└── README.md /README.md: -------------------------------------------------------------------------------- 1 | ## MEGALODON: ML/DL Resources At One Place ## 2 | 3 |

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6 | 7 | 8 | | Blogs | Type | Comments | 9 | | ------------- |:-------------:| -----:| 10 | | [Stanford NLP](https://nlp.stanford.edu/blog/) | Research exposition | | 11 | | [Berkeley AI Research Lab (BAIR)](http://bair.berkeley.edu/blog/) | Research exposition | | 12 | | [Off the Convex Path](http://www.offconvex.org/) | Research exposition | | 13 | | [Andrej Karpathy blog](http://karpathy.github.io/), [Andrej Karpathy - Medium](https://medium.com/@karpathy)| Personal | | 14 | | [Distill](https://distill.pub/) | Research exposition | | 15 | | [Christopher Olah](http://colah.github.io/) | Personal | | 16 | | [Sebastian Ruder](http://sebastianruder.com) | Personal | | 17 | | [Elad Hazan](http://www.minimizingregret.com) | Personal | | 18 | | [Ben Recht](http://www.argmin.net/2017/12/11/alchemy-addendum/)| Personal | | 19 | | [Shakir Muhammed](http://blog.shakirm.com/)| Personal | | 20 | | [Inference.vc](http://www.inference.vc/) | Personal | | 21 | | [R2RT](http://r2rt.com/) | Personal | | 22 | | [Pythonic Perambulations](https://jakevdp.github.io) | Personal | | 23 | | [Sebastian Raschka](https://sebastianraschka.com/blog/index.html) | Personal | | 24 | | [Papers wih Code](https://paperswithcode.com/)||| 25 | | [Depth First Learning](http://www.depthfirstlearning.com/)||| 26 | | [Moritz Hardt](http://blog.mrtz.org/)||| 27 | | [MadryLab](https://gradientscience.org/)||| 28 | 29 | 30 | | Podcasts/Talks | Type | Comments | 31 | | ------------- |:-------------:| -----:| 32 | | [Talking Machines](http://www.thetalkingmachines.com/) | Interviews/Research Exposition | | 33 | | [Radim](https://soundcloud.com/piskvorky) | Interviews | | 34 | | [The AI Podcast](https://soundcloud.com/theaipodcast) | Interviews | | 35 | | [TWiML & AI](https://soundcloud.com/twiml) | Interviews | | 36 | | [NLP-Highlights](https://soundcloud.com/nlp-highlights) | Interviews | | 37 | | [The Thesis Review](https://cs.nyu.edu/~welleck/podcast.html) | Interviews | | 38 | | [NLP with Friends](https://nlpwithfriends.com/past/) | Presentations | | 39 | | [ML Street Talk](https://www.youtube.com/channel/UCMLtBahI5DMrt0NPvDSoIRQ) | Interviews | | 40 | | [Pytorch-dev-podcast](https://pytorch-dev-podcast.simplecast.com/) | Talks | Pytorch Internals | 41 | | [Stanford MLSys Seminar](https://www.youtube.com/channel/UCzz6ructab1U44QPI3HpZEQ) | Talks | | 42 | 43 | 44 | | Books | Focus Areas | Comments | 45 | | ------------- |:-------------:| -----:| 46 | | [Pattern Recognition and Machine Learning](https://www.amazon.com/Pattern-Recognition-Learning-Information-Statistics/dp/0387310738) |     |  [MATLAB Code](http://prml.github.io/) | 47 | | [Machine Learning: A Probabilistic Perspective](https://www.amazon.com/Machine-Learning-Probabilistic-Perspective-Computation/dp/0262018020/ref=pd_lpo_sbs_14_t_2?_encoding=UTF8&psc=1&refRID=Q375K1MS03MBDV35BK04) | | | 48 | | [Deep Learning](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618/ref=pd_bxgy_14_img_3?_encoding=UTF8&pd_rd_i=0262035618&pd_rd_r=CMQXT5D8S1BMTNHYM77T&pd_rd_w=ATpsn&pd_rd_wg=c1mEk&psc=1&refRID=CMQXT5D8S1BMTNHYM77T) | | | 49 | | [The Elements of Statistical Learning](https://www.amazon.com/Elements-Statistical-Learning-Prediction-Statistics/dp/0387848576/ref=pd_sim_14_2?_encoding=UTF8&pd_rd_i=0387848576&pd_rd_r=HTMKEMAQHPW79GM7H19N&pd_rd_w=E5wu6&pd_rd_wg=gjLqq&psc=1&refRID=HTMKEMAQHPW79GM7H19N) | | | 50 | | [Computer Age Statistical Inference](https://www.amazon.com/Computer-Age-Statistical-Inference-Mathematical/dp/1107149894/ref=pd_sim_14_8?_encoding=UTF8&pd_rd_i=1107149894&pd_rd_r=8C8BK3Z2D2D6Z62AF3EF&pd_rd_w=VPa0N&pd_rd_wg=OaWML&psc=1&refRID=8C8BK3Z2D2D6Z62AF3EF) | | | 51 | | [Foundations of Machine Learning](https://www.amazon.com/Foundations-Machine-Learning-Adaptive-Computation/dp/026201825X/ref=pd_sim_14_18?_encoding=UTF8&pd_rd_i=026201825X&pd_rd_r=8C8BK3Z2D2D6Z62AF3EF&pd_rd_w=VPa0N&pd_rd_wg=OaWML&psc=1&refRID=8C8BK3Z2D2D6Z62AF3EF) | | | 52 | | [Understanding Machine Learning: From Theory to Algorithms](https://www.amazon.com/Understanding-Machine-Learning-Theory-Algorithms/dp/1107057132/ref=pd_sim_14_29?_encoding=UTF8&pd_rd_i=1107057132&pd_rd_r=8C8BK3Z2D2D6Z62AF3EF&pd_rd_w=VPa0N&pd_rd_wg=OaWML&psc=1&refRID=8C8BK3Z2D2D6Z62AF3EF) | | | 53 | | [Probabilistic Graphical Models](https://www.amazon.com/Probabilistic-Graphical-Models-Principles-Computation/dp/0262013193/ref=pd_sim_14_33?_encoding=UTF8&pd_rd_i=0262013193&pd_rd_r=8C8BK3Z2D2D6Z62AF3EF&pd_rd_w=VPa0N&pd_rd_wg=OaWML&psc=1&refRID=8C8BK3Z2D2D6Z62AF3EF) | | | 54 | | [Information Theory, Inference and Learning Algorithms](https://www.amazon.com/Information-Theory-Inference-Learning-Algorithms/dp/0521642981/ref=pd_sim_14_5?_encoding=UTF8&pd_rd_i=0521642981&pd_rd_r=KGZCK3EZ5JJKRJ3WGVEX&pd_rd_w=nmpvV&pd_rd_wg=kMbpw&psc=1&refRID=KGZCK3EZ5JJKRJ3WGVEX) | | | 55 | | [Model Based Machine Learning](http://www.mbmlbook.com/) | | | 56 | | [Neural Networks for Pattern Recognition](https://www.amazon.com/Networks-Pattern-Recognition-Advanced-Econometrics/dp/0198538642) | | | 57 | | [Foundations Of Data Science](http://www.cs.cornell.edu/jeh/book%20June%2014,%202017pdf.pdf)| | [Lectures](https://www.youtube.com/watch?v=WEBUWYxaqLQ) | 58 | | [A Course in Machine Learning](http://ciml.info/)| | | 59 | 60 | 61 | | Monographs/Reports/Tutorials | Focus Areas | Comments | 62 | | ------------- |:-------------:| -----:| 63 | | [Algorithmic Aspects Of ML](http://people.csail.mit.edu/moitra/docs/bookex.pdf) | | [Videos](https://www.youtube.com/watch?v=nsHbkVMaUGk&list=PLB3sDpSRdrOvI1hYXNsa6Lety7K8FhPpx) | 64 | | [Non Convex Optimization for ML](https://arxiv.org/abs/1712.07897) | | | 65 | | [NMT AND Seq2Seq Models: A Tutorial](https://arxiv.org/abs/1703.01619) | | | 66 | | [Intro to ML without Deep Learning](http://www.kyunghyuncho.me/home/blog/lecturenotebriefintroductiontomachinelearningwithoutdeeplearning) | | | 67 | | [Frontiers in Massive Data Analysis]( https://www.nap.edu/read/18374/chapter/1) | | | 68 | | [High-Dimensional Data Analysis: Curses & Blessings](http://statweb.stanford.edu/~donoho/Lectures/AMS2000/AMS2000.html) | | [50 years of Data Science](http://courses.csail.mit.edu/18.337/2015/docs/50YearsDataScience.pdf)| 69 | 70 | 71 | | Summer Schools/Seminars | Focus Areas | Comments | 72 | | ------------- |:-------------:| -----:| 73 | | [MLSS, Tubingen 07](http://videolectures.net/mlss07_tuebingen/) | | | 74 | | [Cambridge](http://videolectures.net/mlss09uk_cambridge/) | | | 75 | | [MLSS Purdue](https://www.youtube.com/playlist?list=PL2A65507F7D725EFB) | | | 76 | | [DLSS, Montreal 2015](http://videolectures.net/deeplearning2015_montreal/) | | | 77 | | [DLSS, Montreal 2016](http://videolectures.net/deeplearning2016_montreal/) | | | 78 | | [Deep Learning School, 2016](https://www.youtube.com/watch?v=zij_FTbJHsk&list=PLrAXtmErZgOfMuxkACrYnD2fTgbzk2THW) | | [All Videos](https://www.youtube.com/watch?v=eyovmAtoUx0) | 79 | | [DLSS & RLSS, Montreal 2017](http://videolectures.net/deeplearning2017_montreal/) | | | 80 | | [MLSS, Kioloa 08](http://videolectures.net/mlss08au_kioloa/) | | | 81 | | [MLSS, Chicago 09](http://videolectures.net/mlss09us_chicago/) | | | 82 | | [MLSS, Canberra 02](http://videolectures.net/mlss02_canberra/) | | | 83 | | [MSR India MLSS, 2015](https://www.microsoft.com/en-us/research/event/msr-india-summer-school-2015-on-machine-learning/) || | 84 | | [AI Summer School, 2017](https://www.microsoft.com/en-us/research/event/ai-summer-school-2017/) | | | 85 | | [Deep RL Bootcamp, Berkeley](https://sites.google.com/view/deep-rl-bootcamp/lectures) | | | 86 | | [IPAM Deep Learning, Feature Learning, 2012](http://www.ipam.ucla.edu/programs/summer-schools/graduate-summer-school-deep-learning-feature-learning/?tab=schedule)| | | 87 | | [MLSS, Max Plank Institute, 2017](https://www.youtube.com/watch?v=XugKv3lxQac) | | | 88 | | [MLSS, CMU 2014](http://www.mlss2014.com/) | | | 89 | | [Deep Learning: Theory, Algorithms, and Applications](https://www.youtube.com/playlist?list=PLJOzdkh8T5kqCNV_v1w2tapvtJDZYiohW) | | | 90 | | [Gaussian Process Summer Schools](http://gpss.cc) | | | 91 | | [MLSS, Iceland, 2014](https://www.youtube.com/watch?v=rcZHO2Lyd8Q&list=PLqdbxUnkqOw2nKn7VxYqIrKWcqRkQYOsF)| | | 92 | |[MLSS Sydney 15](https://www.youtube.com/channel/UCT1k2e63pqm_VSXmaF21n6g/videos)||| 93 | |[MLSS London 2019](https://search.videoken.com/?orgId=198#)||| 94 | |[New Tech in Math Seminar](https://cmsa.fas.harvard.edu/tech-in-math/)||| 95 | 96 | 97 | | Video Channels/Videos | Focus Areas | Comments | 98 | | ------------- |:-------------:| -----:| 99 | | [videolectures.net](videolectures.net) | | [ICLR 2016](http://videolectures.net/iclr2016_san_juan/) | 100 | | [Channel9](http://videolectures.net/mlss09uk_cambridge/) | | [NIPS 16](https://channel9.msdn.com/events/Neural-Information-Processing-Systems-Conference/Neural-Information-Processing-Systems-Conference-NIPS-2016) | 101 | | [TechTalks.tv](https://soundcloud.com/theaipodcast) | | [EMNLP 16](http://techtalks.tv/emnlp2016/), [ACL 16](http://techtalks.tv/acl_2016/), [ICML 2016](http://techtalks.tv/icml/2016/) | 102 | | [Deep Learning Book Club](https://www.youtube.com/watch?v=vi7lACKOUao) | | [Deep learning book club](https://www.youtube.com/channel/UCF9O8Vj-FEbRDA5DcDGz-Pg/videos) | 103 | | [Simons Institute](https://www.youtube.com/user/SimonsInstitute/playlists) | | [DL Tutorials](https://simons.berkeley.edu/talks/tutorial-deep-learning), [Opt & Fairness](https://simons.berkeley.edu/symposium/optimization-and-fairness) | 104 | | [Center for Brains, Minds and Machines (CBMM)](https://www.youtube.com/channel/UCGoxKRfTs0jQP52cfHCyyRQ/playlists) | | | 105 | | [CVF](https://www.youtube.com/channel/UC0n76gicaarsN_Y9YShWwhw/playlists) | | | 106 | | [CIS Lectures](https://www.youtube.com/watch?v=FD-DCpiRt4Q&list=PLjV5ChM0ZamXvq-wzo3zMFcb6ZI3r72CN) | | | 107 | | [ICLR 2015](https://www.youtube.com/channel/UCqxFGrNL5nX10lS62bswp9w)| | | 108 | | [IAS, Theoretical ML](https://www.ias.edu/ideas?aff=2)| | | 109 | | [Formal and Applied Linguistics](http://lectures.ms.mff.cuni.cz/) | | | 110 | | [ICLR 19](https://slideslive.com/iclr) | | | 111 | | [David MacKay's Lectures](https://www.youtube.com/channel/UCfoScwn69ekXXWNTN0CLGXA)||| 112 | | [ACL 2019](https://www.livecongress.it/sved/evt/aol_lnk.php?id=60B5FD70) ||| 113 | | [Allen AI](https://allenai.org/videos/videos-all-2019.html)||| 114 | 115 | 116 | 117 | | General Resource Curations | Type | Comments | 118 | | ------------- |:-------------:| -----:| 119 | | [ML Videos](https://github.com/dustinvtran/ml-videos) | | | 120 | | [Scholarpedia](http://www.scholarpedia.org/article/Main_Page) | | | 121 | | [Short Science](http://www.shortscience.org/) | | | 122 | | [Best Papers](http://jeffhuang.com/best_paper_awards.html) | | | 123 | | [Pluralsight](https://www.pluralsight.com/search?q=machine%20learning) | | | 124 | | [Safari Books Online](https://www.safaribooksonline.com/learning-paths/learning-path-machine/9781491987346/) | | | 125 | 126 | 127 | | Specialized Resource Curations | Type | Comments | 128 | | ------------- |:-------------:| -----:| 129 | | [Meta-Learning Papers](https://github.com/songrotek/Meta-Learning-Papers) | | | 130 | | [NLP Tasks](https://github.com/Kyubyong/nlp_tasks) | | | 131 | 132 | | Academic Groups/Labs | Focus Areas | Comments | 133 | | ------------- |:-------------:| -----:| 134 | | [Saarland](http://www.ml.uni-saarland.de/code/IPM/IPM.htm) | | | 135 | | [UFLDL](http://deeplearning.stanford.edu/wiki/index.php/UFLDL_Tutorial) | | | 136 | 137 | 138 | | Industry Groups/Labs | Focus Areas | Comments | 139 | | ------------- |:-------------:| -----:| 140 | | [Microsoft](https://www.microsoft.com/en-us/research/blog/) | | | 141 | | [Microsoft Maluuba](http://www.maluuba.com/blog/) | | | 142 | | [Google Brain](https://research.google.com/teams/brain/) | | | 143 | | [Facebook](https://research.fb.com/category/facebook-ai-research-fair/) | | | 144 | | [Google Deepmind](https://deepmind.com/blog/) | | | 145 | | [Apple](https://machinelearning.apple.com/) | | | 146 | | [Recast AI](https://blog.recast.ai/category/machine-learning/) | NLP & Dialog Management |[API Reference](https://recast.ai/docs/api-reference/) | 147 | | [Salesforce Einstein](https://einstein.ai/research) | | | 148 | 149 | 150 | | Courses | Institute | Comments | 151 | | ------------- |:-------------:| -----:| 152 | | [Tensorfow for DL Research](https://web.stanford.edu/class/cs20si/index.html) | | General: [Advanced Scientific computing](https://am207.github.io/2017/material.html) | 153 | | [Intro to AI, UCB](http://ai.berkeley.edu/lecture_videos.html) | | | 154 | | [CNN for Visual Recognition](http://cs231n.stanford.edu/) | | | 155 | | [Deep Learning for NLP](http://cs224d.stanford.edu/) | | | 156 | | [Intro to Deep Learning, Princeton](https://www.cs.princeton.edu/courses/archive/spring16/cos495/) | | | 157 | | [Intro to Deep Learning, MIT](http://introtodeeplearning.com/) | | | 158 | | [NN for ML](https://www.coursera.org/learn/neural-networks) | | | 159 | | [Stanford ML (Old)](https://www.youtube.com/watch?v=UzxYlbK2c7E), [Current](http://cs229.stanford.edu/) | | | 160 | | [Probabilistic Graphical Models](https://www.coursera.org/learn/probabilistic-graphical-models#)|   |   | 161 | | [Fast.AI](http://www.fast.ai/) | | | 162 | | [Oxford Deep NLP, 17](https://github.com/oxford-cs-deepnlp-2017/lectures) | | | 163 | | [Theories of deep learning](https://stats385.github.io/) | | [Videos](https://www.researchgate.net/project/Theories-of-Deep-Learning) | 164 | | [Deep Learning System](http://dlsys.cs.washington.edu/) | | | 165 | | [PGM](https://github.com/ermongroup/cs228-notes) | | [Flexible models of uncertainty](https://csc2541-f17.github.io/) | 166 | 167 | | Frameworks/Libraries | Type | Comments | 168 | | ------------- |:-------------:| -----:| 169 | | [Tensorfow](https://www.tensorflow.org/) | | [TF Dev Summit, 17](https://www.youtube.com/watch?v=mWl45NkFBOc&list=PLOU2XLYxmsIKGc_NBoIhTn2Qhraji53cv) | 170 | | [Theano](http://deeplearning.net/software/theano/) | | | 171 | | [Lasagne](https://lasagne.readthedocs.io/en/latest/) | | | 172 | | [Keras](https://keras.io/) | | | 173 | | [CNTK](https://docs.microsoft.com/en-us/cognitive-toolkit/) | | | 174 | | [MXNET](https://mxnet.incubator.apache.org/) | | | 175 | | [Torch](http://torch.ch/) | | | 176 | | [PyTorch](http://pytorch.org/) | | | 177 | | [Caffe](http://caffe.berkeleyvision.org/) | | | 178 | | [Caffe2](https://caffe2.ai/) | | | 179 | | [Chainer](https://docs.chainer.org/en/stable/) | | | 180 | | [DyNet](https://dynet.readthedocs.io/en/latest/) | | | 181 | | [DL4J](https://deeplearning4j.org/) | | | 182 | | [Scikit-learn](http://scikit-learn.org/) | | | 183 | | [MALMO](https://www.microsoft.com/en-us/research/project/project-malmo/) | RL Environment | | 184 | | [OpenAI Gym](https://gym.openai.com/docs/) | RL Environments | Not sure if still actively developed | 185 | | [Gluon](http://gluon.mxnet.io/) | | | 186 | | [ConvNetJS](https://github.com/karpathy/convnetjs) | | | 187 | | [deeplearn.js](https://deeplearnjs.org/) | | | 188 | | [Tangent](https://github.com/google/tangent) | Source to Source | | 189 | | [Autograd](https://github.com/HIPS/autograd) | | [Torch-Autograd](https://github.com/twitter/torch-autograd) | 190 | 191 | 192 | | Interviews | Focus Area | Comments | 193 | | ------------- |:-------------:| -----:| 194 | | [Deep Learning Heroes](https://www.youtube.com/watch?v=-eyhCTvrEtE&list=PLfsVAYSMwsksjfpy8P2t_I52mugGeA5gR) | | | 195 | 196 | 197 | | Social Networks | Type | Comments | 198 | | ------------- |:-------------:| -----:| 199 | | [Twitter](https://twitter.com/) | | | 200 | | [Reddit](reddit.com/r/MachineLearning/) | | Go to place for ml | 201 | | [Hacker News](https://news.ycombinator.com/) | | | 202 | | [Deep Learning Study Group, SF](https://www.meetup.com/deep-learning-sf/) | | | 203 | 204 | 205 | | Newsletters | Focus Areas | Comments | 206 | | ------------- |:-------------:| -----:| 207 | | [Wild Week in AI](https://www.getrevue.co/profile/wildml) | | [2017 review](http://www.wildml.com/2017/12/ai-and-deep-learning-in-2017-a-year-in-review/) | 208 | | [NLP News](http://newsletter.ruder.io/issues/nlp-news-nlp-for-beginners-dialogue-sentence-representations-64351) | | | 209 | | [the morning paper](https://blog.acolyer.org/) | | | 210 | | [ML Review](https://medium.com/mlreview) | | | 211 | | [Import AI](https://jack-clark.net/about/) | | | 212 | | [Gitxiv Newsletter](http://www.gitxiv.com/) | | | 213 | | [Nathan Benaich](https://www.getrevue.co/profile/nathanbenaich/) | | | 214 | | [O'reilly AI Newsletter](http://www.oreilly.com/ai/newsletter.html) | | | 215 | | [Inside AI](https://inside.com/ai) | | | 216 | | [Videolectures Digest](http://videolectures.net/) | | | 217 | 218 | 219 | | Datasets | Task | Comments | 220 | | ------------- |:-------------:| -----:| 221 | | [NLP Datasets](https://github.com/karthikncode/nlp-datasets) | || 222 | 223 | 224 | ### Other Blogs 225 | * https://smerity.com/articles/articles.html 226 | * http://veredshwartz.blogspot.in/ 227 | * https://stats385.github.io/blogs 228 | * https://blogs.princeton.edu/imabandit/ 229 | * https://www.countbayesie.com 230 | * http://building-babylon.net/ 231 | * [While My MCMC Gently Samples](http://twiecki.github.io/) 232 | * http://www.marekrei.com/blog/online-representation-learning-in-recurrent-neural-language-models/ 233 | * http://mlg.eng.cam.ac.uk/yarin/blog.html 234 | * https://blogs.msdn.microsoft.com/ericlippert/ 235 | * https://ericlippert.com/ 236 | * https://blogs.msdn.microsoft.com/ericlippert/tag/high-dimensional-spaces/ 237 | * http://blog.echen.me/ 238 | * radford neal's blog https://radfordneal.wordpress.com/ 239 | * http://timvieira.github.io/blog/archives.html 240 | * http://p.migdal.pl/ 241 | * https://www.quora.com/What-are-the-best-machine-learning-blogs-or-resources-available 242 | * http://ml.typepad.com/machine_learning_thoughts/ 243 | * https://jmetzen.github.io/ 244 | * http://peekaboo-vision.blogspot.in/ 245 | * http://sebastianruder.com/word-embeddings-1/index.html?utm_content=bufferca13e&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer 246 | * https://jamesmccaffrey.wordpress.com/ 247 | * http://alexey.radul.name/2/ 248 | * https://www.reddit.com/r/MachineLearning/comments/4juw5z/cool_deep_learning_ml_blogs/ 249 | * http://dsnotes.com/ 250 | * http://mccormickml.com/ 251 | * http://approximatelycorrect.com/ 252 | * http://timdettmers.com/2015/03/26/convolution-deep-learning/ 253 | * https://campus2.acm.org/public/qj/brandingqj/xrds.cfm 254 | * http://www.kemaswill.com/ 255 | * https://jacobgil.github.io/ 256 | * http://www.argmin.net/ 257 | * http://tscholak.github.io/ 258 | * https://theneuralperspective.com/ 259 | * https://devblogs.nvidia.com/parallelforall/ 260 | * http://textminingonline.com/ 261 | * http://douglasduhaime.com/blog/clustering-semantic-vectors-with-python 262 | * https://telecombcn-dl.github.io/2017-dlcv/ 263 | * cs 231 http://cs231n.stanford.edu/, cs 221 http://web.stanford.edu/class/cs221/ 264 | * http://nlpforhackers.io/ 265 | * Keras, Torch, TF, http://dp.readthedocs.io/en/latest/index.html , Theano blogs are also very useful. 266 | * http://gkalliatakis.com/blog/delving-deep-into-gans 267 | * https://oshearesearch.com/ 268 | * https://www.youtube.com/watch?v=Xogn6veSyxA&list=PLbwivfGkPdvi4Pn66Yc8TWpNy18OhEhW_ 269 | * https://terrytao.wordpress.com/2017/03/01/special-cases-of-shannon-entropy/ 270 | * http://nlp.yvespeirsman.be/ 271 | * http://bcomposes.com/2015/11/26/simple-end-to-end-tensorflow-examples/ 272 | * https://prateekvjoshi.com/ 273 | * http://anie.me/ 274 | * http://wellredd.uk/ 275 | * http://p.migdal.pl/2017/04/30/teaching-deep-learning.html 276 | * https://www.countbayesie.com/blog/2017/5/9/kullback-leibler-divergence-explained 277 | * https://vkrakovna.wordpress.com/ 278 | * https://codingmachinelearning.wordpress.com/ 279 | * http://www.seaandsailor.com/index.html 280 | * http://www.kentran.net/ 281 | * http://arogozhnikov.github.io/ 282 | * http://philipperemy.github.io/ 283 | * http://appliedpredictivemodeling.com/blog/2014/11/27/08ks7leh0zof45zpf5vqe56d1sahb0 284 | * http://andymiller.github.io/blog/ 285 | * http://www.argmin.net/ 286 | * http://setosa.io/ev/ 287 | * http://rbharath.github.io/ 288 | * http://wiseodd.github.io/ 289 | * https://www.neurolab.de/cosine_notes.html 290 | * http://willwolf.io/ 291 | * http://www.minimizingregret.com/ 292 | * http://bookworm.benschmidt.org/index.html 293 | * http://www.brainyblog.net/ 294 | * https://iksinc.wordpress.com/ 295 | * https://erikbern.com/ 296 | * https://kevinzakka.github.io/2016/07/13/k-nearest-neighbor/ 297 | * http://dustintran.com/blog/ 298 | * http://blog.kaggle.com/ 299 | * http://jponttuset.cat/blog/ 300 | * http://blog.echen.me/2017/05/30/exploring-lstms/ 301 | * http://deliprao.com/archives/187 302 | * http://www.ams.org/samplings/feature-column/fcarc-svd 303 | * https://www.countbayesie.com/all-posts/ 304 | * http://briandolhansky.com/blog/2013/7/8/ml-primers 305 | * http://iamtrask.github.io/ 306 | * https://hips.seas.harvard.edu/blog/ 307 | * https://jeremykun.com/ 308 | * 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