https://github.com/vyraun/Megalodon
Various ML/DL Resources organised at a single place.
https://github.com/vyraun/Megalodon
deep-learning deep-neural-networks machine-learning
Last synced: 11 months ago
JSON representation
Various ML/DL Resources organised at a single place.
- Host: GitHub
- URL: https://github.com/vyraun/Megalodon
- Owner: vyraun
- Created: 2018-01-01T09:17:21.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2021-05-22T22:07:59.000Z (about 5 years ago)
- Last Synced: 2024-11-27T02:36:02.888Z (over 1 year ago)
- Topics: deep-learning, deep-neural-networks, machine-learning
- Homepage:
- Size: 64.5 KB
- Stars: 185
- Watchers: 19
- Forks: 43
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
## MEGALODON: ML/DL Resources At One Place ##
| Blogs | Type | Comments |
| ------------- |:-------------:| -----:|
| [Stanford NLP](https://nlp.stanford.edu/blog/) | Research exposition | |
| [Berkeley AI Research Lab (BAIR)](http://bair.berkeley.edu/blog/) | Research exposition | |
| [Off the Convex Path](http://www.offconvex.org/) | Research exposition | |
| [Andrej Karpathy blog](http://karpathy.github.io/), [Andrej Karpathy - Medium](https://medium.com/@karpathy)| Personal | |
| [Distill](https://distill.pub/) | Research exposition | |
| [Christopher Olah](http://colah.github.io/) | Personal | |
| [Sebastian Ruder](http://sebastianruder.com) | Personal | |
| [Elad Hazan](http://www.minimizingregret.com) | Personal | |
| [Ben Recht](http://www.argmin.net/2017/12/11/alchemy-addendum/)| Personal | |
| [Shakir Muhammed](http://blog.shakirm.com/)| Personal | |
| [Inference.vc](http://www.inference.vc/) | Personal | |
| [R2RT](http://r2rt.com/) | Personal | |
| [Pythonic Perambulations](https://jakevdp.github.io) | Personal | |
| [Sebastian Raschka](https://sebastianraschka.com/blog/index.html) | Personal | |
| [Papers wih Code](https://paperswithcode.com/)|||
| [Depth First Learning](http://www.depthfirstlearning.com/)|||
| [Moritz Hardt](http://blog.mrtz.org/)|||
| [MadryLab](https://gradientscience.org/)|||
| Podcasts/Talks | Type | Comments |
| ------------- |:-------------:| -----:|
| [Talking Machines](http://www.thetalkingmachines.com/) | Interviews/Research Exposition | |
| [Radim](https://soundcloud.com/piskvorky) | Interviews | |
| [The AI Podcast](https://soundcloud.com/theaipodcast) | Interviews | |
| [TWiML & AI](https://soundcloud.com/twiml) | Interviews | |
| [NLP-Highlights](https://soundcloud.com/nlp-highlights) | Interviews | |
| [The Thesis Review](https://cs.nyu.edu/~welleck/podcast.html) | Interviews | |
| [NLP with Friends](https://nlpwithfriends.com/past/) | Presentations | |
| [ML Street Talk](https://www.youtube.com/channel/UCMLtBahI5DMrt0NPvDSoIRQ) | Interviews | |
| [Pytorch-dev-podcast](https://pytorch-dev-podcast.simplecast.com/) | Talks | Pytorch Internals |
| [Stanford MLSys Seminar](https://www.youtube.com/channel/UCzz6ructab1U44QPI3HpZEQ) | Talks | |
| Books | Focus Areas | Comments |
| ------------- |:-------------:| -----:|
| [Pattern Recognition and Machine Learning](https://www.amazon.com/Pattern-Recognition-Learning-Information-Statistics/dp/0387310738) | | [MATLAB Code](http://prml.github.io/) |
| [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) | | |
| [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) | | |
| [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) | | |
| [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) | | |
| [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) | | |
| [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) | | |
| [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) | | |
| [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) | | |
| [Model Based Machine Learning](http://www.mbmlbook.com/) | | |
| [Neural Networks for Pattern Recognition](https://www.amazon.com/Networks-Pattern-Recognition-Advanced-Econometrics/dp/0198538642) | | |
| [Foundations Of Data Science](http://www.cs.cornell.edu/jeh/book%20June%2014,%202017pdf.pdf)| | [Lectures](https://www.youtube.com/watch?v=WEBUWYxaqLQ) |
| [A Course in Machine Learning](http://ciml.info/)| | |
| Monographs/Reports/Tutorials | Focus Areas | Comments |
| ------------- |:-------------:| -----:|
| [Algorithmic Aspects Of ML](http://people.csail.mit.edu/moitra/docs/bookex.pdf) | | [Videos](https://www.youtube.com/watch?v=nsHbkVMaUGk&list=PLB3sDpSRdrOvI1hYXNsa6Lety7K8FhPpx) |
| [Non Convex Optimization for ML](https://arxiv.org/abs/1712.07897) | | |
| [NMT AND Seq2Seq Models: A Tutorial](https://arxiv.org/abs/1703.01619) | | |
| [Intro to ML without Deep Learning](http://www.kyunghyuncho.me/home/blog/lecturenotebriefintroductiontomachinelearningwithoutdeeplearning) | | |
| [Frontiers in Massive Data Analysis]( https://www.nap.edu/read/18374/chapter/1) | | |
| [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)|
| Summer Schools/Seminars | Focus Areas | Comments |
| ------------- |:-------------:| -----:|
| [MLSS, Tubingen 07](http://videolectures.net/mlss07_tuebingen/) | | |
| [Cambridge](http://videolectures.net/mlss09uk_cambridge/) | | |
| [MLSS Purdue](https://www.youtube.com/playlist?list=PL2A65507F7D725EFB) | | |
| [DLSS, Montreal 2015](http://videolectures.net/deeplearning2015_montreal/) | | |
| [DLSS, Montreal 2016](http://videolectures.net/deeplearning2016_montreal/) | | |
| [Deep Learning School, 2016](https://www.youtube.com/watch?v=zij_FTbJHsk&list=PLrAXtmErZgOfMuxkACrYnD2fTgbzk2THW) | | [All Videos](https://www.youtube.com/watch?v=eyovmAtoUx0) |
| [DLSS & RLSS, Montreal 2017](http://videolectures.net/deeplearning2017_montreal/) | | |
| [MLSS, Kioloa 08](http://videolectures.net/mlss08au_kioloa/) | | |
| [MLSS, Chicago 09](http://videolectures.net/mlss09us_chicago/) | | |
| [MLSS, Canberra 02](http://videolectures.net/mlss02_canberra/) | | |
| [MSR India MLSS, 2015](https://www.microsoft.com/en-us/research/event/msr-india-summer-school-2015-on-machine-learning/) || |
| [AI Summer School, 2017](https://www.microsoft.com/en-us/research/event/ai-summer-school-2017/) | | |
| [Deep RL Bootcamp, Berkeley](https://sites.google.com/view/deep-rl-bootcamp/lectures) | | |
| [IPAM Deep Learning, Feature Learning, 2012](http://www.ipam.ucla.edu/programs/summer-schools/graduate-summer-school-deep-learning-feature-learning/?tab=schedule)| | |
| [MLSS, Max Plank Institute, 2017](https://www.youtube.com/watch?v=XugKv3lxQac) | | |
| [MLSS, CMU 2014](http://www.mlss2014.com/) | | |
| [Deep Learning: Theory, Algorithms, and Applications](https://www.youtube.com/playlist?list=PLJOzdkh8T5kqCNV_v1w2tapvtJDZYiohW) | | |
| [Gaussian Process Summer Schools](http://gpss.cc) | | |
| [MLSS, Iceland, 2014](https://www.youtube.com/watch?v=rcZHO2Lyd8Q&list=PLqdbxUnkqOw2nKn7VxYqIrKWcqRkQYOsF)| | |
|[MLSS Sydney 15](https://www.youtube.com/channel/UCT1k2e63pqm_VSXmaF21n6g/videos)|||
|[MLSS London 2019](https://search.videoken.com/?orgId=198#)|||
|[New Tech in Math Seminar](https://cmsa.fas.harvard.edu/tech-in-math/)|||
| Video Channels/Videos | Focus Areas | Comments |
| ------------- |:-------------:| -----:|
| [videolectures.net](videolectures.net) | | [ICLR 2016](http://videolectures.net/iclr2016_san_juan/) |
| [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) |
| [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/) |
| [Deep Learning Book Club](https://www.youtube.com/watch?v=vi7lACKOUao) | | [Deep learning book club](https://www.youtube.com/channel/UCF9O8Vj-FEbRDA5DcDGz-Pg/videos) |
| [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) |
| [Center for Brains, Minds and Machines (CBMM)](https://www.youtube.com/channel/UCGoxKRfTs0jQP52cfHCyyRQ/playlists) | | |
| [CVF](https://www.youtube.com/channel/UC0n76gicaarsN_Y9YShWwhw/playlists) | | |
| [CIS Lectures](https://www.youtube.com/watch?v=FD-DCpiRt4Q&list=PLjV5ChM0ZamXvq-wzo3zMFcb6ZI3r72CN) | | |
| [ICLR 2015](https://www.youtube.com/channel/UCqxFGrNL5nX10lS62bswp9w)| | |
| [IAS, Theoretical ML](https://www.ias.edu/ideas?aff=2)| | |
| [Formal and Applied Linguistics](http://lectures.ms.mff.cuni.cz/) | | |
| [ICLR 19](https://slideslive.com/iclr) | | |
| [David MacKay's Lectures](https://www.youtube.com/channel/UCfoScwn69ekXXWNTN0CLGXA)|||
| [ACL 2019](https://www.livecongress.it/sved/evt/aol_lnk.php?id=60B5FD70) |||
| [Allen AI](https://allenai.org/videos/videos-all-2019.html)|||
| General Resource Curations | Type | Comments |
| ------------- |:-------------:| -----:|
| [ML Videos](https://github.com/dustinvtran/ml-videos) | | |
| [Scholarpedia](http://www.scholarpedia.org/article/Main_Page) | | |
| [Short Science](http://www.shortscience.org/) | | |
| [Best Papers](http://jeffhuang.com/best_paper_awards.html) | | |
| [Pluralsight](https://www.pluralsight.com/search?q=machine%20learning) | | |
| [Safari Books Online](https://www.safaribooksonline.com/learning-paths/learning-path-machine/9781491987346/) | | |
| Specialized Resource Curations | Type | Comments |
| ------------- |:-------------:| -----:|
| [Meta-Learning Papers](https://github.com/songrotek/Meta-Learning-Papers) | | |
| [NLP Tasks](https://github.com/Kyubyong/nlp_tasks) | | |
| Academic Groups/Labs | Focus Areas | Comments |
| ------------- |:-------------:| -----:|
| [Saarland](http://www.ml.uni-saarland.de/code/IPM/IPM.htm) | | |
| [UFLDL](http://deeplearning.stanford.edu/wiki/index.php/UFLDL_Tutorial) | | |
| Industry Groups/Labs | Focus Areas | Comments |
| ------------- |:-------------:| -----:|
| [Microsoft](https://www.microsoft.com/en-us/research/blog/) | | |
| [Microsoft Maluuba](http://www.maluuba.com/blog/) | | |
| [Google Brain](https://research.google.com/teams/brain/) | | |
| [Facebook](https://research.fb.com/category/facebook-ai-research-fair/) | | |
| [Google Deepmind](https://deepmind.com/blog/) | | |
| [Apple](https://machinelearning.apple.com/) | | |
| [Recast AI](https://blog.recast.ai/category/machine-learning/) | NLP & Dialog Management |[API Reference](https://recast.ai/docs/api-reference/) |
| [Salesforce Einstein](https://einstein.ai/research) | | |
| Courses | Institute | Comments |
| ------------- |:-------------:| -----:|
| [Tensorfow for DL Research](https://web.stanford.edu/class/cs20si/index.html) | | General: [Advanced Scientific computing](https://am207.github.io/2017/material.html) |
| [Intro to AI, UCB](http://ai.berkeley.edu/lecture_videos.html) | | |
| [CNN for Visual Recognition](http://cs231n.stanford.edu/) | | |
| [Deep Learning for NLP](http://cs224d.stanford.edu/) | | |
| [Intro to Deep Learning, Princeton](https://www.cs.princeton.edu/courses/archive/spring16/cos495/) | | |
| [Intro to Deep Learning, MIT](http://introtodeeplearning.com/) | | |
| [NN for ML](https://www.coursera.org/learn/neural-networks) | | |
| [Stanford ML (Old)](https://www.youtube.com/watch?v=UzxYlbK2c7E), [Current](http://cs229.stanford.edu/) | | |
| [Probabilistic Graphical Models](https://www.coursera.org/learn/probabilistic-graphical-models#)| | |
| [Fast.AI](http://www.fast.ai/) | | |
| [Oxford Deep NLP, 17](https://github.com/oxford-cs-deepnlp-2017/lectures) | | |
| [Theories of deep learning](https://stats385.github.io/) | | [Videos](https://www.researchgate.net/project/Theories-of-Deep-Learning) |
| [Deep Learning System](http://dlsys.cs.washington.edu/) | | |
| [PGM](https://github.com/ermongroup/cs228-notes) | | [Flexible models of uncertainty](https://csc2541-f17.github.io/) |
| Frameworks/Libraries | Type | Comments |
| ------------- |:-------------:| -----:|
| [Tensorfow](https://www.tensorflow.org/) | | [TF Dev Summit, 17](https://www.youtube.com/watch?v=mWl45NkFBOc&list=PLOU2XLYxmsIKGc_NBoIhTn2Qhraji53cv) |
| [Theano](http://deeplearning.net/software/theano/) | | |
| [Lasagne](https://lasagne.readthedocs.io/en/latest/) | | |
| [Keras](https://keras.io/) | | |
| [CNTK](https://docs.microsoft.com/en-us/cognitive-toolkit/) | | |
| [MXNET](https://mxnet.incubator.apache.org/) | | |
| [Torch](http://torch.ch/) | | |
| [PyTorch](http://pytorch.org/) | | |
| [Caffe](http://caffe.berkeleyvision.org/) | | |
| [Caffe2](https://caffe2.ai/) | | |
| [Chainer](https://docs.chainer.org/en/stable/) | | |
| [DyNet](https://dynet.readthedocs.io/en/latest/) | | |
| [DL4J](https://deeplearning4j.org/) | | |
| [Scikit-learn](http://scikit-learn.org/) | | |
| [MALMO](https://www.microsoft.com/en-us/research/project/project-malmo/) | RL Environment | |
| [OpenAI Gym](https://gym.openai.com/docs/) | RL Environments | Not sure if still actively developed |
| [Gluon](http://gluon.mxnet.io/) | | |
| [ConvNetJS](https://github.com/karpathy/convnetjs) | | |
| [deeplearn.js](https://deeplearnjs.org/) | | |
| [Tangent](https://github.com/google/tangent) | Source to Source | |
| [Autograd](https://github.com/HIPS/autograd) | | [Torch-Autograd](https://github.com/twitter/torch-autograd) |
| Interviews | Focus Area | Comments |
| ------------- |:-------------:| -----:|
| [Deep Learning Heroes](https://www.youtube.com/watch?v=-eyhCTvrEtE&list=PLfsVAYSMwsksjfpy8P2t_I52mugGeA5gR) | | |
| Social Networks | Type | Comments |
| ------------- |:-------------:| -----:|
| [Twitter](https://twitter.com/) | | |
| [Reddit](reddit.com/r/MachineLearning/) | | Go to place for ml |
| [Hacker News](https://news.ycombinator.com/) | | |
| [Deep Learning Study Group, SF](https://www.meetup.com/deep-learning-sf/) | | |
| Newsletters | Focus Areas | Comments |
| ------------- |:-------------:| -----:|
| [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/) |
| [NLP News](http://newsletter.ruder.io/issues/nlp-news-nlp-for-beginners-dialogue-sentence-representations-64351) | | |
| [the morning paper](https://blog.acolyer.org/) | | |
| [ML Review](https://medium.com/mlreview) | | |
| [Import AI](https://jack-clark.net/about/) | | |
| [Gitxiv Newsletter](http://www.gitxiv.com/) | | |
| [Nathan Benaich](https://www.getrevue.co/profile/nathanbenaich/) | | |
| [O'reilly AI Newsletter](http://www.oreilly.com/ai/newsletter.html) | | |
| [Inside AI](https://inside.com/ai) | | |
| [Videolectures Digest](http://videolectures.net/) | | |
| Datasets | Task | Comments |
| ------------- |:-------------:| -----:|
| [NLP Datasets](https://github.com/karthikncode/nlp-datasets) | ||
### Other Blogs
* https://smerity.com/articles/articles.html
* http://veredshwartz.blogspot.in/
* https://stats385.github.io/blogs
* https://blogs.princeton.edu/imabandit/
* https://www.countbayesie.com
* http://building-babylon.net/
* [While My MCMC Gently Samples](http://twiecki.github.io/)
* http://www.marekrei.com/blog/online-representation-learning-in-recurrent-neural-language-models/
* http://mlg.eng.cam.ac.uk/yarin/blog.html
* https://blogs.msdn.microsoft.com/ericlippert/
* https://ericlippert.com/
* https://blogs.msdn.microsoft.com/ericlippert/tag/high-dimensional-spaces/
* http://blog.echen.me/
* radford neal's blog https://radfordneal.wordpress.com/
* http://timvieira.github.io/blog/archives.html
* http://p.migdal.pl/
* https://www.quora.com/What-are-the-best-machine-learning-blogs-or-resources-available
* http://ml.typepad.com/machine_learning_thoughts/
* https://jmetzen.github.io/
* http://peekaboo-vision.blogspot.in/
* http://sebastianruder.com/word-embeddings-1/index.html?utm_content=bufferca13e&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer
* https://jamesmccaffrey.wordpress.com/
* http://alexey.radul.name/2/
* https://www.reddit.com/r/MachineLearning/comments/4juw5z/cool_deep_learning_ml_blogs/
* http://dsnotes.com/
* http://mccormickml.com/
* http://approximatelycorrect.com/
* http://timdettmers.com/2015/03/26/convolution-deep-learning/
* https://campus2.acm.org/public/qj/brandingqj/xrds.cfm
* http://www.kemaswill.com/
* https://jacobgil.github.io/
* http://www.argmin.net/
* http://tscholak.github.io/
* https://theneuralperspective.com/
* https://devblogs.nvidia.com/parallelforall/
* http://textminingonline.com/
* http://douglasduhaime.com/blog/clustering-semantic-vectors-with-python
* https://telecombcn-dl.github.io/2017-dlcv/
* cs 231 http://cs231n.stanford.edu/, cs 221 http://web.stanford.edu/class/cs221/
* http://nlpforhackers.io/
* Keras, Torch, TF, http://dp.readthedocs.io/en/latest/index.html , Theano blogs are also very useful.
* http://gkalliatakis.com/blog/delving-deep-into-gans
* https://oshearesearch.com/
* https://www.youtube.com/watch?v=Xogn6veSyxA&list=PLbwivfGkPdvi4Pn66Yc8TWpNy18OhEhW_
* https://terrytao.wordpress.com/2017/03/01/special-cases-of-shannon-entropy/
* http://nlp.yvespeirsman.be/
* http://bcomposes.com/2015/11/26/simple-end-to-end-tensorflow-examples/
* https://prateekvjoshi.com/
* http://anie.me/
* http://wellredd.uk/
* http://p.migdal.pl/2017/04/30/teaching-deep-learning.html
* https://www.countbayesie.com/blog/2017/5/9/kullback-leibler-divergence-explained
* https://vkrakovna.wordpress.com/
* https://codingmachinelearning.wordpress.com/
* http://www.seaandsailor.com/index.html
* http://www.kentran.net/
* http://arogozhnikov.github.io/
* http://philipperemy.github.io/
* http://appliedpredictivemodeling.com/blog/2014/11/27/08ks7leh0zof45zpf5vqe56d1sahb0
* http://andymiller.github.io/blog/
* http://www.argmin.net/
* http://setosa.io/ev/
* http://rbharath.github.io/
* http://wiseodd.github.io/
* https://www.neurolab.de/cosine_notes.html
* http://willwolf.io/
* http://www.minimizingregret.com/
* http://bookworm.benschmidt.org/index.html
* http://www.brainyblog.net/
* https://iksinc.wordpress.com/
* https://erikbern.com/
* https://kevinzakka.github.io/2016/07/13/k-nearest-neighbor/
* http://dustintran.com/blog/
* http://blog.kaggle.com/
* http://jponttuset.cat/blog/
* http://blog.echen.me/2017/05/30/exploring-lstms/
* http://deliprao.com/archives/187
* http://www.ams.org/samplings/feature-column/fcarc-svd
* https://www.countbayesie.com/all-posts/
* http://briandolhansky.com/blog/2013/7/8/ml-primers
* http://iamtrask.github.io/
* https://hips.seas.harvard.edu/blog/
* https://jeremykun.com/
* https://theclevermachine.wordpress.com/
* http://nlp.yvespeirsman.be/blog/
* http://andrew.gibiansky.com/
* https://joanna-bryson.blogspot.de/
* http://tullo.ch/
* http://yanran.li/
* https://theneural.wordpress.com/
* http://jotterbach.github.io/archive/
* http://ischlag.github.io/
* http://www.marekrei.com/blog/
* http://alexhwilliams.info/itsneuronalblog/
* http://planspace.org/
* https://shapeofdata.wordpress.com/page/2/
* https://machinethoughts.wordpress.com/
* http://shubhanshu.com/blog/
* https://gmarti.gitlab.io/
* http://www.panderson.me/blog/
* http://giorgiopatrini.org/posts/2017/09/06/in-search-of-the-missing-signals/
* http://www-users.cs.umn.edu/~verma/blog.html
* https://www.papernot.fr/en/blog
* https://gab41.lab41.org/
* http://blog.smola.org/
* http://mogren.one/blog/
* http://www.alexirpan.com/ good batchnorm
* https://machinethoughts.wordpress.com/
* http://deepdish.io/page3/
* https://github.com/ml4a ml for artists
* https://severelytheoretical.wordpress.com/
* https://codingmachinelearning.wordpress.com
* https://kratzert.github.io/openlearning
* https://recast.ai/
* https://www.techemergence.com/artificial-intelligence-podcast/