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MEGALODON: ML/DL Resources At One Place

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://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/