and Scenes - 用于测试多类物体检测和场景识别算法的数据库. 超过72,000张图像,带有2873个带注释的帧. 超过50个带注释的对象类. (格式:jpg)
  89. The RVL SPEC-DB (SPECularity DataBase) - 在三种不同的照明条件下(漫反射/环境/定向)拍摄的100个物体的300多张真实图像的集合. - 使用这些图像测试用于检测和补偿彩色图像中的镜面反射高光的算法. (格式:TIFF)
  90. Robot Vision Laboratory
  91. The Xm2vts database - XM2VTSDB包含四个为期四个月的295人的四个数字录音. 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 - 数千帧数字化交通图像序列以及“大理石块”序列(灰度图像)(格式:GIF)
  94. IAKS/KOGS
  95. U Bern Face images - 数百张图片(格式:Sun rasterfile)
  96. U Michigan textures (格式:压缩原始)
  97. U Oulu wood and knots database - 包括分类 - 1000+彩色图像(格式:ppm)
  98. UCID - an Uncompressed Colour Image Database - 具有预定义基础事实的图像检索的基准数据库. (格式:tiff)
  99. UMass Vision Image Archive - 具有航拍,空间,立体声,医学图像等的大型图像数据库. (格式:自制软件)
  100. UNC's 3D image database - 许多图像(格式:GIF)
  101. USF Range Image Data with Segmentation Ground Truth - 80幅图像集(格式:太阳光栅图像)
  102. University of Oulu Physics-based Face Database - 包含不同光源下的面部彩色图像和相机校准条件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,每个表面纹理在三个光源,六个空间分辨率和九个旋转角度下捕获. 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成千上万张脸部图像(大约994)
  107. View Sphere Database - 从许多不同视点看到的8个物体的图像. 使用具有172个图像/球体的测地线对视图球体进行采样. 提供两套培训和测试. (格式:ppm)
  108. PRIMA, GRAVIR
  109. Vision-list Imagery Archive - 许多图像,许多格式
  110. Wiry Object Recognition Database - 成千上万的购物车,梯子,凳子,自行车,椅子和杂乱的场景图像,边缘和区域的真实标签. (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个图像(15个人).
  113. Yale Face Database B - 在576个观察条件下观察到的10个对象的5760个单光源图像(9个构成×64个照明条件). (格式:PGM)
  114. Center for Computational Vision and Control
  115. DeepMind QA Corpus - 来自CNN和DailyMail的文本QA语料库. 总共超过300K文件. Paper 以供参考.
  116. YouTube-8M Dataset - YouTube-8M是一个大型标记视频数据集,由800万个YouTube视频ID和来自4800个视觉实体的不同词汇表的相关标签组成.
  117. Open Images dataset - “打开图像”是一个数据集,其中约有900万个网址,这些网址已使用超过6000个类别的标签进行注释.
  118. Visual Object Classes Challenge 2012 (VOC2012) - VOC2012数据集,包含12k图像,带有20个注释类,用于对象检测和分割.
  119. Fashion-MNIST - MNIST,如时尚产品数据集,由60,000个示例的训练集和10,000个示例的测试集组成. 每个示例都是28x28灰度图像,与10个类别的标签相关联.
  120. Large-scale Fashion (DeepFashion) Database - 包含超过800,000种不同的时尚形象. 此数据集中的每个图像都标有50个类别,1,000个描述属性,边界框和服装标记
  121. FakeNewsCorpus - 包含大约1000万条新闻文章 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

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. TensorForce - A TensorFlow library for applied reinforcement learning
  52. Coach - Reinforcement Learning Coach by Intel® AI Lab
  53. albumentations - A fast and framework agnostic image augmentation library

Tools

  1. Netron - 用于深度学习和机器学习模型的Visualizer
  2. Jupyter Notebook - 基于Web的交互式计算笔记本环境
  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

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. AlphaGo - A replication of DeepMind's 2016 Nature publication, "Mastering the game of Go with deep neural networks and tree search"
  28. Machine Learning for Software Engineers
  29. Machine Learning is Fun!
  30. Siraj Raval's Deep Learning tutorials
  31. Dockerface - 易于安装和使用深度学习更快的R-CNN面部检测,用于码头集装箱中的图像和视频.
  32. Awesome Deep Learning Music - 与音乐深度学习科学研究相关的精选文章清单
  33. Awesome Graph Embedding - 图表级别的图形结构化数据深度学习科学研究相关文章.
  34. Awesome Network Embedding - 与节点级图形结构化数据的深度学习科学研究相关的文章列表.

Contributing

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


License

CC0

在法律允许的范围内, Christos Christofidis 已放弃对此作品的所有版权及相关或相邻权利.