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MEGALODON: ML/DL Resources At One Place ##\n\n\u003cp\u003e\n\u003cimg src=\"https://i.imgur.com/m9rlj23.jpg\"\u003e\n\u003c/p\u003e\n\n\n| Blogs        | Type           | Comments  |\n| ------------- |:-------------:| -----:|\n| [Stanford NLP](https://nlp.stanford.edu/blog/)      | Research exposition |  |\n| [Berkeley AI Research Lab (BAIR)](http://bair.berkeley.edu/blog/)      | Research exposition      |   |\n| [Off the Convex Path](http://www.offconvex.org/) | Research exposition      |   |\n| [Andrej Karpathy blog](http://karpathy.github.io/), [Andrej Karpathy - Medium](https://medium.com/@karpathy)| Personal      |    |\n| [Distill](https://distill.pub/)      | Research exposition |  |\n| [Christopher Olah](http://colah.github.io/)      | Personal |  |\n| [Sebastian Ruder](http://sebastianruder.com)      | Personal |  |\n| [Elad Hazan](http://www.minimizingregret.com)      | Personal |  |\n| [Ben Recht](http://www.argmin.net/2017/12/11/alchemy-addendum/)| Personal | |\n| [Shakir Muhammed](http://blog.shakirm.com/)| Personal | |\n| [Inference.vc](http://www.inference.vc/)      | Personal |  |\n| [R2RT](http://r2rt.com/)      | Personal |  |\n| [Pythonic Perambulations](https://jakevdp.github.io)      | Personal |  |\n| [Sebastian Raschka](https://sebastianraschka.com/blog/index.html)      | Personal |  |\n| [Papers wih Code](https://paperswithcode.com/)|||\n| [Depth First Learning](http://www.depthfirstlearning.com/)|||\n| [Moritz Hardt](http://blog.mrtz.org/)|||\n| [MadryLab](https://gradientscience.org/)|||\n\n\n| Podcasts/Talks        | Type           | Comments  |\n| ------------- |:-------------:| -----:|\n| [Talking Machines](http://www.thetalkingmachines.com/)      | Interviews/Research Exposition |  |\n| [Radim](https://soundcloud.com/piskvorky)      | Interviews      |    |\n| [The AI Podcast](https://soundcloud.com/theaipodcast) | Interviews      |    |\n| [TWiML \u0026 AI](https://soundcloud.com/twiml) | Interviews      |    |\n| [NLP-Highlights](https://soundcloud.com/nlp-highlights) | Interviews      |    |\n| [The Thesis Review](https://cs.nyu.edu/~welleck/podcast.html) | Interviews      |    |\n| [NLP with Friends](https://nlpwithfriends.com/past/) | Presentations      |    |\n| [ML Street Talk](https://www.youtube.com/channel/UCMLtBahI5DMrt0NPvDSoIRQ) | Interviews      |    |\n| [Pytorch-dev-podcast](https://pytorch-dev-podcast.simplecast.com/) | Talks      |  Pytorch Internals  |\n| [Stanford MLSys Seminar](https://www.youtube.com/channel/UCzz6ructab1U44QPI3HpZEQ) | Talks      |    |\n\n\n| Books        |    Focus Areas        | Comments  |\n| ------------- |:-------------:| -----:|\n| [Pattern Recognition and Machine Learning](https://www.amazon.com/Pattern-Recognition-Learning-Information-Statistics/dp/0387310738) |      |  [MATLAB Code](http://prml.github.io/)   |\n| [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\u0026psc=1\u0026refRID=Q375K1MS03MBDV35BK04) |       |    |\n| [Deep Learning](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618/ref=pd_bxgy_14_img_3?_encoding=UTF8\u0026pd_rd_i=0262035618\u0026pd_rd_r=CMQXT5D8S1BMTNHYM77T\u0026pd_rd_w=ATpsn\u0026pd_rd_wg=c1mEk\u0026psc=1\u0026refRID=CMQXT5D8S1BMTNHYM77T)      |      |    |\n| [The Elements of Statistical Learning](https://www.amazon.com/Elements-Statistical-Learning-Prediction-Statistics/dp/0387848576/ref=pd_sim_14_2?_encoding=UTF8\u0026pd_rd_i=0387848576\u0026pd_rd_r=HTMKEMAQHPW79GM7H19N\u0026pd_rd_w=E5wu6\u0026pd_rd_wg=gjLqq\u0026psc=1\u0026refRID=HTMKEMAQHPW79GM7H19N)      |       |    |\n| [Computer Age Statistical Inference](https://www.amazon.com/Computer-Age-Statistical-Inference-Mathematical/dp/1107149894/ref=pd_sim_14_8?_encoding=UTF8\u0026pd_rd_i=1107149894\u0026pd_rd_r=8C8BK3Z2D2D6Z62AF3EF\u0026pd_rd_w=VPa0N\u0026pd_rd_wg=OaWML\u0026psc=1\u0026refRID=8C8BK3Z2D2D6Z62AF3EF)      |       |    |\n| [Foundations of Machine Learning](https://www.amazon.com/Foundations-Machine-Learning-Adaptive-Computation/dp/026201825X/ref=pd_sim_14_18?_encoding=UTF8\u0026pd_rd_i=026201825X\u0026pd_rd_r=8C8BK3Z2D2D6Z62AF3EF\u0026pd_rd_w=VPa0N\u0026pd_rd_wg=OaWML\u0026psc=1\u0026refRID=8C8BK3Z2D2D6Z62AF3EF)      |      |   |\n| [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\u0026pd_rd_i=1107057132\u0026pd_rd_r=8C8BK3Z2D2D6Z62AF3EF\u0026pd_rd_w=VPa0N\u0026pd_rd_wg=OaWML\u0026psc=1\u0026refRID=8C8BK3Z2D2D6Z62AF3EF)      |       |    |\n| [Probabilistic Graphical Models](https://www.amazon.com/Probabilistic-Graphical-Models-Principles-Computation/dp/0262013193/ref=pd_sim_14_33?_encoding=UTF8\u0026pd_rd_i=0262013193\u0026pd_rd_r=8C8BK3Z2D2D6Z62AF3EF\u0026pd_rd_w=VPa0N\u0026pd_rd_wg=OaWML\u0026psc=1\u0026refRID=8C8BK3Z2D2D6Z62AF3EF)      |       |    |\n| [Information Theory, Inference and Learning Algorithms](https://www.amazon.com/Information-Theory-Inference-Learning-Algorithms/dp/0521642981/ref=pd_sim_14_5?_encoding=UTF8\u0026pd_rd_i=0521642981\u0026pd_rd_r=KGZCK3EZ5JJKRJ3WGVEX\u0026pd_rd_w=nmpvV\u0026pd_rd_wg=kMbpw\u0026psc=1\u0026refRID=KGZCK3EZ5JJKRJ3WGVEX)      |       |    |\n| [Model Based Machine Learning](http://www.mbmlbook.com/)      |      |    |\n| [Neural Networks for Pattern Recognition](https://www.amazon.com/Networks-Pattern-Recognition-Advanced-Econometrics/dp/0198538642)      |       |    |\n| [Foundations Of Data Science](http://www.cs.cornell.edu/jeh/book%20June%2014,%202017pdf.pdf)| | [Lectures](https://www.youtube.com/watch?v=WEBUWYxaqLQ) |\n| [A Course in Machine Learning](http://ciml.info/)| |  |\n\n\n| Monographs/Reports/Tutorials        | Focus Areas           | Comments  |\n| ------------- |:-------------:| -----:|\n| [Algorithmic Aspects Of ML](http://people.csail.mit.edu/moitra/docs/bookex.pdf)      |  | [Videos](https://www.youtube.com/watch?v=nsHbkVMaUGk\u0026list=PLB3sDpSRdrOvI1hYXNsa6Lety7K8FhPpx) |\n| [Non Convex Optimization for ML](https://arxiv.org/abs/1712.07897)      |       |    |\n| [NMT AND Seq2Seq Models: A Tutorial](https://arxiv.org/abs/1703.01619) |      |     |\n| [Intro to ML without Deep Learning](http://www.kyunghyuncho.me/home/blog/lecturenotebriefintroductiontomachinelearningwithoutdeeplearning) |      |     |\n| [Frontiers in Massive Data Analysis]( https://www.nap.edu/read/18374/chapter/1)      |       |   |\n| [High-Dimensional Data Analysis: Curses \u0026 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)|\n\n\n| Summer Schools/Seminars        | Focus Areas           | Comments  |\n| ------------- |:-------------:| -----:|\n| [MLSS, Tubingen 07](http://videolectures.net/mlss07_tuebingen/)      |  |  |\n| [Cambridge](http://videolectures.net/mlss09uk_cambridge/)      |       |    |\n| [MLSS Purdue](https://www.youtube.com/playlist?list=PL2A65507F7D725EFB) |      |     |\n| [DLSS, Montreal 2015](http://videolectures.net/deeplearning2015_montreal/) |      |     |\n| [DLSS, Montreal 2016](http://videolectures.net/deeplearning2016_montreal/) |      |    |\n| [Deep Learning School, 2016](https://www.youtube.com/watch?v=zij_FTbJHsk\u0026list=PLrAXtmErZgOfMuxkACrYnD2fTgbzk2THW) |      |    [All Videos](https://www.youtube.com/watch?v=eyovmAtoUx0) |\n| [DLSS \u0026 RLSS, Montreal 2017](http://videolectures.net/deeplearning2017_montreal/) |     |     |\n| [MLSS, Kioloa 08](http://videolectures.net/mlss08au_kioloa/) |    |     |\n| [MLSS, Chicago 09](http://videolectures.net/mlss09us_chicago/) |      |     |\n| [MLSS, Canberra 02](http://videolectures.net/mlss02_canberra/) |      |     |\n| [MSR India MLSS, 2015](https://www.microsoft.com/en-us/research/event/msr-india-summer-school-2015-on-machine-learning/) ||     |\n| [AI Summer School, 2017](https://www.microsoft.com/en-us/research/event/ai-summer-school-2017/) |     |    |\n| [Deep RL Bootcamp, Berkeley](https://sites.google.com/view/deep-rl-bootcamp/lectures)      |       |    |\n| [IPAM Deep Learning, Feature Learning, 2012](http://www.ipam.ucla.edu/programs/summer-schools/graduate-summer-school-deep-learning-feature-learning/?tab=schedule)|    |    |\n| [MLSS, Max Plank Institute, 2017](https://www.youtube.com/watch?v=XugKv3lxQac) |      |     |\n| [MLSS, CMU 2014](http://www.mlss2014.com/) |      |     |\n| [Deep Learning: Theory, Algorithms, and Applications](https://www.youtube.com/playlist?list=PLJOzdkh8T5kqCNV_v1w2tapvtJDZYiohW) |      |     |\n| [Gaussian Process Summer Schools](http://gpss.cc) |      |     |\n| [MLSS, Iceland, 2014](https://www.youtube.com/watch?v=rcZHO2Lyd8Q\u0026list=PLqdbxUnkqOw2nKn7VxYqIrKWcqRkQYOsF)| | |\n|[MLSS Sydney 15](https://www.youtube.com/channel/UCT1k2e63pqm_VSXmaF21n6g/videos)|||\n|[MLSS London 2019](https://search.videoken.com/?orgId=198#)|||\n|[New Tech in Math Seminar](https://cmsa.fas.harvard.edu/tech-in-math/)|||\n\n\n| Video Channels/Videos        | Focus Areas           | Comments  |\n| ------------- |:-------------:| -----:|\n| [videolectures.net](videolectures.net)      |  | [ICLR 2016](http://videolectures.net/iclr2016_san_juan/) |\n| [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) |\n| [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/) |\n| [Deep Learning Book Club](https://www.youtube.com/watch?v=vi7lACKOUao) |       | [Deep learning book club](https://www.youtube.com/channel/UCF9O8Vj-FEbRDA5DcDGz-Pg/videos)   |\n| [Simons Institute](https://www.youtube.com/user/SimonsInstitute/playlists) |       |    [DL Tutorials](https://simons.berkeley.edu/talks/tutorial-deep-learning), [Opt \u0026 Fairness](https://simons.berkeley.edu/symposium/optimization-and-fairness) |\n| [Center for Brains, Minds and Machines (CBMM)](https://www.youtube.com/channel/UCGoxKRfTs0jQP52cfHCyyRQ/playlists) |      |     |\n| [CVF](https://www.youtube.com/channel/UC0n76gicaarsN_Y9YShWwhw/playlists) |      |    |\n| [CIS Lectures](https://www.youtube.com/watch?v=FD-DCpiRt4Q\u0026list=PLjV5ChM0ZamXvq-wzo3zMFcb6ZI3r72CN) | | |\n| [ICLR 2015](https://www.youtube.com/channel/UCqxFGrNL5nX10lS62bswp9w)| | |\n| [IAS, Theoretical ML](https://www.ias.edu/ideas?aff=2)|  | |\n| [Formal and Applied Linguistics](http://lectures.ms.mff.cuni.cz/) | | |\n| [ICLR 19](https://slideslive.com/iclr) | | |\n| [David MacKay's Lectures](https://www.youtube.com/channel/UCfoScwn69ekXXWNTN0CLGXA)|||\n| [ACL 2019](https://www.livecongress.it/sved/evt/aol_lnk.php?id=60B5FD70) |||\n| [Allen AI](https://allenai.org/videos/videos-all-2019.html)|||\n\n\n\n| General Resource Curations        | Type           | Comments  |\n| ------------- |:-------------:| -----:|\n| [ML Videos](https://github.com/dustinvtran/ml-videos)      |  |  |\n| [Scholarpedia](http://www.scholarpedia.org/article/Main_Page)      |      |    |\n| [Short Science](http://www.shortscience.org/) |      |     |\n| [Best Papers](http://jeffhuang.com/best_paper_awards.html) |       |     |\n| [Pluralsight](https://www.pluralsight.com/search?q=machine%20learning) |       |     |\n| [Safari Books Online](https://www.safaribooksonline.com/learning-paths/learning-path-machine/9781491987346/) |      |     |\n\n\n| Specialized Resource Curations        | Type           | Comments  |\n| ------------- |:-------------:| -----:|\n| [Meta-Learning Papers](https://github.com/songrotek/Meta-Learning-Papers)      |  |  |\n| [NLP Tasks](https://github.com/Kyubyong/nlp_tasks)      |  |  |\n\n| Academic Groups/Labs        | Focus Areas           | Comments  |\n| ------------- |:-------------:| -----:|\n| [Saarland](http://www.ml.uni-saarland.de/code/IPM/IPM.htm)      |  |  |\n| [UFLDL](http://deeplearning.stanford.edu/wiki/index.php/UFLDL_Tutorial)      |      |    |\n\n\n| Industry Groups/Labs        | Focus Areas           | Comments  |\n| ------------- |:-------------:| -----:|\n| [Microsoft](https://www.microsoft.com/en-us/research/blog/)      |  |  |\n| [Microsoft Maluuba](http://www.maluuba.com/blog/)      |  |  |\n| [Google Brain](https://research.google.com/teams/brain/)      |      |    |\n| [Facebook](https://research.fb.com/category/facebook-ai-research-fair/) |       |     |\n| [Google Deepmind](https://deepmind.com/blog/) |       |    |\n| [Apple](https://machinelearning.apple.com/) |     |    |\n| [Recast AI](https://blog.recast.ai/category/machine-learning/) | NLP \u0026 Dialog Management |[API Reference](https://recast.ai/docs/api-reference/)     |\n| [Salesforce Einstein](https://einstein.ai/research) |      |    |\n\n\n| Courses        | Institute           | Comments  |\n| ------------- |:-------------:| -----:|\n| [Tensorfow for DL Research](https://web.stanford.edu/class/cs20si/index.html)      |  | General: [Advanced Scientific computing](https://am207.github.io/2017/material.html) |\n| [Intro to AI, UCB](http://ai.berkeley.edu/lecture_videos.html) |      |     |\n| [CNN for Visual Recognition](http://cs231n.stanford.edu/) |       |     |\n| [Deep Learning for NLP](http://cs224d.stanford.edu/) |      |     |\n| [Intro to Deep Learning, Princeton](https://www.cs.princeton.edu/courses/archive/spring16/cos495/) | | |\n| [Intro to Deep Learning, MIT](http://introtodeeplearning.com/) |  |  |\n| [NN for ML](https://www.coursera.org/learn/neural-networks) |       |    |\n| [Stanford ML (Old)](https://www.youtube.com/watch?v=UzxYlbK2c7E), [Current](http://cs229.stanford.edu/) | |  |\n| [Probabilistic Graphical Models](https://www.coursera.org/learn/probabilistic-graphical-models#)|    |    |\n| [Fast.AI](http://www.fast.ai/)      |  |    |\n| [Oxford Deep NLP, 17](https://github.com/oxford-cs-deepnlp-2017/lectures)      |  |   |\n| [Theories of deep learning](https://stats385.github.io/) | | [Videos](https://www.researchgate.net/project/Theories-of-Deep-Learning) |\n| [Deep Learning System](http://dlsys.cs.washington.edu/) | | |\n| [PGM](https://github.com/ermongroup/cs228-notes) | | [Flexible models of uncertainty](https://csc2541-f17.github.io/) |\n\n| Frameworks/Libraries        | Type           | Comments  |\n| ------------- |:-------------:| -----:|\n| [Tensorfow](https://www.tensorflow.org/)      |  | [TF Dev Summit, 17](https://www.youtube.com/watch?v=mWl45NkFBOc\u0026list=PLOU2XLYxmsIKGc_NBoIhTn2Qhraji53cv) |\n| [Theano](http://deeplearning.net/software/theano/)      |       |   |\n| [Lasagne](https://lasagne.readthedocs.io/en/latest/) |     |     |\n| [Keras](https://keras.io/) |    |     |\n| [CNTK](https://docs.microsoft.com/en-us/cognitive-toolkit/) |      |     |\n| [MXNET](https://mxnet.incubator.apache.org/) |      |    |\n| [Torch](http://torch.ch/) |     |    |\n| [PyTorch](http://pytorch.org/) |    |    |\n| [Caffe](http://caffe.berkeleyvision.org/) |       |     |\n| [Caffe2](https://caffe2.ai/) |     |    |\n| [Chainer](https://docs.chainer.org/en/stable/) |      |     |\n| [DyNet](https://dynet.readthedocs.io/en/latest/) |      |     |\n| [DL4J](https://deeplearning4j.org/) |     |    |\n| [Scikit-learn](http://scikit-learn.org/) |       |    |\n| [MALMO](https://www.microsoft.com/en-us/research/project/project-malmo/) |  RL Environment    |    |\n| [OpenAI Gym](https://gym.openai.com/docs/) |   RL Environments   | Not sure if still actively developed   |\n| [Gluon](http://gluon.mxnet.io/) |      |     |\n| [ConvNetJS](https://github.com/karpathy/convnetjs) |      |     |\n| [deeplearn.js](https://deeplearnjs.org/) |      |     |\n| [Tangent](https://github.com/google/tangent) |  Source to Source    |     |\n| [Autograd](https://github.com/HIPS/autograd) |      | [Torch-Autograd](https://github.com/twitter/torch-autograd)     |\n\n\n| Interviews        | Focus Area           | Comments  |\n| ------------- |:-------------:| -----:|\n| [Deep Learning Heroes](https://www.youtube.com/watch?v=-eyhCTvrEtE\u0026list=PLfsVAYSMwsksjfpy8P2t_I52mugGeA5gR)      |    |    |\n\n\n| Social Networks        | Type           | Comments  |\n| ------------- |:-------------:| -----:|\n| [Twitter](https://twitter.com/)      |  |  |\n| [Reddit](reddit.com/r/MachineLearning/)      |       |  Go to place for ml  |\n| [Hacker News](https://news.ycombinator.com/) |       |     |\n| [Deep Learning Study Group, SF](https://www.meetup.com/deep-learning-sf/) |       |    |\n\n\n| Newsletters        | Focus Areas           | Comments  |\n| ------------- |:-------------:| -----:|\n| [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/) |\n| [NLP News](http://newsletter.ruder.io/issues/nlp-news-nlp-for-beginners-dialogue-sentence-representations-64351) |      |    |\n| [the morning paper](https://blog.acolyer.org/)      |     |    |\n| [ML Review](https://medium.com/mlreview)      |     |    |\n| [Import AI](https://jack-clark.net/about/)      |     |    |\n| [Gitxiv Newsletter](http://www.gitxiv.com/)      |     |    |\n| [Nathan Benaich](https://www.getrevue.co/profile/nathanbenaich/) |     |    |\n| [O'reilly AI Newsletter](http://www.oreilly.com/ai/newsletter.html) |       |    |\n| [Inside AI](https://inside.com/ai) |     |    |\n| [Videolectures Digest](http://videolectures.net/) |      |    |\n\n\n| Datasets        | Task           | Comments  |\n| ------------- |:-------------:| -----:|\n| [NLP Datasets](https://github.com/karthikncode/nlp-datasets)      | ||\n\n\n### Other Blogs\n* https://smerity.com/articles/articles.html\n* http://veredshwartz.blogspot.in/\n* https://stats385.github.io/blogs\n* https://blogs.princeton.edu/imabandit/\n* https://www.countbayesie.com\n* http://building-babylon.net/\n* [While My MCMC Gently Samples](http://twiecki.github.io/)\n* http://www.marekrei.com/blog/online-representation-learning-in-recurrent-neural-language-models/\n* http://mlg.eng.cam.ac.uk/yarin/blog.html\n* https://blogs.msdn.microsoft.com/ericlippert/\n* https://ericlippert.com/\n* https://blogs.msdn.microsoft.com/ericlippert/tag/high-dimensional-spaces/\n* http://blog.echen.me/\n* radford neal's blog https://radfordneal.wordpress.com/\n* http://timvieira.github.io/blog/archives.html\n* http://p.migdal.pl/\n* https://www.quora.com/What-are-the-best-machine-learning-blogs-or-resources-available\n* http://ml.typepad.com/machine_learning_thoughts/\n* https://jmetzen.github.io/\n* http://peekaboo-vision.blogspot.in/\n* http://sebastianruder.com/word-embeddings-1/index.html?utm_content=bufferca13e\u0026utm_medium=social\u0026utm_source=twitter.com\u0026utm_campaign=buffer\n* https://jamesmccaffrey.wordpress.com/\n* http://alexey.radul.name/2/\n* https://www.reddit.com/r/MachineLearning/comments/4juw5z/cool_deep_learning_ml_blogs/\n* http://dsnotes.com/\n* http://mccormickml.com/\n* http://approximatelycorrect.com/\n* http://timdettmers.com/2015/03/26/convolution-deep-learning/\n* https://campus2.acm.org/public/qj/brandingqj/xrds.cfm\n* http://www.kemaswill.com/\n* https://jacobgil.github.io/\n* http://www.argmin.net/\n* http://tscholak.github.io/\n* https://theneuralperspective.com/\n* https://devblogs.nvidia.com/parallelforall/\n* http://textminingonline.com/\n* http://douglasduhaime.com/blog/clustering-semantic-vectors-with-python\n* https://telecombcn-dl.github.io/2017-dlcv/\n* cs 231 http://cs231n.stanford.edu/, cs 221 http://web.stanford.edu/class/cs221/\n* http://nlpforhackers.io/\n* Keras, Torch, TF, http://dp.readthedocs.io/en/latest/index.html , Theano blogs are also very useful.\n* http://gkalliatakis.com/blog/delving-deep-into-gans\n* https://oshearesearch.com/\n* https://www.youtube.com/watch?v=Xogn6veSyxA\u0026list=PLbwivfGkPdvi4Pn66Yc8TWpNy18OhEhW_\n* https://terrytao.wordpress.com/2017/03/01/special-cases-of-shannon-entropy/\n* http://nlp.yvespeirsman.be/\n* http://bcomposes.com/2015/11/26/simple-end-to-end-tensorflow-examples/\n* https://prateekvjoshi.com/\n* http://anie.me/\n* http://wellredd.uk/\n* http://p.migdal.pl/2017/04/30/teaching-deep-learning.html\n* https://www.countbayesie.com/blog/2017/5/9/kullback-leibler-divergence-explained\n* https://vkrakovna.wordpress.com/\n* https://codingmachinelearning.wordpress.com/\n* http://www.seaandsailor.com/index.html\n* http://www.kentran.net/\n* http://arogozhnikov.github.io/\n* http://philipperemy.github.io/\n* http://appliedpredictivemodeling.com/blog/2014/11/27/08ks7leh0zof45zpf5vqe56d1sahb0\n* http://andymiller.github.io/blog/\n* http://www.argmin.net/\n* http://setosa.io/ev/\n* http://rbharath.github.io/\n* http://wiseodd.github.io/\n* https://www.neurolab.de/cosine_notes.html\n* http://willwolf.io/\n* http://www.minimizingregret.com/\n* http://bookworm.benschmidt.org/index.html\n* http://www.brainyblog.net/\n* https://iksinc.wordpress.com/\n* https://erikbern.com/\n* https://kevinzakka.github.io/2016/07/13/k-nearest-neighbor/\n* http://dustintran.com/blog/\n* http://blog.kaggle.com/\n* http://jponttuset.cat/blog/\n* http://blog.echen.me/2017/05/30/exploring-lstms/\n* http://deliprao.com/archives/187\n* http://www.ams.org/samplings/feature-column/fcarc-svd\n* https://www.countbayesie.com/all-posts/\n* http://briandolhansky.com/blog/2013/7/8/ml-primers\n* http://iamtrask.github.io/\n* https://hips.seas.harvard.edu/blog/\n* https://jeremykun.com/\n* https://theclevermachine.wordpress.com/\n* http://nlp.yvespeirsman.be/blog/\n* http://andrew.gibiansky.com/\n* https://joanna-bryson.blogspot.de/\n* http://tullo.ch/\n* http://yanran.li/\n* https://theneural.wordpress.com/\n* http://jotterbach.github.io/archive/\n* http://ischlag.github.io/\n* http://www.marekrei.com/blog/\n* http://alexhwilliams.info/itsneuronalblog/\n* http://planspace.org/\n* https://shapeofdata.wordpress.com/page/2/\n* https://machinethoughts.wordpress.com/\n* http://shubhanshu.com/blog/\n* https://gmarti.gitlab.io/\n* http://www.panderson.me/blog/\n* http://giorgiopatrini.org/posts/2017/09/06/in-search-of-the-missing-signals/\n* http://www-users.cs.umn.edu/~verma/blog.html\n* https://www.papernot.fr/en/blog\n* https://gab41.lab41.org/\n* http://blog.smola.org/\n* http://mogren.one/blog/\n* http://www.alexirpan.com/ good batchnorm\n* https://machinethoughts.wordpress.com/\n* http://deepdish.io/page3/\n* https://github.com/ml4a ml for artists\n* https://severelytheoretical.wordpress.com/\n* https://codingmachinelearning.wordpress.com\n* https://kratzert.github.io/openlearning\n* https://recast.ai/\n* https://www.techemergence.com/artificial-intelligence-podcast/\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvyraun%2FMegalodon","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fvyraun%2FMegalodon","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvyraun%2FMegalodon/lists"}