└── README.md /README.md: -------------------------------------------------------------------------------- 1 | # Links of resources for learning AI. Feel free to suggest for more resources. 2 | 3 | Advanced Crash Courses 4 | Deep Learning by Ruslan Salakhutdinov @ KDD 2014 5 | http://videolectures.net/kdd2014_salakhutdinov_deep_learning 6 | 7 | 8 | Overview of DL including DBN, RBM, PGM etc which are not as popular these days. Very theoretical, dense and mathematical. Maybe not that useful for beginners. Salakhutdinov is another major player in DL. 9 | Introduction to Reinforcement Learning with Function Approximation by Rich Sutton @ NIPS 2015 10 | 11 | 12 | http://research.microsoft.com/apps/video/?id=259577 13 | 14 | 15 | Another intro to RL but more technical and theoretical. Rich Sutton is old school king of RL. 16 | Deep Reinforcement Learning by David Silver @ RLDM 2015 17 | 18 | 19 | http://videolectures.net/rldm2015_silver_reinforcement_learning 20 | 21 | 22 | Advanced intro to Deep RL as used by Deepmind on the Atari games and AlphaGo. Quite technical and requires decent understanding of RL, TD learning and Q-Learning etc. (see RL courses below). David Silver is the new school king of RL and superstar of Deepmind’s AlphaGo (which uses Deep RL). 23 | Monte Carlo Inference Methods by Ian Murray @ NIPS 2015 24 | 25 | 26 | http://research.microsoft.com/apps/video/?id=259575 27 | 28 | 29 | Good introduction and overview of sampling / monte carlo based methods. Not essential for a lot of DL, but good side knowledge to have. 30 | How to Grow a Mind: Statistics, Structure and Abstraction by Josh Tenenbaum @ AAAI 2012 31 | http://videolectures.net/aaai2012_tenenbaum_grow_mind/ 32 | 33 | 34 | Completely unrelated to current DL and takes a very different approach: Bayesian Heirarchical Models. Not much success in real world yet, but I’m still a fan as the questions and problems they’re looking at feels a lot more applicable to real world than DL (e.g. one-shot learning and transfer learning, though Deepmind is looking at this with DL as well now). 35 | Two architectures for one-shot learning by Josh Tenenbaum @ NIPS 2013 36 | http://videolectures.net/nipsworkshops2013_tenenbaum_learning 37 | 38 | 39 | Similar to above but slightly more recent. 40 | 41 | Optimal and Suboptimal Control in Brain and Behavior by Nathaniel Daw @ NIPS 2015 42 | http://videolectures.net/rldm2015_daw_brain_and_behavior 43 | 44 | 45 | Quite unrelated to DL, looks at human learning — combined with research from pyschology and neuroscience — through the computational lens of RL. Requires decent understanding of RL. 46 | Lots more one-off video lectures at: 47 | http://videolectures.net/Top/Computer_Science/Artificial_Intelligence 48 | 49 | 50 | http://videolectures.net/Top/Computer_Science/Machine_Learning/ 51 | 52 | 53 | Massive Open Online Courses (MOOC) 54 | These are concentrated long term courses consisting of many video lectures. Ordered very roughly in the order that I recommend they are watched. 55 | Foundation / Maths 56 | 57 | https://www.khanacademy.org/math/probability 58 | 59 | 60 | 61 | https://www.khanacademy.org/math/linear-algebra 62 | 63 | 64 | 65 | https://www.khanacademy.org/math/calculus-home 66 | 67 | 68 | 69 | http://research.microsoft.com/apps/video/?id=259574 70 | 71 | 72 | http://videolectures.net/sahd2014_lecun_deep_learning/ 73 | 74 | 75 | http://videolectures.net/rldm2015_littman_computational_reinforcement 76 | 77 | 78 |

Resource for beginners:

79 |

80 |

[2] Machine Learning Theory

81 |

[3] Introduction to Computer Science and Programming in Python

82 |

[4] Seeing Theory

83 |

[5] Udacity - Intro to Artificial Intelligence

84 |

[1] Scaler Blogs - AI and Machine Learning

85 |

[5] Udacity - Deep Learning Foundations Course

86 |

[6] Hacker’s guide to Neural Networks

87 |

[7] CS 131 Computer Vision: Foundations and Applications

88 |

[8] Coursera Machine Learning courses

89 |

[9] Introduction to Artificial Neural Networks and Deep Learning

90 |

[10] Python Programing by Harrison

91 |

[11] Youtube channel of Harrison(from basic python to machine learning)

92 |

[12] Matlab neural network toolbox

93 |

[13] Matlab for deep learning

94 |

[14] Learning Circles

95 |

[15] Playground

96 |

[16] A.I. Experiments

97 |

[17] Machine Learning Algorithm Cheat Sheet

98 |

[18] Tombone’s Computer Vision Blog

99 |

[19] Bokeh Gallery

100 |

[20] A visual introduction to machine learning

101 |

[21] Machine Learning Mastery

102 |

[22] Everything I know about Python

103 |

[23] TensorFlow and Deep Learning without a PhD(video)

104 |

[24] Daniel Nouri Blog

105 |

[25] Programming a Perceptron in Python

106 |

[26] Improving our neural network by optimising Gradient Descent

107 |

[27] Learn TensorFlow and deep learning, without a PhD.(note)

108 |

[28] 13 Free Self-Study Books on Mathematics, Machine Learning & Deep Learning

109 |

[29] Python Program Flow Visualizer

110 |

[30] Collaborative Open Computer Science

111 |

[31] The Open Cognition Project

112 |

[32] Hvass Labs TensorFlow Tutorials

113 |

[33] Introduction to Machine Learning for Arts / Music

114 |

[34] Stanford University class CS231n: Convolutional Neural Networks for Visual Recognition by Prof. Fei-Fei Li

115 |

[35] TSne

116 |

[36] Learning Object Categories

117 |

[37] Chris Olah’s blog

118 |

[38] CS224d: Deep Learning for Natural Language Processing

119 |

[39] Jake VanderPlas Blog

120 |

[40] AIDL Blog

121 |

[41] KD Nuggets

122 |

[42] What is Deep Learning?

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Resource for the average user:

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[1] Convolutional Neural Networks for Visual Recognition.

127 |

[1] Deep Learning, An MIT Press book

128 | 129 |

[1] Deep Learning, Datacamp

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[1] Tensorflow, Pluralsight

131 |

[1] Natural Language Processing, Stanford

132 |

[2] Stanford University Deep Learning Tutorial

133 |

[3] A Guide to Deep Learning

134 |

[4] Deep Learning for Self-Driving Car

135 |

[5] Deep Learning for Self-Driving Cars (website)

136 |

[6] Deep Natural Language Processing

137 |

[7] Deep Learning documentation

138 |

[8] Deep Learning Tutorial

139 |

[9] Neural Networks and Deep Learning

140 |

[10] Deep Learning Forum

141 |

[11] Tensorflow for Deep Learning Research

142 |

[12] Pylearn2 Vision

143 |

[13] Siraj Raval youtube channel

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[14] TUTORIAL ON DEEP LEARNING FOR VISION

145 |

[15] Mining of Massive Datasets

146 |

[16] Accelerate Machine Learning with the cuDNN Deep Neural Network Library

147 |

[17] Deep Learning for Computer Vision with Caffe and cuDNN

148 |

[18] Embedded Machine Learning with the cuDNN Deep Neural Network Library and Jetson TK1

149 |

[19] Deep Learning in your browser (ConvNetJS)

150 |

[20] Machine Learning with Matlab

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[21] Toronto deep learning demo

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[22] Fields lectures

153 |

[23] Zipfian Academy

154 |

[24] Machine Learning Recipes with Josh Gordon

155 |

[25] Microsoft Professional Program

156 |

[26] Intel for deep learning

157 |

[27] GPU Accelerated Computing with Python

158 |

[28] Import AI Newsletter

159 |

[29] Traffic Sign Recognition with TensorFlow

160 |

[30] Understand backpropagation

161 |

[31] Bigdata University

162 |

[32] Open-source language understanding for bots

163 |

[33] Pure Python Decision Trees

164 |

[34] Top 20 Python Machine Learning Open Source Projects

165 |

[35] Deep Learning, NLP, and Representations

166 |

[36] Deep Learning Research Review: Natural Language Processing

167 |

[37] Image-to-Image Translation with Conditional Adversarial Nets

168 |

[38] CMUSphinx Tutorial For Developers

169 |

[39] Machine Learning in Arts by Gene Kogan

170 |

[40] The Neural Aesthetic

171 |

[41] Visualizing High-Dimensional Space

172 |

[42] Deep-visualization-toolbox

173 |

[43] Picasso CNN visualizer

174 |

[44] Self-Driving Car

175 |

[45] NN for Self-Driving Car

176 |

[46] Simulate a Self-Driving Car

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[47] CS 20SI: Tensorflow for Deep Learning Research

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179 |

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Resources for advanced user and researchers:

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[1] Recent Researches

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[2] The Morning Paper

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[3] Most Cited Deep Learning Papers

184 |

[4] Arxiv Sanity Preserver

185 |

[5] Uncertainty in Deep Learning

186 |

[6] Deep Patient

187 |

[7] A space-time delay neural network

188 |

[8] Google Cloud Natural Language API

189 |

[8] FloydHub - Heroku for DL

190 |

[9] Blue Brain Project

191 |

[10] Whole genome sequencing resource

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[11] Sorta Insightful

193 |

[12] The Eyescream Project

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[13] Generative Adversarial Networks

195 |

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Open source libraries/repositories/Framework:

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[1] Tensor Flow

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[2] Keras

199 |

[3] Scikit-learn

200 |

[4] Universe

201 |

[4] Lua

202 |

[5] Torch

203 |

[6] Theano

204 |

[7] Machine Learning Library (MLlib)

205 |

[8] UC Irvine Machine Learning Repository

206 |

[9] The CIFAR-10 dataset

207 |

[10] NeuPy

208 |

[11] Deeplearning4j

209 |

[12] ImageNet

210 |

[13] Seaborn

211 |

[14] MLdata

212 |

[15] CNTK

213 |

[16] Natural Language Toolkit(NLTK)

214 |

[17] Spacy

215 |

[18] CoreNLP

216 |

[19] Requests: HTTP for Humans

217 |

[20] Computational Healthcare library

218 |

[21] Blaze

219 |

[22] Dask

220 |

[23] Array Express

221 |

[24] Pillow

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[25] HTM

223 |

[26] Pybrain

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[27] Nilearn

225 |

[28] Pattern

226 |

[29] Fuel

227 |

[30] Pylearn2

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[31] Bob

229 |

[32] Skdata

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[33] MILK

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[34] IEPY

232 |

[35] Quepy

233 |

[36] nupic

234 |

[37] Hebel

235 |

[38] Ramp

236 |

[39] Machine-learning-samples

237 |

[40] H2O

238 |

[41] Optunity

239 |

[42] Awesome Public Datasets

240 |

[43] PyTorch

241 |

[44] Kubernetes

242 |

[45] Generative Adversarial Text-to-Image Synthesis

243 |

[46] Pydata

244 |

[47] Open Data Kit (ODK)

245 |

[48] Open Detection

246 |

[49] Mycroft

247 |

[50] Medical Image Net

248 |

[51] Biorxiv (archive and distribution service for unpublished preprints in the life sciences)

249 |

[52] Udacity Self-Driving Car Simulator

250 |

[53] List of Medical Datasets and repositories

251 |

252 |

All video materials:

253 |

[1] Machine Learning Recipes with Josh Gordon

254 |

[2] Deep Learning for Vision with Caffe framework

255 |

[3] Stanford University Machine Learning course (By Prof. Andrew Ng)

256 |

[4] Deep Learning for Computer Vision by Dr. Rob Fergus

257 |

[5] Caltech Machine Learning Course

258 |

[6] Machine Learning and AI via Brain simulations

259 |

[7] Deep Learning of Representations (Google Talk)

260 |

[8] Data School

261 |

[9] How to run Neural Nets on GPUs’ by Melanie Warrick

262 |

[10] TensorFlow and Deep Learning without a PhD

263 |

[11] Youtube channel of Harrison(from basic python to machine learning)

264 |

[12] Siraj Raval youtube channel

265 |

[13] Machine Learning Prepare Data Tutorial

266 |

[14] Hvass Laboratories

267 |

Brain-Computer Interfacing:

268 |

[1] All BCI resources at one place

269 |

270 |

AI companies / organisations:

271 |

[1] DeepMind

272 |

[2] MILA

273 |

[3] IBM Watson

274 |

[4] The Swiss AI Lab (IDSIA)

275 |

[5] Comma AI

276 |

[6] Indico

277 |

[7] Osaro

278 |

[8] Cloudera

279 |

[9] Geometric Intelligence

280 |

[10] Skymind

281 |

[11] MetaMind

282 |

[12] Iris AI

283 |

[13] Feedzai

284 |

[14] Loomai

285 |

[15] BenevolentAI

286 |

[16] Baidu Research

287 |

[17] Rasa AI

288 |

[18] AI Gym

289 |

[19] Nervana

290 |

[20] CrowdAI

291 |

[21] Idiap Research Institute

292 |

[22] Maluuba

293 |

[23] Neurala

294 |

[24] Artificial Intelligence Group at UCSD

295 |

[25] Turi

296 |

[26] Enlitic

297 |

[27] Element AI

298 |

[28] Accel AI

299 |

[29] Datalog AI

300 |

[30] Fast AI

301 |

[31] Applied Brain Research

302 |

[32] Neuraldesigner
303 |

304 |

[33] Autox

305 |

[34] Niramai

306 |

[35] Isenses

307 |

[36] MedGenome

308 |

[37] Recursion Pharmaceuticals, Inc.

309 |

[38] Geometric Intelligence

310 |

[39] jukedeck (AI Musician)

311 |

[40] Galaxy AI

312 |

[41] Atomwise

313 |

[42] DeepArt

314 |

315 |

316 |

AI Personalities:

317 |

318 |

319 |

320 |

321 |

322 |

323 |

324 |

325 |

326 |

327 |

328 |

329 |

Carl Edward Rasmussen

330 |

331 |

332 |

333 |

334 |

335 |

336 |

337 |

Joaquin Quinonero Candela

338 |

339 |

340 |

341 |

342 | 343 | Reference: http://aimedicines.com/2017/03/17/all-ai-resources-at-one-place/ 344 | --------------------------------------------------------------------------------