awesome_stuff

It turns out that twitter only keeps links for a year. Which is super lame. So here is a living document of everything I find on the internet that is awesome.

https://github.com/ianozsvald/featherweight_web_api

-- automatically generate a web api for a python function

http://pytube.org/

--a site for talks about python

http://arxiv.org/abs/1606.03476

--an interesting paper about Generative Adversarial Imitation Learning

http://inhabitat.com/video-nikola-teslas-dream-is-finally-a-reality-with-wi-fi-powered-electronics/

--ambient backscatter - completely battery free technology

https://www.opendatascience.com/

--odsc blog posts

https://www.ices.utexas.edu/about/news/350/

--Navier-Stokes equations explained

http://www.darpa.mil/news-events/2016-06-17

--darpa is funding machine learning algorithms that generate machine learning algorithms.

http://arxiv.org/abs/1606.05340

--proof that deep networks can expose nonlinearities in nonlinear space, translating them into flat fields.

https://www.oreilly.com/learning/hello-tensorflow

--a great intro to tensorflow

https://www.facebook.com/quartznews/videos/1202874983079535/

--an ai algorithm that figures out what sound an object should make

https://golem.ph.utexas.edu/category/2016/06/how_the_simplex_is_a_vector_sp.html

-- how the simplex is a vector space

https://github.com/josephmisiti/awesome-machine-learning

-- awesome machine learning libraries list

https://www.safaribooksonline.com/library/view/python-cookbook-3rd/9781449357337/ch01s05.html

--implementation of a priority queue

https://www.cs.bris.ac.uk/~montanar/teaching/dsa/dijkstra-handout.pdf

-- dijkstra's with priority queue

http://docs.scala-lang.org/tutorials/scala-for-java-programmers.html

-- scala for java programmers

https://www.cs.cmu.edu/~rwh/theses/okasaki.pdf

--functional data structures

http://www.oreilly.com/programming/free/files/functional-programming-python.pdf

--functional programming in python

http://tinkersphere.com/stores

--where to get a raspberry pi in nyc

http://blog.smola.org/post/145983963411/leaving-cmu

--ml guy heads to amazon

http://exploreflask.readthedocs.io/en/latest/views.html

--interesting set of patterns for flask

http://stackoverflow.com/questions/29987323/how-do-i-send-data-from-js-to-python-with-flask

--flask from jquery

https://code.jquery.com/

--jquery cdn

http://stackoverflow.com/questions/1034621/get-current-url-in-web-browser

-- get current url from browser

http://stackoverflow.com/questions/558518/how-can-i-serialize-an-object-to-json-in-javascript

--object serialization in javascript

https://developer.mozilla.org/en-US/docs/Web/API/Geolocation/Using_geolocation

-- getting location from browser

https://pypi.python.org/pypi/honcho

--foreman clone in python

http://stackoverflow.com/questions/16086962/how-to-get-a-time-zone-from-a-location-using-latitude-and-longitude-coordinates

--an interesting discussion about timezones

https://teamtreehouse.com/community/nested-loops-in-flask-how-to-iterate-and-make-nested-lists

-- nested forloops in flask

https://medium.com/@handaru/build-recommendation-engine-using-graph-cbd6d8732e46#.y6b7vd4g3

--recommender engine with graphs

http://www.cs.yale.edu/homes/perlis-alan/quotes.html

--platitudes about programming

https://www.youtube.com/watch?v=3N__tvmZrzc

--programming languages class

http://stackoverflow.com/questions/32311366/alembic-util-command-error-cant-find-identifier

https://devcenter.heroku.com/articles/heroku-postgresql

--how to update your database with migrations when flask-migrate fails to work on heroku

http://www.techinsider.io/modafinil-is-an-effective-cognitive-enhancement-nootropic-2016-6?utm_content=buffer4e362&utm_medium=social&utm_source=facebook.com&utm_campaign=buffer-ti

--an interesting debate on intelligence enhancing drugs

https://medium.com/snips-ai/ntm-lasagne-a-library-for-neural-turing-machines-in-lasagne-2cdce6837315#.wiuyzrlri

--Neural turing machines

http://minimaxir.com/2016/06/interactive-reactions/

--interesting analysis of public facebook posts

http://www.slideshare.net/AperioIntel/financial-crime-in-the-real-estate-sector-countering-illicit-money-flows

-- how to detect money laundering, with examples

https://medium.com/data-science-cafe/apache-spark-1-6-0-setup-on-mac-os-x-yosemite-d58076e8064e#.2vggkt2n6

--spark setup

https://courses.edx.org/courses/course-v1:BerkeleyX+CS105x+1T2016/info

--spark course

http://bugra.github.io/work/notes/2014-04-19/alternating-least-squares-method-for-collaborative-filtering/

--collaborative filtering via alternating least squares with implementation in python

https://mathiasbynens.be/notes/shell-script-mac-apps

--appify your shell scripts

http://stackoverflow.com/questions/13636848/is-it-possible-to-do-fuzzy-match-merge-with-python-pandas

--fuzzy matching with python data frames

https://github.com/dgrtwo/fuzzyjoin

--fuzzy join in R

http://conferences.oreilly.com/strata/hadoop-big-data-ny/public/schedule/speakers

--strata hadoop speakers

http://conferences.oreilly.com/strata/hadoop-big-data-ny

--strata hadoop NY

http://conferences.oreilly.com/strata

--strata conf

https://www.odsc.com/boston

--odsc east

http://mlconf.com/events/new-york-city-ny/

--mlconf nyc

http://icml.cc/2016/?page_id=1519

--workshops at a glance

http://icml.cc/2016/?page_id=97

--tutorials at icml

http://icml.cc/2016/?page_id=1839

--schedule icml

http://techtalks.tv/icml2016/

--icml papers

https://sites.google.com/site/icmlworkshoponanomalydetection/

--anomaly detection workshop

https://spark.apache.org/docs/0.9.0/mllib-guide.html

--spark mllib docs

https://spark.apache.org/docs/0.9.0/python-programming-guide.html

--pyspark

https://www.youtube.com/watch?v=wmw8Bbb_eIE&app=desktop

--tensorflow intro

http://www.fastcompany.com/3059634/your-most-productive-self/your-brain-has-a-delete-button-heres-how-to-use-it

--your brain has a delete button

https://mlalgorithm.wordpress.com/2016/06/08/hierarchical-clustering/

--hierarchical clustering

https://github.com/unitedstates

--united states github

https://github.com/jmcarp/robobrowser

--bad ass web scraper

https://arxiv.org/abs/1606.09458

--ensemble voting methods

http://www.umiacs.umd.edu/~hal/docs/daume04rkhs.pdf

--math hardcore

http://colah.github.io/posts/2015-08-Understanding-LSTMs/

--lstm

http://lesswrong.com

--Bayesian salad

http://blog.socialcops.com/engineering/machine-learning-python?utm_source=facebook&utm_medium=social&utm_campaign=blog_share

--from nothing to nn's

https://dataorigami.net/blogs/napkin-folding/19055451-percentile-and-quantile-estimation-of-big-data-the-t-digest

--compute the median fast

http://machinelearningmastery.com/applied-deep-learning-in-python-mini-course/

--deep learning at breakneck speed

http://highscalability.com/blog/2016/7/6/machine-learning-driven-programming-a-new-programming-for-a.html

--deep learning for code generation

https://www.whitehouse.gov/the-press-office/2016/06/30/fact-sheet-launching-data-driven-justice-initiative-disrupting-cycle

--AI and justice from the whitehouse

http://earthmysterynews.com/2016/05/05/physicists-send-particles-of-light-into-the-past-proving-time-travel-is-possible/

--an experiment confirming that time travel is possible

https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearning/

--theoretical machine learning course

http://ec2-52-51-244-37.eu-west-1.compute.amazonaws.com

--narrative flow analysis with som

http://www.analyticbridge.com/m/group/discussion?id=2004291%3ATopic%3A304182

--data science book

http://www.datasciencecentral.com/profiles/blogs/machine-learning-anomaly-detection-finding-a-needle-in-a-haystack?overrideMobileRedirect=1

--anomaly detection

http://www.wired.com/2016/05/google-open-sourced-syntaxnet-ai-natural-language/?utm_content=buffer7037c&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer

--syntax net is open source!

http://www.deepgram.com

--audio api

https://www.opendatascience.com/blog/understanding-principal-component-analysis/

--PCA explained

http://stats.stackexchange.com/questions/8000/proper-way-of-using-recurrent-neural-network-for-time-series-analysis

--great description of RNNs for time series (what they are not)

http://science.tumblr.com/post/147401742140/the-most-beautiful-equation

--recursion

https://magenta.tensorflow.org/2016/07/15/lookback-rnn-attention-rnn/

--rnn applications

https://github.com/llllllllll/lazy_python and https://github.com/llllllllll/codetransformer

-- hacking python for fun and profit!

http://tech.magnetic.com/

-- good blog

http://tech.magnetic.com/2016/04/demystifying-logistic-regression.html

--simple intro to logistic regression and ML

https://github.com/mcraig2/pygotham-talk/blob/master/tflow.ipynb

--tensorflow intro

https://pypi.python.org/pypi/ad/1.3.2

--automatic differentiation

https://pypi.python.org/pypi/yappi

--profiler for python

http://kcachegrind.sourceforge.net/html/Home.html

--visualize the profiling from yappi

http://mike.place/talks/pygotham/#1

--document summarization

https://www.youtube.com/watch?v=0VTI1BBLydE

--stanford music generation with RNNs

https://github.com/MattVitelli/GRUV

--source code

http://oubiwann.blogspot.com/2014/07/oscon-2014-theme-song-andrew-sorensen.html

--andrew sorenson keynote on music generation

http://pybee.org/

-- for mobile development

https://github.com/spotify/annoy

--nearest neighbor implementation

http://www.cs.cmu.edu/~ggordon/singh-gordon-kdd-factorization.pdf

--collective matrix factorization

http://videolectures.net/cmulls08_singh_rlm/

--collective matrix factorization

http://www.benjamintd.com/blog/spynet/

an rnn that writes Python!

http://askubuntu.com/questions/761180/wifi-doesnt-work-after-suspend-after-16-04-upgrade

-- fix wifi issue

https://www.opendatascience.com/blog/the-forgotton-optimization-topic-set-diversity/

--optimization texhnique

https://adeshpande3.github.io/adeshpande3.github.io/A-Beginner's-Guide-To-Understanding-Convolutional-Neural-Networks/

--conv net theory

https://www.quora.com/How-can-I-prepare-myself-to-be-a-software-engineer-at-Google/answer/Gaurav-Jha-9?srid=0c9s

http://multithreaded.stitchfix.com/blog/2016/07/21/skynet-salesman/

--RL deep Q

https://github.com/deepmind/rc-data

--deep learning language data set

https://github.com/rouseguy/europython2016_dl-nlp/tree/master/notebooks

--deep learning language nlp

https://medium.com/@ageitgey/machine-learning-is-fun-part-4-modern-face-recognition-with-deep-learning-c3cffc121d78#.wp3bwd9ez

--svm face rec

https://www.reddit.com/r/textdatamining/

--textmining reddit

http://arxiv.org/abs/1503.04069

--an analysis of LSTM

https://web.stanford.edu/~arbenson/cme193.html

--scientific computation in python

http://cs231n.github.io/

--stanford convolutional neural networks course with numpy

http://jvns.ca/

-- a very awesome blog

https://github.com/tzutalin/labelImg

--graphical label annotation

https://www.nyu.edu/projects/bowman/bowman2016phd.pdf

--modeling natural language with learned representations

https://www.mapr.com/blog/design-patterns-recommendation-systems-%E2%80%93-everyone-wants-pony

--recommender system

https://www.technologyreview.com/s/539706/how-the-new-science-of-game-stories-could-change-the-future-of-sports/?utm_campaign=socialflow&utm_source=facebook&utm_medium=post

--interesting analysis and visualization of stories

http://buff.ly/2b5wvMm

--3D modeling in Python

http://www.rightrelevance.com/search/articles/hero?article=bb58e4504d119319a294fd269b5e1f61558cb26a&query=particle%20physics&taccount=parrticlephy

--simple flow equation

https://github.com/EricSchles/paper-notes

--from kapathary, looks super cool

https://www.technologyreview.com/s/601774/data-mining-reveals-the-crucial-factors-that-determine-when-people-make-blunders/?utm_content=buffer2f2d5&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer

--how decisions are made

https://github.com/EricSchles/drmad

--hyper parameter tuning, some folks on reddit seem to think this is yet another useless technique.

https://github.com/EricSchles/reddit_crawlers

--reddit crawler that for some reason has a serious deep learning component, worth investigating

https://github.com/dyelax/Adversarial_Video_Generation/tree/master/Code

--an implementation of adversarial networks! Definitely need to read through in detail

http://sebastianraschka.com/blog/2016/model-evaluation-selection-part2.html

--part of a series on model selection, looks pretty good.

http://www.futurecrunch.com.au/writing/

--political economy writing

http://chrisalbon.com/

--sane examples of pandas and R

https://qbox.io/blog/sparse-matrix-multiplication-elasticsearch-apache-spark

--elasticsearch matrix multiplication

http://www.rightrelevance.com/search/articles/hero?article=5fb7a116712286ad60484e7f05d4fdeb75e26454&query=artificial%20intelligence&taccount=ml_toparticles

-- machine learning for first responders

http://blog.getstream.io/fast-recommendations-for-activity-streams-using-vowpal-wabbit?utm_content=buffera5da5&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer

--vopal wabbit

http://mike.place/talks/pygotham/#p1

--Document summarization

http://github.com/coxlab/prednet --recurrent convolutional net

https://github.com/MacLeek/trackmac --tracking project time on mac

https://github.com/HackerHouseYT/Smart-Mirror --smart miror w/ raspbery pi

http://distill.pub/2016/augmented-rnns/ --RNNs

https://medium.com/@USCTO/public-input-and-next-steps-on-the-future-of-artificial-intelligence-458b82059fc3#.vad6ol11a --interesting read on ML

http://blog.quantopian.com/optimize_capacity/ --sharpe Ratio

https://unu.edu/fighting-human-trafficking-in-conflict --human trafficking in conflict

https://www.datacamp.com/courses/intro-to-python-for-data-science?utm_content=buffer556a6&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer --data camp python class

http://www.aosabook.org/en/500L/a-python-interpreter-written-in-python.html --python interpretter written in python

http://sunlightfoundation.com/blog/2016/09/08/today-in-opengov-the-future-of-the-us-city-open-data-census-first-us-ciso-and-more/ --open data

https://deepmind.com/blog/wavenet-generative-model-raw-audio/ --wave net for audio

https://medium.com/@ageitgey/machine-learning-is-fun-part-5-language-translation-with-deep-learning-and-the-magic-of-sequences-2ace0acca0aa#.3e9v5nggx --machine language translation

http://www.rightrelevance.com/search/articles/hero?article=e036c156aa408a235aa740162e3b1cfd2e0e985c&query=python&taccount=pythonrr --python intel distro

http://fusion.net/story/344884/sex-slave-bars-in-united-states/ --great set of visuals about human trafficking

https://m.youtube.com/playlist?list=PLmImxx8Char9Ig0ZHSyTqGsdhb9weEGam And https://m.youtube.com/watch?v=sU_Yu_USrNc --Stanford nlp lectures

http://www.rightrelevance.com/search/articles/hero?article=4c40ce09cb544b00b68580b7866fe18ce48a27eb&query=python&taccount=pythonrr --sandman library

https://www.facebook.com/inthenow/videos/681969348620104/ --ambulance drone

http://www.pyimagesearch.com/2016/09/26/a-simple-neural-network-with-python-and-keras/ --minimal neural network with Keras

https://github.com/datascopeanalytics/traces --uneven time series analysis

https://blog.monkeylearn.com/the-definitive-guide-to-natural-language-processing/ --high level walk through of NLP concepts

https://www.yhat.com/ops-demos/ --ml demos with keras / opencv

http://bit.ly/2eNfcOs --wrapper around Google charts API

https://github.com/metagrover/ES6-for-humans --a good set of descriptions of javascript conventions, symbols and syntax

https://github.com/wireservice/agate --data discovery tool

http://www.primaryobjects.com/2013/01/27/using-artificial-intelligence-to-write-self-modifying-improving-programs/ --program that generates code

http://textminingonline.com/getting-started-with-word2vec-and-glove-in-python --word2vec vs GloVe

https://mostafa-samir.github.io/ml-theory-pt3/ --an introduction to bias variance trade off

https://www.opendatascience.com/blog/bayesian-deep-learning/ and https://www.opendatascience.com/blog/bayesian-deep-learning-part-ii-bridging-pymc3-and-lasagne-to-build-a-hierarchical-neural-network/ --neural nets and bayesian thinking

https://inviqa.com/blog/graphs-database-sql-meets-social-network --loops in SQL, graph traversal in SQL

https://blog.bigchaindb.com/blockchains-for-artificial-intelligence-ec63b0284984#.dzilfvdfq --blockchain ml

http://pytorch.org/ --neual nets

https://engineering.instagram.com/dismissing-python-garbage-collection-at-instagram-4dca40b29172#.75j94rygt --Python Garbage Collection

http://dustintran.com/talks/Tran_Edward.pdf --probability modeling

https://www.r-bloggers.com/outlier-detection-with-mahalanobis-distance/ --outlier detection

http://yann.readthedocs.io/en/master/ --yet another neuaral network library

https://arxiv.org/abs/1701.06538?utm_content=buffer26227&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer --flow of control in neural networks

http://peterdowns.com/posts/first-time-with-pypi.html --making a pypi package

https://github.com/lenagroeger/gifs --data visualization gifs

https://github.com/mbernico/snape --realistic dummy data for testing algorimths.

https://research.fb.com/prophet-forecasting-at-scale/ --facebook forecasting library

sudo killall coreaudiod -- for when screen hero audio doesn't work

https://pypi.python.org/pypi/ERAlchemy --Create ER diagrams "for free"

http://students.brown.edu/seeing-theory/?vt=4 --visual descriptions of basic probability

https://blog.dominodatalab.com/fitting-gaussian-process-models-python/ --gaussian processes for prediction in python

http://www.kdnuggets.com/2017/03/yhat-beginner-guide-customer-segmentation.html --pedogogical intro to clustering

http://dan.iel.fm/emcee/current/user/line/ --parameter estimation with MCMC

http://nipy.org/nitime/api/generated/nitime.timeseries.html --time series analysis

http://fb09-pasig.umwelt.uni-giessen.de/spotpy/ --spotpy docs for doing simulation of data

https://github.com/slavivanov/Style-Tranfer --style transfer code with a conv net in keras

http://www.datasciencecentral.com/profiles/blogs/top-10-ipython-tutorials-for-data-science-and-machine-learning --whole bunch of ml notebooks

https://arstechnica.co.uk/information-technology/2017/03/google-jpeg-guetzli-encoder-file-size/ --file compression.

https://blog.jisungkim.com/machine-learning-and-art-9ea2c9342180#.2ve57gv6f -- art and ml examples

http://www.kdnuggets.com/2017/03/simple-xgboost-tutorial-iris-dataset.html?utm_content=buffer8924b&utm_medium=social&utm_source=facebook.com&utm_campaign=buffer --xgboost tutorial python

http://blog.yhat.com/posts/python-generated-powerpoint.html --power point generator

https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Scikit_Learn_Cheat_Sheet_Python.pdf -scikit learning cheat sheet

http://deeplearning.net/tutorial/deeplearning.pdf --deep learning in python book theano numpy

http://www.markhneedham.com/blog/2017/03/25/luigi-externalprogramtask-example-converting-json-csv/ --luigi intro

https://github.com/fchollet/keras-resources --keras resources

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5016890/pdf/12859_2016_Article_1236.pdf -- generalized logistic regression

https://github.com/rajshah4/image_keras -- image classification

http://www.pyimagesearch.com/2017/04/17/real-time-facial-landmark-detection-opencv-python-dlib/ --facial recognition for video

https://tech-forward-2.glitch.me/ --list of awesome tech orgs

http://www.datasciencecentral.com/profiles/blogs/introduction-to-outlier-detection-methods?utm_content=buffer0fb5c&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer --an introduction to outlier detection

http://theorangeduck.com/page/phase-functioned-neural-networks-character-control?utm_content=buffereda7e&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer --phase function neural networks - might be useful for timeseries

https://www.quantinsti.com/blog/trading-using-machine-learning-python/#DataScience -- timeseries prediction in data science parlence.

https://github.com/wayaai --a very cool deep learning company

https://gist.github.com/baraldilorenzo/07d7802847aaad0a35d3 --how to work with keras and VGG16 (also from keras.applications.vgg16 import VGG16; model = VGG16())

http://www.kdnuggets.com/2017/04/ai-maturity-model.html --maturity model

https://medium.com/airbnb-engineering/automated-machine-learning-a-paradigm-shift-that-accelerates-data-scientist-productivity-airbnb-f1f8a10d61f8?from=timeline&isappinstalled=0 --artificial intelligence automation

http://p.migdal.pl/2017/04/30/teaching-deep-learning.html --deep learning keras intro

https://www.xenonstack.com/blog/overview-of-artificial-intelligence-and-role-of-natural-language-processing-in-big-data --great nlp

https://www.quantinsti.com/blog/trading-using-machine-learning-python/#DataScience -- timeseries in python

http://www.kdnuggets.com/2017/03/naive-sharding-centroid-initialization-method.html?utm_content=buffer45425&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer -- k-means improvement

http://www.datasciencecentral.com/profiles/blogs/10-deep-learning-terms-explained-in-simple-english?utm_content=buffer6e829&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer -- list of machine learning terms

http://flowingdata.com/2017/05/02/shifting-incomes-for-young-people/ --job data

http://www.rightrelevance.com/search/articles/hero?article=b8c3fc25c7f0238393be0d0ad4fc93fa074be5f6&query=data%20science&taccount=ml_toparticles --mortality data

http://cmawer.github.io/trainspotting/trainspotting-blog.html --train detection and direction detection

http://www.kdnuggets.com/2017/04/datascience-introduction-anomaly-detection.html --anamoly detection

https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python?utm_content=buffer85c3f&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer --kalman and bayesian filters in python

http://www.kdnuggets.com/2016/06/open-source-machine-learning-degree.html?utm_content=bufferea858&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer -- open source data science degree

https://medium.com/merantix/picasso-a-free-open-source-visualizer-for-cnns-d8ed3a35cfc5?platform=hootsuite --cnn visualizer

https://medium.com/intuitionmachine/deep-adversarial-learning-is-finally-ready-and-will-radically-change-the-game-f0cfda7b91d3 -- good basic description of generative adversarial neural networks.

http://www.pyimagesearch.com/2016/08/10/imagenet-classification-with-python-and-keras/ --keras image processing tutorial

https://www.datascience.com/resources/tools/skater -- model interpretation library

http://www.datasciencecentral.com/profiles/blogs/how-to-tell-a-compelling-story-with-data-6-rules-6-tools?overrideMobileRedirect=1 --telling data stories

https://github.com/gaojiuli/tomd --converts HTML into markdown

https://medium.com/@karpathy/alphago-in-context-c47718cb95a5 --super awesome description of AlphaGo

https://opendatascience.com/rec-system/?utm_content=52586516&_hsenc=p2ANqtz-9jGizLlpsoa76ETOX2LRnsRKzzER0lIeENGuQuIvUflcllijdwfT6L6w-md3zQOEiTZp3xaIy1l0CsoeDgKVLRhzkPKg&_hsmi=52595398 --recommender system intro in Python

https://opendatascience.com/blog/factorization-machines-for-recommendation-systems/?utm_campaign=Newsletters&utm_source=hs_email&utm_medium=email&utm_content=52586516&_hsenc=p2ANqtz-_Vr8oIhp5ceuxkCEIrj9ccwSKBPIedXDF0ORf1j2E1dN6JzTR1RwAlSNVTU-eb6uHdMS4secVkw0s5ryG5qne6SioKVg&_hsmi=52595398 --more recommender stuff in Python

https://opendatascience.com/time-series-analysis-with-generalized-additive-models/?utm_content=52586516&_hsenc=p2ANqtz-9oWCL-QDRrDQOcDJdmmzUvRdBBnRf_L8cn5epiWWHWOdOVzwCEcWZUP8U-Hv6ZoUI1hrzfyt-Vf7jlEoFjxoqR7FeIGg&_hsmi=52595398 --timeseries analysis with additive models

http://babble-rnn.consected.com/docs/babble-rnn-generating-speech-from-speech-post.html --speech processing in keras

https://github.com/ZWMiller/svdRec -- recommender system with SVD

http://blog.echen.me/2017/05/30/exploring-lstms/?utm_content=bufferb0490&utm_medium=social&utm_source=linkedin.com&utm_campaign=buffer -- recurrent neural networks in java

https://github.com/aredridel/how-to-read-code/blob/master/how-to-read-code.md -- how to read code

https://2016.foss4g-na.org/sites/default/files/slides/FOSS4G_machine_learning.pdf -- ml and geospatial

https://github.com/EricSchles/reveal.js --js slides in browser

https://hilaryparker.com/ -- R programmer worth following

https://github.com/starcolon/vor-knowledge-graph -- open knowledge graph generator from wikipedia

https://en.wikipedia.org/wiki/AIML -- AI markup language

http://python-for-multivariate-analysis.readthedocs.io/a_little_book_of_python_for_multivariate_analysis.html -- a fantastic introduction to multivariate analysis with a great explanation of LDA, PCA

https://help.gooddata.com/display/doc/Normality+Testing+-+Skewness+and+Kurtosis --understanding the results of the normal test in scipy

http://www.statisticssolutions.com/correlation-pearson-kendall-spearman/ -- understanding different correlation tests

http://nbviewer.jupyter.org/gist/aflaxman/6871948 -- understanding bootstraping

http://www.stat.pitt.edu/stoffer/tsa4/tsaEZ.pdf --introduction to timeseries analysis

http://arch.readthedocs.io/en/latest/index.html --advanced timeseries modeling

http://machinelearningmastery.com/time-series-prediction-lstm-recurrent-neural-networks-python-keras/ -- timeseries modeling with keras

http://machinelearningmastery.com/time-series-forecasting-long-short-term-memory-network-python/ -- timeseries modeling with keras (part 2)

https://www.amazon.com/Deep-Time-Forecasting-Python-Introduction-ebook/dp/B01N100IPR -- timeseries forecasting with keras book

http://www.stata.com/meeting/5nasug/TSFiltering_beamer.pdf --band filtering

https://thehackerdiary.wordpress.com/2017/06/09/it-is-ridiculously-easy-to-generate-any-audio-signal-using-python/ --make music with Python

https://semaphoreci.com/community/tutorials/generating-fake-data-for-python-unit-tests-with-faker -- a pretty decent data faking package.

https://sflscientific.com/blog/2017/2/10/predicting-stock-volume-with-lstm -- stockmarket analysis with RNNs

https://kndrck.co/indexing-faces-on-instagram.html --horrifying and creepy, but useful in the anti trafficking context - scraping faces from instagram

https://serverlesscode.com/post/rich-jones-interview-django-zappa/ -- AWS lambda

http://www.kdnuggets.com/2017/03/working-numpy-matrices.html -- tiny intro to numpy

https://github.com/rigetticomputing/pyquil -- quantum cloud computing library for python

http://blog.aylien.com/understanding-customer-frustrations-in-the-airline-industry-with-aspect-based-sentiment-analysis/ -- aspect based sentiment analysis

https://github.com/DistrictDataLabs/yellowbrick -- Visual analysis and diagnostic tools to facilitate machine learning model selection

https://github.com/DistrictDataLabs/partisan-discourse -- build your own nlp corpus

https://pypi.python.org/pypi/baleen/0.3.3 -- build your own nlp corpus

https://github.com/DistrictDataLabs/minke -- nlp feature extractor w/ metadata

https://github.com/ethereum/pyethereum --python interface for ethereum

https://www.wired.com/2016/01/use-code-to-create-sweet-3-d-visualizations-of-electric-fields/ --3-D models

https://www.youtube.com/watch?v=oNf3I1fVmg8&feature=share --tensorflow, spark, advanced algebra things

https://github.com/meetshah1995/pytorch-semseg --semantic image segmentation

https://github.com/bokeh/datashader?utm_content=buffera606e&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer -- shards big data correctly and geomaps it

http://www.kdnuggets.com/2017/07/strange-loop-deep-learning.html?utm_content=bufferdb453&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer --ladder networks explained.

https://github.com/mehrdadn/SOTA-Py?utm_content=bufferd4663&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer --routing problem algorithm - "How do you travel from point A to point B in T time under traffic?"

https://github.com/reinforceio/tensorforce -- deep reinforcement learning

http://tensorflow-world-resources.readthedocs.io/en/latest/ --tensorflow intro

https://research.googleblog.com/2017/07/facets-open-source-visualization-tool.html?m=1 -- data viz library of winning and awesomeness.

http://bair.berkeley.edu/blog/2017/07/18/learning-to-learn/ -- learning to learn

https://engineering.upside.com/a-beginners-guide-to-optimizing-pandas-code-for-speed-c09ef2c6a4d6 -- pandas optimizations

https://www.technologyreview.com/s/608387/an-algorithm-trained-on-emoji-knows-when-youre-being-sarcastic-on-twitter/?set=608492 -- detecting sarcasm with emojis

https://github.com/blue-yonder/tsfresh --feature extraction for timeseries

https://blog.slavv.com/37-reasons-why-your-neural-network-is-not-working-4020854bd607 -- super good read - debugging neural networks.

https://stats.stackexchange.com/questions/18891/bagging-boosting-and-stacking-in-machine-learning -- boosting bagging and stacking explained!

https://www.buzzfeed.com/peteraldhous/hidden-spy-planes?utm_term=.uu8969pK9#.krQ0O0qe0 -- geo classification example

https://www.analyticsvidhya.com/blog/2017/08/catboost-automated-categorical-data/?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3A+AnalyticsVidhya+%28Analytics+Vidhya%29 -- how to use categorical boosting library

https://web.stanford.edu/~hastie/CASI_files/PDF/casi.pdf -- a good general book on data science

https://labs.eleks.com/2016/10/combined-different-methods-create-advanced-time-series-prediction.html -- a good use of timeseries techniques

https://repositorio-aberto.up.pt/bitstream/10216/82298/2/37884.pdf -- spatial timeseries data analysis book

https://pdfs.semanticscholar.org/cb6d/e3eeb810a5fe3341118b492aa94ecd5b8c83.pdf -- timeseries analysis

https://medium.com/towards-data-science/gradient-descend-with-free-monads-ebf9a23bece5 -- gradient descent in scala

http://www.paddlepaddle.org/ --baidu's deep learning library

https://ringtheory.herokuapp.com/ -- ring theory database.

https://medium.com/twentybn/visual-explanation-for-video-recognition-87e9ba2a675b -- categorizing actions

https://github.com/adebayoj/fairml -- detect racial bias

https://oneraynyday.github.io/2017/08/20/VC-Dimensions/ -- statistical learning blog

https://machinelearning.apple.com/2017/08/06/siri-voices.html?utm_content=buffer1ad8c&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer -- text to speech generation

https://twitter.com/planarrowspace/status/901480960587218944/photo/1 -- reinforcement learning

https://hackernoon.com/docker-compose-gpu-tensorflow-%EF%B8%8F-a0e2011d36 --GPU + Docker + tensorflow

http://www.datasciencecentral.com/profiles/blogs/comprehensive-repository-of-data-science-and-ml-resources?utm_content=buffer6ddaa&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer -- list of lists of data science things

http://nuit-blanche.blogspot.fr/2017/08/projectionnet-learning-efficient-on.html?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed:+blogspot/wCeDd+(Nuit+Blanche --projection networks - compressing large network architectures

http://nuit-blanche.blogspot.fr/2017/08/videos-deep-learning-dlss-and.html?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed:+blogspot/wCeDd+(Nuit+Blanche -- reinforcement learning videos

http://allendowney.blogspot.com/2015/05/hypothesis-testing-is-only-mostly.html --the true value of computing the p-value. This is very interesting because it gives us not only the use-case of the p-value but also a path forward to test for bias as well.

https://gmarti.gitlab.io/ml/2017/09/07/how-to-sort-distance-matrix.html --agglomerative clustering algorithm visualization in action! The idea here is that by first sorting data according to the hierarchical algorithm you can produce a strong and intuitive clustering visualization of your data.

https://medium.com/towards-data-science/a-brief-overview-of-outlier-detection-techniques-1e0b2c19e561 -- outlier detection - covers a nice overview including three specific examples - z-score, dbscan and isolation forrests. Unfortunately doesn't cover the rest of the types of algorithms that are mentioned in the high level overview.

https://medium.com/towards-data-science/deep-learning-for-object-detection-a-comprehensive-review-73930816d8d9 -- A good explanation of the current state of the art for image classification. This article like most of the articles of this kind cover three techniques - R-CNN, Faster-R-CNN and SSD. The computational architecture of each model is explained and some mention of where you might find these models, namely tensorflow is mentioned. They all seem to have similar performance in terms of accuracy. The main area of interest in this article was speed - how fast do the algorithms run. This may appear to be a subtle shift, but typically image classification algorithm explainations of read in the past have only been concerned with performance in terms of accuracy. The fact that folks are now more concerned with speed means we are hitting the upper limit of accuracy.

https://dzone.com/articles/machine-learning-measuring -- a good set of distance metrics used in machine learning problems.

http://goodtables.okfnlabs.org/ -- data validation

https://userinput.io/#/#examples -- userinput testing

https://blog.openai.com/unsupervised-sentiment-neuron/ -- really good sentiment classifier

https://machinelearningmastery.com/transduction-in-machine-learning/ -- transduction defined

https://www.digitaltrends.com/business/washington-post-robot-reporter-heliograf/?utm_content=buffer20089&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer --an article on how machines write our news now

https://nlml.github.io/in-raw-numpy/in-raw-numpy-t-sne/ -- a great introduction to t-SNE

https://www.analyticsvidhya.com/blog/2017/09/pseudo-labelling-semi-supervised-learning-technique/?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3A+AnalyticsVidhya+%28Analytics+Vidhya%29

https://security-informatics.springeropen.com/articles/10.1186/s13388-017-0029-8 -- two articles on semi supervised learning

https://www.twilio.com/blog/2017/08/geospatial-analysis-python-geojson-geopandas.html -- a super good intro to geospatial analysis in python

https://github.com/dwillis/nyc-maps.git --nyc maps in geojson format

http://jose-coto.com/plotting-geopandas --an awesome analysis of plotting points with a geometry

https://www.datacamp.com/community/tutorials/preprocessing-in-data-science-part-2-centering-scaling-and-logistic-regression#gs.jzWZFRU -- a good analysis of the trade off between logistic regression and k-nearest-neighbors. Knn needs data to scale, logistic regression will do about the same, even with scaled data.

https://monkeylearn.com/blog/beginners-guide-text-vectorization/ -- some text classification stuff. specifically skip thought vectors versus bag of words and then joining the techniques together for better performance.

https://hackernoon.com/machine-learning-with-javascript-part-1-9b97f3ed4fe5 -- machine learning tutorial in javascript

http://www.kdnuggets.com/2017/10/upcoming-meetings-analytics-big-data-science-machine-learning.html?utm_content=buffer3ed10&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer --a good explanation of boosting weak classifiers. covers gradient boosting and extreme boosting (xgboost)

http://www.kdnuggets.com/2017/10/understanding-machine-learning-algorithms.html?utm_content=buffer559a8&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer -- a good overview of decision trees, random forests, support vector machines, and neural networks. The kernal trick of svms is well explained, finally.

https://dzone.com/articles/breakthrough-research-papers-and-models-for-sentim -- neural network sentiment analysis

http://stackabuse.com/parallel-processing-in-python/ -- a good introduction to parallel processing

https://jtsulliv.github.io/stock-movement/?utm_content=buffer0d87f&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer --a good introduction to brownian motion and Euler-Maruyama Model time series analysis

https://www.oreilly.com/ideas/deep-matrix-factorization-using-apache-mxnet -- recommender systems

https://becominghuman.ai/following-messi-with-tensorflow-and-object-detection-20ba6d75667 -- custom object detection in video using tensorflow

https://chatbotnewsdaily.com/since-the-initial-standpoint-of-science-technology-and-ai-scientists-following-blaise-pascal-and-804ac13d8151 -- a nice little history for machine learning

http://www.bodowinter.com/tutorial/bw_LME_tutorial1.pdf -- a good introduction to fixed effects

http://www.bodowinter.com/tutorial/bw_LME_tutorial2.pdf -- a good introduction to mixed effects

https://medium.com/towards-data-science/squeeze-and-excitation-networks-9ef5e71eacd7 -- holy crap! 25% performance jump on imagenet

https://github.com/MaxHalford/xam -- interesting ml toolbox

https://journals.aps.org/pra/abstract/10.1103/PhysRevA.96.042113 -- solving problems in physics with precision without an analytic form.

https://wxs.ca/research/multiscale-neural-synthesis/?utm_content=buffer08f81&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer --this is cool multiscale neural style synthesis

https://medium.com/emergent-future/simple-reinforcement-learning-with-tensorflow-part-8-asynchronous-actor-critic-agents-a3c-c88f72a5e9f2 --Good intro to state of art in Reinforcement learning

https://github.com/asktree/Asymmetric-Hashing-ANN -- asymmetric hashing algorithm from google - uses asymmetric hashing and beam search to speed up automatic reply

https://www.pyimagesearch.com/2017/10/30/how-to-multi-gpu-training-with-keras-python-and-deep-learning/ -- a good introduction to multiple gpu training for keras

https://medium.com/towards-data-science/the-10-statistical-techniques-data-scientists-need-to-master-1ef6dbd531f7 -- some good model introspection techniques here, also a good basic understanding of splines, PCR and PLS

https://dzone.com/articles/optimizing-k-means-clustering-for-time-series-data -- time series k-means clustering

https://medium.com/towards-data-science/15-stunning-data-visualizations-and-what-you-can-learn-from-them-fc5b78f21fb8 -- a good intro to data visualization best practice

https://twitter.com/AllenDowney/status/926960793261928449 -- an introduction to bell's inequality

https://www.newnorth.com/creating-a-predictive-churn-mode-part-1l/ -- churn modeling basics

https://www.datascience.com/blog/what-is-a-churn-analysis-and-why-is-it-valuable-for-business -- churn modeling modeling high level

http://blog.yhat.com/posts/predicting-customer-churn-with-sklearn.html -- modeling churn with scikit

https://github.com/aloctavodia/Statistical-Rethinking-with-Python-and-PyMC3 -- bayesian book

https://petewarden.com/2017/10/29/how-do-cnns-deal-with-position-differences/?utm_content=bufferd86a2&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer -- convolutional neural networks introduced in a detailed way.

https://github.com/tomlepaine/fast-wavenet --fast convnets for timeseries analysis

https://medium.com/@keeper6928/how-to-unit-test-machine-learning-code-57cf6fd81765 -- how to test machine learning code

https://machinelearningmastery.com/prepare-photo-caption-dataset-training-deep-learning-model/ -- captioning text for images

https://github.com/Mic92/kshape -- time series clustering

https://www.slideshare.net/HamdanAzhar1/open-data-science-west-introduction-to-emoji-data-science-hamdan-azhar-nov-3-2017-81595966?trk=v-feed&lipi=urn%3Ali%3Apage%3Ad_flagship3_feed%3Bzm0bIKk7TjaWWw5P7THNGA%3D%3D --emoji's are also data

http://vertex.ai/blog/announcing-plaidml?utm_content=buffereb80a&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer --an altnerative to the tensorflow backend

https://www.datasciencecentral.com/forum/topics/k-means-clustering-effect-of-random-seed?utm_content=buffer9a2fb&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer -- seed matters for k-means

https://randomekek.github.io/deep/deeplearning.html --deep learning reference

https://medium.com/singular-distillation/little-explanations-information-bottleneck-theory-its-possible-link-to-neural-networks-1d4df1badf72 -- mutual information used to study neural networks. I say, so what? But maybe this is a useful thing.

https://schedule.readthedocs.io/en/stable/ --a simple scheduler

https://www.youtube.com/watch?v=3VQ382QG-y4&feature=youtu.be --an introduction to lambda calculus

https://github.com/stitchfix/diamond -- mixed effects models in python

https://github.com/civisanalytics/civisml-extensions -- scikit learning classifier and regressor stacking

https://github.com/caseyclements/pennies -- advanced time series modeling in python

https://arxiv.org/pdf/1607.06520.pdf -- super good paper on identifying gender bias

https://github.com/ericmjl/bayesian-analysis-recipes -- bayesian deep learning examples

https://github.com/mila-udem -- a very neat collection of tools

https://github.com/bnaul/IrregularTimeSeriesAutoencoderPaper -- A recurrent neural network for classification of unevenly sampled variable stars

https://www.youtube.com/user/PyDataTV/videos -- pydata videos

https://www.kdnuggets.com/2017/11/automated-feature-engineering-time-series-data.html?utm_content=buffere2903&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer -- time series feature engineering

http://www.nehalemlabs.net/prototype/blog/2013/04/05/an-introduction-to-smoothing-time-series-in-python-part-i-filtering-theory/ -- a bunch of smoothing techniques

https://www.kdnuggets.com/2017/07/when-not-use-deep-learning.html -- fantastic explanation of deep learning

https://www.kdnuggets.com/2017/11/10-statistical-techniques-data-scientists-need-master.html?utm_content=bufferd0f6b&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer -- a survey of statistical techniques

https://www.fullstackpython.com/blog/first-steps-gitpython.html -- python git client

http://pbpython.com/market-basket-analysis.html -- aprori algorithm at work

https://towardsdatascience.com/using-word2vec-for-music-recommendations-bb9649ac2484 -- music word2vec

http://rlhick.people.wm.edu/posts/estimating-custom-mle.html -- how to write a custom MLE with OLS as an example

https://github.com/ipython-books/cookbook-code -- a cookbook of a lot of scientific computing stuff. Mostly a bunch of great patterns for using numpy.

https://pypi.python.org/pypi/thinkx/1.1.2 --thinkbayes package

https://brilliant.org/wiki/stationary-distributions/ -- a very good introduction to Markov Chains. Sadly I know understand graphs, as a consequence, to be just another representation of matrices. Also, markov chains do finally make sense. And interestingly, you can find the steady states of Markov Chains from time to time. (joke)

https://github.com/scrat-online/pySTARMA -- geospatial timeseries ARIMA algorithm. Looks out of date, consider updating.

https://github.com/wkentaro/labelme -- an image annotation tool, which may be useful for annotating various images in image training sets.

https://cupy.chainer.org/?utm_content=bufferc0bef&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer -- numpy written for cuda

https://einstein.ai/research/hierarchical-reinforcement-learning?utm_content=bufferdcdd1&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer -- hierarchical RL language models

https://github.com/artpar/languagecrunch -- an NLP server ready to go

https://blog.dominodatalab.com/bias-policing-analysis-traffic-stop-data/?utm_content=buffer8976c&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer -- a great analysis of racial bias

https://towardsdatascience.com/how-to-create-data-products-that-are-magical-using-sequence-to-sequence-models-703f86a231f8 -- a good example of how to use sequence to sequence models in industry.

https://towardsdatascience.com/train-test-split-and-cross-validation-in-python-80b61beca4b6 -- great intro to cross validation, k-fold for sklearn

https://github.com/chrispaulca/waterfall --waterfall is an interesting visualization tool. Most interestingly, it can be used in conjunction with treeinterpretter to produce visualizations for tree based model interpretation - since you can retrain any model on a tree structure, this can be used as a general interpretability visualization across feature space.

https://github.com/andosa/treeinterpreter -- tree interpreter interprets tree based models of any kind. Looks very promising for understanding various models.

https://openreview.net/ -- very interesting set of resources on the papers to understand and internalize within ML

https://towardsdatascience.com/building-a-logistic-regression-in-python-step-by-step-becd4d56c9c8 -- a good explaination of feature engineering for logistic regression

https://en.wikipedia.org/wiki/Silhouette_(clustering) -- used to assess the quality of clustering algorithms

https://www.youtube.com/watch?v=MIKYRZc9A1M -- a fantastic deconstruction of superman

https://www.youtube.com/watch?v=R13BD8qKeTg -- best introduction to bayes I've ever seen

https://github.com/bmabey/pyLDAvis -- LDA visualization library

http://scikit-learn.org/stable/related_projects.html -- a great list of related packages and tools

https://github.com/cytoscape/cytoscape.js -- graph visualization js library

https://realpython.com/blog/python/python-matplotlib-guide/ -- a good introduction to matplotlib

https://gist.github.com/aronwc/8248457 -- gensim and sklearn together

https://en.wikipedia.org/wiki/Synthetic_control_method -- a way of doing natural experiments

http://ecocontrol.readthedocs.io/en/latest/index.html -- interesting timeseries forecasting system

http://www.cs.cornell.edu/~tomf/pyglpk/glpk.html -- interesting looking package

https://github.com/laspy/laspy -- LiDAR

https://medium.com/luminovo/a-refresher-on-batch-re-normalization-5e0a1e902960 -- batch renormalization, better than batch normalization

https://www.linkedin.com/pulse/4-reasons-your-machine-learning-model-wrong-how-fix-bilal-mahmood/ -- bias variance trade off and precision recall

https://www.kaggle.com/marknagelberg/rmsle-function -- root mean squared loss error function

http://www.business-science.io/code-tools/2017/10/28/demo_week_h2o.html -- timeseries automl R

https://towardsdatascience.com/how-i-learned-to-love-parallelized-applies-with-python-pandas-dask-and-numba-f06b0b367138 -- pandas numba dask performance benchmarking

https://machinelearningmastery.com/keras-functional-api-deep-learning/ -- shared layers neural network architecture for keras

https://github.com/titu1994/BatchRenormalization -- batch renormalization in keras

https://www.programcreek.com/python/example/83247/sklearn.cross_validation.KFold -- a good set of automl and cross validation techniques

https://github.com/Britefury/batchup -- a program for batching datasets.

https://github.com/mdbloice/Augmentor -- image augmentation library for deep learning

https://github.com/HIPS/molecule-autoencoder

https://brightthemag.com/legalizing-sex-work-spain-prostitution-human-rights-trafficking-immigration-gender-78b96c05e6fa -- what happens when you decriminalize sex

https://www.arxiv-vanity.com/papers/1803.04488/ -- concept2vec - embeddings for ontological concepts

https://www.oreilly.com/ideas/introducing-capsule-networks -- capsule net introduction

https://medium.freecodecamp.org/understanding-capsule-networks-ais-alluring-new-architecture-bdb228173ddc -- another great intro to capsule net

https://stackoverflow.com/questions/11404156/how-do-i-replace-text-in-a-selection -- sublime magic - replace text in selected area

https://www.youtube.com/watch?v=CY3t11vuuOM -- introduction to LIME

https://github.com/Ahmkel/Keras-Project-Template/blob/master/README.md -- keras templates

http://sigmajs.org/ --sigma.js graph visualization library

List intersection:

https://stackoverflow.com/questions/6369527/python-list-intersection-efficiency-generator-or-filter

https://www.geeksforgeeks.org/python-intersection-two-lists/

-- efficiently combine two lists

https://towardsdatascience.com/simple-and-multiple-linear-regression-in-python-c928425168f9 -- linear regression in Python, explained well

http://readingthemarkets.blogspot.com/2010/11/critique-of-granger-causality.html --criticism of granger causality

http://www.statsoft.com/Textbook/Time-Series-Analysis#lags -- statistics book

https://danielcscheer.files.wordpress.com/2012/03/food-stamps-and-poverty-irp-2012.pdf -- a good explanation of a lot of things. A great explaination of the matching problem.

https://medium.com/@Francesco_AI/artificial-intelligence-verticals-ii-fintech-daf6f0bd302c -- finance DIY

http://brohrer.github.io/how_convolutional_neural_networks_work.html --intro to conv nets

https://adeshpande3.github.io/adeshpande3.github.io/A-Beginner%27s-Guide-To-Understanding-Convolutional-Neural-Networks/ -- intro to conv nets

http://betatim.github.io/posts/bayesian-hyperparameter-search/ --smarter grid search

https://medium.freecodecamp.org/how-to-build-interactive-presentations-with-jupyter-notebook-and-reveal-js-c7e24f4bd9c5 --jupyter notebook to slides

https://explosion.ai/blog/sense2vec-with-spacy -- sense to vec - part of speech aware word2vec

https://homes.cs.washington.edu/~marcotcr/blog/lime/ -- LIME intro

https://github.com/TeamHG-Memex/eli5 --super interesting explainability of models

https://keras.io/getting-started/functional-api-guide/ --play with this for more sophisticated models

https://github.com/farizrahman4u/seq2seq -- seq2seq code keras

https://towardsdatascience.com/stochastic-weight-averaging-a-new-way-to-get-state-of-the-art-results-in-deep-learning-c639ccf36a -- state of the art neural networks

https://stats.stackexchange.com/questions/84076/negative-values-for-aic-in-general-mixed-model --A good interpretation of AIC and how to deal with negative values

https://www.datasciencecentral.com/profiles/blogs/swarm-optimization-goodbye-gradients -- alternative to stochastic gradient descent

https://quantdare.com/what-is-the-difference-between-bagging-and-boosting/ -- boosting versus bagging

https://towardsdatascience.com/boosting-algorithm-xgboost-4d9ec0207d -- subtle differences between xgboost and gradient boosted trees

http://www.swig.org/Doc1.3/Python.html --Cython like tool

https://blog.jle.im/entry/purely-functional-typed-models-1.html -- machine learning in haskell

https://www.coursera.org/specializations/aml?siteID=lVarvwc5BD0-BShznKdc3CUauhfsM7_8xw&utm_content=2&utm_medium=partners&utm_source=linkshare&utm_campaign=lVarvwc5BD0 -- coursera deep learning specialization

https://www.coursera.org/learn/machine-learning?utm_source=gg&utm_medium=sem&campaignid=685340575&adgroupid=32639001341&device=c&keyword=coursera%20machine%20learning%20course&matchtype=b&network=g&devicemodel=&adpostion=1t1&creativeid=176442054671&hide_mobile_promo&gclid=Cj0KCQjw5-TXBRCHARIsANLixNzthz0on3vVC1Vg9ldWyDzt0pY_0s2sdmUmKOPX7_H2UPH5GIA1vY4aAvDxEALw_wcB -- deep learning coursera

https://towardsdatascience.com/data2vis-automatic-generation-of-data-visualizations-using-sequence-to-sequence-recurrent-neural-5da8e9d3e43e --data visualization automated with sequence to sequence vectors

https://www.youtube.com/watch?v=jpNLp9SnTF8&t=1581s --interesting neural network architecture - attention, memory

https://machinelearningmastery.com/nonparametric-statistical-significance-tests-in-python/?utm_source=dlvr.it&utm_medium=twitter -- introduction to nonparametric tests

https://multithreaded.stitchfix.com/blog/2018/05/14/two-things-about-power/ -- really great post on the power test

http://blog.datadive.net/selecting-good-features-part-iv-stability-selection-rfe-and-everything-side-by-side/ --feature selection with sklearn

https://towardsdatascience.com/the-fall-of-rnn-lstm-2d1594c74ce0 -- reinterpretation of a very contraversal paper...Don't think I completely agree

https://machinelearningmastery.com/grid-search-arima-hyperparameters-with-python/ --grid search for timeseries

https://www.aiworkbox.com/lessons/specify-pytorch-tensor-minimum-value-threshold --aiworkbox deep learning tutorials

https://stats.stackexchange.com/questions/145566/how-to-calculate-area-under-the-curve-auc-or-the-c-statistic-by-hand --AUC explained, in detail

https://www.kaggle.com/jayatou/xgbregressor-with-gridsearchcv -- good basic xgboost example

https://python-graph-gallery.com/ --graph visualization examples galor!