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README.md	https://github.com/FerMatPy/datascience/blob/master/README.md
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CC0-1.0 license	https://github.com/FerMatPy/datascience
	https://github.com/FerMatPy/datascience#awesome-data-science-with-python
	https://github.com/FerMatPy/datascience#core
pandas	https://pandas.pydata.org/
numpy	https://www.numpy.org/
scikit-learn	https://scikit-learn.org/stable/
matplotlib	https://matplotlib.org/
seaborn	https://seaborn.pydata.org/
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missingno	https://github.com/ResidentMario/missingno
rainbow-csv	https://marketplace.visualstudio.com/items?itemName=mechatroner.rainbow-csv
	https://github.com/FerMatPy/datascience#environment-and-jupyter
General Jupyter Tricks	https://www.dataquest.io/blog/jupyter-notebook-tips-tricks-shortcuts/
link	https://jakevdp.github.io/blog/2017/12/05/installing-python-packages-from-jupyter/
blog post	https://www.blog.pythonlibrary.org/2018/10/17/jupyter-notebook-debugging/
video	https://www.youtube.com/watch?v=Z0ssNAbe81M&t=1h44m15s
cheatsheet	https://nblock.org/2011/11/15/pdb-cheatsheet/
cookiecutter-data-science	https://github.com/drivendata/cookiecutter-data-science
nteract	https://nteract.io/
papermill	https://github.com/nteract/papermill
tutorial	https://pbpython.com/papermil-rclone-report-1.html
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handcalcs	https://github.com/connorferster/handcalcs
	https://github.com/FerMatPy/datascience#pandas-tricks-alternatives-and-additions
Pandas Tricks	https://towardsdatascience.com/5-lesser-known-pandas-tricks-e8ab1dd21431
Using df.pipe() (video)	https://www.youtube.com/watch?v=yXGCKqo5cEY
pandasvault	https://github.com/firmai/pandasvault
modin	https://github.com/modin-project/modin
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xarray	https://github.com/pydata/xarray/
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pandas-log	https://github.com/eyaltrabelsi/pandas-log
pandapy	https://github.com/firmai/pandapy
	https://github.com/FerMatPy/datascience#helpful
drawdata	https://github.com/koaning/drawdata
website	https://drawdata.xyz/
tqdm	https://github.com/tqdm/tqdm
pandas apply()	https://stackoverflow.com/a/34365537/1820480
icecream	https://github.com/gruns/icecream
loguru	https://github.com/Delgan/loguru
pyprojroot	https://github.com/chendaniely/pyprojroot
intake	https://github.com/intake/intake
talk	https://www.youtube.com/watch?v=s7Ww5-vD2Os&t=33m40s
	https://github.com/FerMatPy/datascience#extraction
textract	https://github.com/deanmalmgren/textract
camelot	https://github.com/socialcopsdev/camelot
	https://github.com/FerMatPy/datascience#big-data
spark	https://docs.databricks.com/spark/latest/dataframes-datasets/introduction-to-dataframes-python.html#work-with-dataframes
cheatsheet	https://gist.github.com/crawles/b47e23da8218af0b9bd9d47f5242d189
tutorial	https://github.com/ericxiao251/spark-syntax
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koalas	https://github.com/databricks/koalas
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resources	https://matthewrocklin.com/blog//work/2018/07/17/dask-dev
talk1	https://www.youtube.com/watch?v=ccfsbuqsjgI
talk2	https://www.youtube.com/watch?v=RA_2qdipVng
notebooks	https://github.com/dask/dask-ec2/tree/master/notebooks
videos	https://www.youtube.com/user/mdrocklin
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	https://github.com/FerMatPy/datascience#command-line-tools-csv
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	https://github.com/FerMatPy/datascience#classical-statistics
	https://github.com/FerMatPy/datascience#statistical-tests-and-packages
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statsmodels	https://www.statsmodels.org/stable/index.html
pingouin	https://github.com/raphaelvallat/pingouin
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	https://github.com/FerMatPy/datascience#interim-analyses--sequential-analysis--stopping
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sequential	https://cran.r-project.org/web/packages/Sequential/Sequential.pdf
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	https://github.com/FerMatPy/datascience#visualizations
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Correlation	https://rpsychologist.com/d3/correlation/
Cohen's d	https://rpsychologist.com/d3/cohend/
Confidence Interval	https://rpsychologist.com/d3/CI/
Equivalence, non-inferiority and superiority testing	https://rpsychologist.com/d3/equivalence/
Bayesian two-sample t test	https://rpsychologist.com/d3/bayes/
Distribution of p-values when comparing two groups	https://rpsychologist.com/d3/pdist/
Understanding the t-distribution and its normal approximation	https://rpsychologist.com/d3/tdist/
	https://github.com/FerMatPy/datascience#talks
Inverse Propensity Weighting	https://www.youtube.com/watch?v=SUq0shKLPPs
Dealing with Selection Bias By Propensity Based Feature Selection	https://www.youtube.com/watch?reload=9&v=3ZWCKr0vDtc
	https://github.com/FerMatPy/datascience#texts
Greenland - Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4877414/
Lindeløv - Common statistical tests are linear models	https://lindeloev.github.io/tests-as-linear/
Chatruc - The Central Limit Theorem and its misuse	https://lambdaclass.com/data_etudes/central_limit_theorem_misuse/
Al-Saleh - Properties of the Standard Deviation that are Rarely Mentioned in Classrooms	http://www.stat.tugraz.at/AJS/ausg093/093Al-Saleh.pdf
Wainer - The Most Dangerous Equation	http://www-stat.wharton.upenn.edu/~hwainer/Readings/Most%20Dangerous%20eqn.pdf
Gigerenzer - The Bias Bias in Behavioral Economics	https://www.nowpublishers.com/article/Details/RBE-0092
Cook - Estimating the chances of something that hasn’t happened yet	https://www.johndcook.com/blog/2010/03/30/statistical-rule-of-three/
	https://github.com/FerMatPy/datascience#epidemiology
R Epidemics Consortium	https://www.repidemicsconsortium.org/projects/
Github	https://github.com/reconhub
incidence2	https://github.com/reconhub/incidence2
EpiEstim	https://github.com/mrc-ide/EpiEstim
paper	https://academic.oup.com/aje/article/178/9/1505/89262
researchpy	https://github.com/researchpy/researchpy
zEpid	https://github.com/pzivich/zEpid
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	https://github.com/FerMatPy/datascience#exploration-and-cleaning
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cleanlab	https://github.com/cgnorthcutt/cleanlab
pandasgui	https://github.com/adamerose/pandasgui
janitor	https://pyjanitor.readthedocs.io/
impyute	https://github.com/eltonlaw/impyute
fancyimpute	https://github.com/iskandr/fancyimpute
imbalanced-learn	https://github.com/scikit-learn-contrib/imbalanced-learn
tspreprocess	https://github.com/MaxBenChrist/tspreprocess
Kaggler	https://github.com/jeongyoonlee/Kaggler
pyupset	https://github.com/ImSoErgodic/py-upset
pyemd	https://github.com/wmayner/pyemd
OpenCV implementation	https://docs.opencv.org/3.4/d6/dc7/group__imgproc__hist.html
POT implementation	https://pythonot.github.io/auto_examples/plot_OT_2D_samples.html
littleballoffur	https://github.com/benedekrozemberczki/littleballoffur
	https://github.com/FerMatPy/datascience#train--test-split
iterative-stratification	https://github.com/trent-b/iterative-stratification
	https://github.com/FerMatPy/datascience#feature-engineering
Talk	https://www.youtube.com/watch?v=68ABAU_V8qI
sklearn	https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html
examples	https://github.com/jem1031/pandas-pipelines-custom-transformers
pdpipe	https://github.com/shaypal5/pdpipe
scikit-lego	https://github.com/koaning/scikit-lego
skoot	https://github.com/tgsmith61591/skoot
categorical-encoding	https://github.com/scikit-learn-contrib/categorical-encoding
vtreat (R package)	https://cran.r-project.org/web/packages/vtreat/vignettes/vtreat.html
dirty_cat	https://github.com/dirty-cat/dirty_cat
patsy	https://github.com/pydata/patsy/
mlxtend	https://rasbt.github.io/mlxtend/user_guide/feature_extraction/LinearDiscriminantAnalysis/
featuretools	https://github.com/Featuretools/featuretools
example	https://github.com/WillKoehrsen/automated-feature-engineering/blob/master/walk_through/Automated_Feature_Engineering.ipynb
tsfresh	https://github.com/blue-yonder/tsfresh
pypeln	https://github.com/cgarciae/pypeln
feature_engine	https://github.com/solegalli/feature_engine
	https://github.com/FerMatPy/datascience#feature-engineering-images
skimage	https://scikit-image.org/docs/dev/api/skimage.measure.html#skimage.measure.regionprops
mahotas	https://github.com/luispedro/mahotas
pyradiomics	https://github.com/AIM-Harvard/pyradiomics
pyefd	https://github.com/hbldh/pyefd
	https://github.com/FerMatPy/datascience#feature-selection
Talk	https://www.youtube.com/watch?v=JsArBz46_3s
Repo	https://github.com/Yimeng-Zhang/feature-engineering-and-feature-selection
1	http://blog.datadive.net/selecting-good-features-part-i-univariate-selection/
2	http://blog.datadive.net/selecting-good-features-part-ii-linear-models-and-regularization/
3	http://blog.datadive.net/selecting-good-features-part-iii-random-forests/
4	http://blog.datadive.net/selecting-good-features-part-iv-stability-selection-rfe-and-everything-side-by-side/
1	https://www.kaggle.com/residentmario/automated-feature-selection-with-sklearn
2	https://machinelearningmastery.com/feature-selection-machine-learning-python/
sklearn	https://scikit-learn.org/stable/modules/classes.html#module-sklearn.feature_selection
eli5	https://eli5.readthedocs.io/en/latest/blackbox/permutation_importance.html#feature-selection
scikit-feature	https://github.com/jundongl/scikit-feature
stability-selection	https://github.com/scikit-learn-contrib/stability-selection
scikit-rebate	https://github.com/EpistasisLab/scikit-rebate
scikit-genetic	https://github.com/manuel-calzolari/sklearn-genetic
boruta_py	https://github.com/scikit-learn-contrib/boruta_py
explaination	https://stats.stackexchange.com/questions/264360/boruta-all-relevant-feature-selection-vs-random-forest-variables-of-importanc/264467
example	https://www.kaggle.com/tilii7/boruta-feature-elimination
linselect	https://github.com/efavdb/linselect
mlxtend	https://rasbt.github.io/mlxtend/user_guide/feature_selection/ExhaustiveFeatureSelector/
BoostARoota	https://github.com/chasedehan/BoostARoota
INVASE	https://github.com/jsyoon0823/INVASE
	https://github.com/FerMatPy/datascience#dimensionality-reduction--representation-learning
	https://github.com/FerMatPy/datascience#selection
Review	https://members.loria.fr/moberger/Enseignement/AVR/Exposes/TR_Dimensiereductie.pdf
link	https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html
link	https://blog.keras.io/building-autoencoders-in-keras.html
link	https://scikit-learn.org/stable/modules/generated/sklearn.manifold.Isomap.html#sklearn.manifold.Isomap
link	https://scikit-learn.org/stable/modules/generated/sklearn.manifold.LocallyLinearEmbedding.html
link	https://scanpy.readthedocs.io/en/stable/api/scanpy.tl.draw_graph.html#scanpy.tl.draw_graph
link	https://scikit-learn.org/stable/modules/generated/sklearn.manifold.MDS.html
link	https://scanpy.readthedocs.io/en/stable/api/scanpy.tl.diffmap.html
link	https://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html#sklearn.manifold.TSNE
link	https://github.com/ziyuang/pynerv
paper	https://www.jmlr.org/papers/volume11/venna10a/venna10a.pdf
link	https://github.com/EpistasisLab/scikit-mdr
link	https://github.com/lmcinnes/umap
link	https://scikit-learn.org/stable/modules/random_projection.html
link	https://github.com/beringresearch/ivis
link	https://github.com/lightly-ai/lightly
	https://github.com/FerMatPy/datascience#neural-network-based
lightly	https://github.com/lightly-ai/lightly
esvit	https://github.com/microsoft/esvit
MCML	https://github.com/pachterlab/MCML
paper	https://www.biorxiv.org/content/10.1101/2021.08.25.457696v1
	https://github.com/FerMatPy/datascience#packages
Talk	https://www.youtube.com/watch?v=9iol3Lk6kyU
tsne intro	https://distill.pub/2016/misread-tsne/
sklearn.manifold	https://scikit-learn.org/stable/modules/classes.html#module-sklearn.manifold
sklearn.decomposition	https://scikit-learn.org/stable/modules/classes.html#module-sklearn.decomposition
sklearn.random_projection	https://scikit-learn.org/stable/modules/random_projection.html
prince	https://github.com/MaxHalford/prince
lvdmaaten	https://lvdmaaten.github.io/tsne/
MulticoreTSNE	https://github.com/DmitryUlyanov/Multicore-TSNE
FIt-SNE	https://github.com/KlugerLab/FIt-SNE
umap	https://github.com/lmcinnes/umap
talk	https://www.youtube.com/watch?v=nq6iPZVUxZU
explorer	https://github.com/GrantCuster/umap-explorer
explanation	https://pair-code.github.io/understanding-umap/
parallel version	https://docs.rapids.ai/api/cuml/stable/api.html
sleepwalk	https://github.com/anders-biostat/sleepwalk/
somoclu	https://github.com/peterwittek/somoclu
scikit-tda	https://github.com/scikit-tda/scikit-tda
paper	https://www.nature.com/articles/srep01236
talk	https://www.youtube.com/watch?v=F2t_ytTLrQ4
talk	https://www.youtube.com/watch?v=AWoeBzJd7uQ
paper	https://www.uncg.edu/mat/faculty/cdsmyth/topological-approaches-skin.pdf
giotto-tda	https://github.com/giotto-ai/giotto-tda
ivis	https://github.com/beringresearch/ivis
trimap	https://github.com/eamid/trimap
scanpy	https://github.com/theislab/scanpy
Force-directed graph drawing	https://scanpy.readthedocs.io/en/stable/api/scanpy.tl.draw_graph.html#scanpy.tl.draw_graph
Diffusion Maps	https://scanpy.readthedocs.io/en/stable/api/scanpy.tl.diffmap.html
direpack	https://github.com/SvenSerneels/direpack
DBS	https://cran.r-project.org/web/packages/DatabionicSwarm/vignettes/DatabionicSwarm.html
	https://github.com/FerMatPy/datascience#training-related
iterative-stratification	https://github.com/trent-b/iterative-stratification
livelossplot	https://github.com/stared/livelossplot
	https://github.com/FerMatPy/datascience#visualization
All charts	https://datavizproject.com/
Austrian monuments	https://github.com/njanakiev/austrian-monuments-visualization
cufflinks	https://github.com/santosjorge/cufflinks
plotly	https://plot.ly/
medium	https://towardsdatascience.com/the-next-level-of-data-visualization-in-python-dd6e99039d5e
example	https://github.com/WillKoehrsen/Data-Analysis/blob/master/plotly/Plotly%20Whirlwind%20Introduction.ipynb
physt	https://github.com/janpipek/physt
talk	https://www.youtube.com/watch?v=ZG-wH3-Up9Y
notebook	https://nbviewer.jupyter.org/github/janpipek/pydata2018-berlin/blob/master/notebooks/talk.ipynb
fast-histogram	https://github.com/astrofrog/fast-histogram
matplotlib_venn	https://github.com/konstantint/matplotlib-venn
alternative	https://github.com/penrose/penrose
joypy	https://github.com/sbebo/joypy
Ridge plots in seaborn	https://seaborn.pydata.org/examples/kde_ridgeplot.html
mosaic plots	https://www.statsmodels.org/dev/generated/statsmodels.graphics.mosaicplot.mosaic.html
example	https://sukhbinder.wordpress.com/2018/09/18/mosaic-plot-in-python/
scikit-plot	https://github.com/reiinakano/scikit-plot
yellowbrick	https://github.com/DistrictDataLabs/yellowbrick
bokeh	https://bokeh.pydata.org/en/latest/
Examples	https://bokeh.pydata.org/en/latest/docs/user_guide/server.html
Examples	https://github.com/WillKoehrsen/Bokeh-Python-Visualization
lets-plot	https://github.com/JetBrains/lets-plot/blob/master/README_PYTHON.md
animatplot	https://github.com/t-makaro/animatplot
plotnine	https://github.com/has2k1/plotnine
altair	https://altair-viz.github.io/
bqplot	https://github.com/bloomberg/bqplot
hvplot	https://github.com/pyviz/hvplot
holoviews	http://holoviews.org/
dtreeviz	https://github.com/parrt/dtreeviz
chartify	https://github.com/spotify/chartify/
VivaGraphJS	https://github.com/anvaka/VivaGraphJS
pm	https://github.com/anvaka/pm
example	https://w2v-vis-dot-hcg-team-di.appspot.com/#/galaxy/word2vec?cx=5698&cy=-5135&cz=5923&lx=0.1127&ly=0.3238&lz=-0.1680&lw=0.9242&ml=150&s=1.75&l=1&v=hc
python-ternary	https://github.com/marcharper/python-ternary
falcon	https://github.com/uwdata/falcon
hiplot	https://github.com/facebookresearch/hiplot
visdom	https://github.com/fossasia/visdom
mpl-scatter-density	https://github.com/astrofrog/mpl-scatter-density
ComplexHeatmap	https://github.com/jokergoo/ComplexHeatmap
largeVis	https://github.com/elbamos/largeVis
	https://github.com/FerMatPy/datascience#colors
palettable	https://github.com/jiffyclub/palettable
colorbrewer2	https://colorbrewer2.org/#type=sequential&scheme=BuGn&n=3
colorcet	https://github.com/holoviz/colorcet
	https://github.com/FerMatPy/datascience#dashboards
superset	https://github.com/apache/superset
streamlit	https://github.com/streamlit/streamlit
Resources	https://github.com/marcskovmadsen/awesome-streamlit
Gallery	https://awesome-streamlit.org/
Components	https://www.streamlit.io/components
bokeh-events	https://github.com/ash2shukla/streamlit-bokeh-events
dash	https://dash.plot.ly/gallery
Resources	https://github.com/ucg8j/awesome-dash
visdom	https://github.com/facebookresearch/visdom
panel	https://panel.pyviz.org/index.html
altair example	https://github.com/xhochy/altair-vue-vega-example
Video	https://www.youtube.com/watch?v=4L568emKOvs
voila	https://github.com/QuantStack/voila
	https://github.com/FerMatPy/datascience#survey-tools
samplics	https://github.com/samplics-org/samplics
	https://github.com/FerMatPy/datascience#geographical-tools
folium	https://github.com/python-visualization/folium
jupyter plugin	https://github.com/jupyter-widgets/ipyleaflet
gmaps	https://github.com/pbugnion/gmaps
stadiamaps	https://stadiamaps.com/
datashader	https://github.com/bokeh/datashader
sklearn	https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.BallTree.html
Example	https://tech.minodes.com/experiments-with-in-memory-spatial-radius-queries-in-python-e40c9e66cf63
pynndescent	https://github.com/lmcinnes/pynndescent
geocoder	https://github.com/DenisCarriere/geocoder
talk	https://www.youtube.com/watch?v=eHRggqAvczE
repo	https://github.com/dillongardner/PyDataSpatialAnalysis
geopandas	https://github.com/geopandas/geopandas
ipynb	https://github.com/njanakiev/osm-predict-economic-measurements/blob/master/osm-predict-economic-indicators.ipynb
PySal	https://github.com/pysal/pysal
geography	https://github.com/ushahidi/geograpy
cartogram	https://go-cart.io/cartogram
	https://github.com/FerMatPy/datascience#recommender-systems
1	https://lazyprogrammer.me/tutorial-on-collaborative-filtering-and-matrix-factorization-in-python/
2	https://medium.com/@james_aka_yale/the-4-recommendation-engines-that-can-predict-your-movie-tastes-bbec857b8223
2-ipynb	https://github.com/khanhnamle1994/movielens/blob/master/Content_Based_and_Collaborative_Filtering_Models.ipynb
3	https://www.kaggle.com/morrisb/how-to-recommend-anything-deep-recommender
surprise	https://github.com/NicolasHug/Surprise
talk	https://www.youtube.com/watch?v=d7iIb_XVkZs
turicreate	https://github.com/apple/turicreate
implicit	https://github.com/benfred/implicit
spotlight	https://github.com/maciejkula/spotlight
lightfm	https://github.com/lyst/lightfm
funk-svd	https://github.com/gbolmier/funk-svd
pywFM	https://github.com/jfloff/pywFM
	https://github.com/FerMatPy/datascience#decision-tree-models
Intro to Decision Trees and Random Forests	https://victorzhou.com/blog/intro-to-random-forests/
Intro to Gradient Boosting	http://blog.kaggle.com/2017/01/23/a-kaggle-master-explains-gradient-boosting/
lightgbm	https://github.com/Microsoft/LightGBM
doc	https://sites.google.com/view/lauraepp/parameters
xgboost	https://github.com/dmlc/xgboost
doc	https://sites.google.com/view/lauraepp/parameters
link1	https://stats.stackexchange.com/questions/255783/confidence-interval-for-xgb-forecast
link2	https://towardsdatascience.com/regression-prediction-intervals-with-xgboost-428e0a018b
catboost	https://github.com/catboost/catboost
h2o	https://github.com/h2oai/h2o-3
snapml	https://www.zurich.ibm.com/snapml/
PyPI	https://pypi.org/project/snapml/
pycaret	https://github.com/pycaret/pycaret
thundergbm	https://github.com/Xtra-Computing/thundergbm
h2o	https://github.com/h2oai/h2o-3
forestci	https://github.com/scikit-learn-contrib/forest-confidence-interval
scikit-garden	https://github.com/scikit-garden/scikit-garden
grf	https://github.com/grf-labs/grf
dtreeviz	https://github.com/parrt/dtreeviz
Nuance	https://github.com/SauceCat/Nuance
rfpimp	https://github.com/parrt/random-forest-importances
link	http://explained.ai/rf-importance/index.html
treeinterpreter	https://github.com/andosa/treeinterpreter
bartpy	https://github.com/JakeColtman/bartpy
infiniteboost	https://github.com/arogozhnikov/infiniteboost
merf	https://github.com/manifoldai/merf
video	https://www.youtube.com/watch?v=gWj4ZwB7f3o
rrcf	https://github.com/kLabUM/rrcf
groot	https://github.com/tudelft-cda-lab/GROOT
linear-tree	https://github.com/cerlymarco/linear-tree
	https://github.com/FerMatPy/datascience#natural-language-processing-nlp--text-processing
talk	https://www.youtube.com/watch?v=6zm9NC9uRkk
nb	https://nbviewer.jupyter.org/github/skipgram/modern-nlp-in-python/blob/master/executable/Modern_NLP_in_Python.ipynb
nb2	https://ahmedbesbes.com/how-to-mine-newsfeed-data-and-extract-interactive-insights-in-python.html
talk	https://www.youtube.com/watch?time_continue=2&v=sI7VpFNiy_I
Text classification Intro	https://mlwhiz.com/blog/2018/12/17/text_classification/
Preprocessing blog post	https://mlwhiz.com/blog/2019/01/17/deeplearning_nlp_preprocess/
gensim	https://radimrehurek.com/gensim/
Example	https://markroxor.github.io/gensim/static/notebooks/gensim_news_classification.html
Coherence Model	https://radimrehurek.com/gensim/models/coherencemodel.html
GloVe	https://nlp.stanford.edu/projects/glove/
1	https://www.kaggle.com/jhoward/improved-lstm-baseline-glove-dropout
2	https://www.kaggle.com/sbongo/do-pretrained-embeddings-give-you-the-extra-edge
StarSpace	https://github.com/facebookresearch/StarSpace
wikipedia2vec	https://wikipedia2vec.github.io/wikipedia2vec/pretrained/
visualization	https://projector.tensorflow.org/
magnitude	https://github.com/plasticityai/magnitude
pyldavis	https://github.com/bmabey/pyLDAvis
spaCy	https://spacy.io/
NTLK	https://www.nltk.org/
pytext	https://github.com/facebookresearch/PyText
fastText	https://github.com/facebookresearch/fastText
annoy	https://github.com/spotify/annoy
faiss	https://github.com/facebookresearch/faiss
pysparnn	https://github.com/facebookresearch/pysparnn
infomap	https://github.com/mapequation/infomap
example	https://github.com/mapequation/infomap/blob/master/examples/python/infomap-examples.ipynb
datasketch	https://github.com/ekzhu/datasketch
flair	https://github.com/zalandoresearch/flair
stanfordnlp	https://github.com/stanfordnlp/stanfordnlp
Chatistics	https://github.com/MasterScrat/Chatistics
textvec	https://github.com/textvec/textvec
	https://github.com/FerMatPy/datascience#papers
Search Engine Correlation	https://arxiv.org/pdf/1107.2691.pdf
	https://github.com/FerMatPy/datascience#biology
	https://github.com/FerMatPy/datascience#sequencing
scanpy	https://github.com/theislab/scanpy
tutorial	https://github.com/theislab/single-cell-tutorial
	https://github.com/FerMatPy/datascience#image-related
mahotas	http://luispedro.org/software/mahotas/
example	https://github.com/luispedro/python-image-tutorial/blob/master/Segmenting%20cell%20images%20(fluorescent%20microscopy).ipynb
imagepy	https://github.com/Image-Py/imagepy
CellProfiler	https://github.com/CellProfiler/CellProfiler
imglyb	https://github.com/imglib/imglyb
talk	https://www.youtube.com/watch?v=Ddo5z5qGMb8
slides	https://github.com/hanslovsky/scipy-2019/blob/master/scipy-2019-imglyb.pdf
microscopium	https://github.com/microscopium/microscopium
talk	https://www.youtube.com/watch?v=ytEQl9xs8FQ
cytokit	https://github.com/hammerlab/cytokit
	https://github.com/FerMatPy/datascience#image-processing
Talk	https://www.youtube.com/watch?v=Y5GJmnIhvFk
cv2	https://github.com/skvark/opencv-python
Gaussian Filter	https://docs.opencv.org/3.1.0/d4/d13/tutorial_py_filtering.html
Morphological Transformations	https://docs.opencv.org/3.0-beta/doc/py_tutorials/py_imgproc/py_morphological_ops/py_morphological_ops.html
scikit-image	https://github.com/scikit-image/scikit-image
	https://github.com/FerMatPy/datascience#neural-networks
	https://github.com/FerMatPy/datascience#tutorials--viewer
Convolutional Neural Networks for Visual Recognition	https://cs231n.github.io/
Lessons 1-7	https://course.fast.ai/videos/?lesson=1
Lessons 8-14	http://course18.fast.ai/lessons/lessons2.html
Tensorflow without a PhD	https://github.com/GoogleCloudPlatform/tensorflow-without-a-phd
Blog	https://distill.pub/2017/feature-visualization/
PPT	http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture12.pdf
Tensorflow Playground	https://playground.tensorflow.org/
Visualization of optimization algorithms	https://vis.ensmallen.org/
Another visualization	https://github.com/jettify/pytorch-optimizer
cutouts-explorer	https://github.com/mgckind/cutouts-explorer
	https://github.com/FerMatPy/datascience#image-related-1
imgaug	https://github.com/aleju/imgaug
Augmentor	https://github.com/mdbloice/Augmentor
keras preprocessing	https://keras.io/preprocessing/image/
albumentations	https://github.com/albu/albumentations
augmix	https://github.com/google-research/augmix
kornia	https://github.com/kornia/kornia
augly	https://github.com/facebookresearch/AugLy
	https://github.com/FerMatPy/datascience#lossfunction-related
SegLoss	https://github.com/JunMa11/SegLoss
	https://github.com/FerMatPy/datascience#text-related
ktext	https://github.com/hamelsmu/ktext
textgenrnn	https://github.com/minimaxir/textgenrnn
ctrl	https://github.com/salesforce/ctrl
	https://github.com/FerMatPy/datascience#libs
keras	https://keras.io/
tensorflow	https://www.tensorflow.org/
examples	https://gist.github.com/candlewill/552fa102352ccce42fd829ae26277d24
timm	https://github.com/rwightman/pytorch-image-models
keras-contrib	https://github.com/keras-team/keras-contrib
keras-tuner	https://github.com/keras-team/keras-tuner
hyperas	https://github.com/maxpumperla/hyperas
elephas	https://github.com/maxpumperla/elephas
tflearn	https://github.com/tflearn/tflearn
tensorlayer	https://github.com/tensorlayer/tensorlayer
tricks	https://github.com/wagamamaz/tensorlayer-tricks
tensorforce	https://github.com/reinforceio/tensorforce
fastai	https://github.com/fastai/fastai
pytorch-optimizer	https://github.com/jettify/pytorch-optimizer
ignite	https://github.com/pytorch/ignite
skorch	https://github.com/dnouri/skorch
talk	https://www.youtube.com/watch?v=0J7FaLk0bmQ
slides	https://github.com/thomasjpfan/skorch_talk
autokeras	https://github.com/jhfjhfj1/autokeras
PlotNeuralNet	https://github.com/HarisIqbal88/PlotNeuralNet
lucid	https://github.com/tensorflow/lucid
Activation Maps	https://openai.com/blog/introducing-activation-atlases/
tcav	https://github.com/tensorflow/tcav
AdaBound	https://github.com/Luolc/AdaBound
alt	https://github.com/titu1994/keras-adabound
foolbox	https://github.com/bethgelab/foolbox
hiddenlayer	https://github.com/waleedka/hiddenlayer
imgclsmob	https://github.com/osmr/imgclsmob
netron	https://github.com/lutzroeder/netron
torchcv	https://github.com/donnyyou/torchcv
pytorch-lightning	https://github.com/PyTorchLightning/PyTorch-lightning
lightly	https://github.com/lightly-ai/lightly
	https://github.com/FerMatPy/datascience#distributed-libs
flexflow	https://github.com/flexflow/FlexFlow
	https://github.com/FerMatPy/datascience#architecture-visualization
netron	https://github.com/lutzroeder/netron
	https://github.com/FerMatPy/datascience#object-detection--instance-segmentation
segmentation_models	https://github.com/qubvel/segmentation_models
yolact	https://github.com/dbolya/yolact
EfficientDet Pytorch	https://github.com/toandaominh1997/EfficientDet.Pytorch
EfficientDet Keras	https://github.com/xuannianz/EfficientDet
detectron2	https://github.com/facebookresearch/detectron2
simpledet	https://github.com/TuSimple/simpledet
CenterNet	https://github.com/xingyizhou/CenterNet
FCOS	https://github.com/tianzhi0549/FCOS
norfair	https://github.com/tryolabs/norfair
	https://github.com/FerMatPy/datascience#image-annotation
cvat	https://github.com/openvinotoolkit/cvat
pigeon	https://github.com/agermanidis/pigeon
	https://github.com/FerMatPy/datascience#image-classification
nfnets	https://github.com/ypeleg/nfnets-keras
efficientnet	https://github.com/lukemelas/EfficientNet-PyTorch
pycls	https://github.com/facebookresearch/pycls
	https://github.com/FerMatPy/datascience#applications-and-snippets
CycleGAN and Pix2pix	https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix
SPADE	https://github.com/nvlabs/spade
Entity Embeddings of Categorical Variables	https://arxiv.org/abs/1604.06737
code	https://github.com/entron/entity-embedding-rossmann
kaggle	https://www.kaggle.com/aquatic/entity-embedding-neural-net/code
Image Super-Resolution	https://github.com/idealo/image-super-resolution
Talk	https://www.youtube.com/watch?v=dVFZpodqJiI
1	https://www.thomasjpfan.com/2018/07/nuclei-image-segmentation-tutorial/
2	https://www.thomasjpfan.com/2017/08/hassle-free-unets/
deeplearning-models	https://github.com/rasbt/deeplearning-models
	https://github.com/FerMatPy/datascience#variational-autoencoders-vae
disentanglement_lib	https://github.com/google-research/disentanglement_lib
	https://github.com/FerMatPy/datascience#graph-based-neural-networks
How to do Deep Learning on Graphs with Graph Convolutional Networks	https://towardsdatascience.com/how-to-do-deep-learning-on-graphs-with-graph-convolutional-networks-7d2250723780
Introduction To Graph Convolutional Networks	http://tkipf.github.io/graph-convolutional-networks/
An attempt at demystifying graph deep learning	https://ericmjl.github.io/essays-on-data-science/machine-learning/graph-nets/
ogb	https://ogb.stanford.edu/
networkx	https://github.com/networkx/networkx
cugraph	https://github.com/rapidsai/cugraph
pytorch-geometric	https://github.com/rusty1s/pytorch_geometric
dgl	https://github.com/dmlc/dgl
graph_nets	https://github.com/deepmind/graph_nets
	https://github.com/FerMatPy/datascience#other-neural-network-and-deep-learning-frameworks
caffe	https://github.com/BVLC/caffe
pretrained models	https://github.com/BVLC/caffe/wiki/Model-Zoo
mxnet	https://github.com/apache/incubator-mxnet
book	https://d2l.ai/index.html
	https://github.com/FerMatPy/datascience#model-conversion
hummingbird	https://github.com/microsoft/hummingbird
	https://github.com/FerMatPy/datascience#gpu
cuML	https://github.com/rapidsai/cuml
Intro	https://www.youtube.com/watch?v=6XzS5XcpicM&t=2m50s
thundergbm	https://github.com/Xtra-Computing/thundergbm
thundersvm	https://github.com/Xtra-Computing/thundersvm
video	https://www.youtube.com/watch?v=Jxxs_moibog
	https://github.com/FerMatPy/datascience#regression
slides	https://cs.adelaide.edu.au/~chhshen/teaching/ML_SVR.pdf
forum	https://www.quora.com/How-does-support-vector-regression-work
paper	http://alex.smola.org/papers/2003/SmoSch03b.pdf
pyearth	https://github.com/scikit-learn-contrib/py-earth
tutorial	https://uc-r.github.io/mars
pygam	https://github.com/dswah/pyGAM
Explanation	https://multithreaded.stitchfix.com/blog/2015/07/30/gam/
GLRM	https://github.com/madeleineudell/LowRankModels.jl
tweedie	https://xgboost.readthedocs.io/en/latest/parameter.html#parameters-for-tweedie-regression-objective-reg-tweedie
Talk	https://www.youtube.com/watch?v=-o0lpHBq85I
	https://github.com/FerMatPy/datascience#classification
Talk	https://www.youtube.com/watch?v=DkLPYccEJ8Y
Notebook	https://github.com/ianozsvald/data_science_delivered/blob/master/ml_creating_correct_capable_classifiers.ipynb
Blog post: Probability Scoring	https://machinelearningmastery.com/how-to-score-probability-predictions-in-python/
All classification metrics	http://rali.iro.umontreal.ca/rali/sites/default/files/publis/SokolovaLapalme-JIPM09.pdf
DESlib	https://github.com/scikit-learn-contrib/DESlib
human-learn	https://github.com/koaning/human-learn
	https://github.com/FerMatPy/datascience#metric-learning
Contrastive Representation Learning	https://lilianweng.github.io/lil-log/2021/05/31/contrastive-representation-learning.html
metric-learn	https://github.com/scikit-learn-contrib/metric-learn
pytorch-metric-learning	https://github.com/KevinMusgrave/pytorch-metric-learning
deep_metric_learning	https://github.com/ronekko/deep_metric_learning
ivis	https://bering-ivis.readthedocs.io/en/latest/supervised.html
	https://github.com/FerMatPy/datascience#distance-functions
scipy.spatial	https://docs.scipy.org/doc/scipy/reference/spatial.distance.html
pyemd	https://github.com/wmayner/pyemd
OpenCV implementation	https://docs.opencv.org/3.4/d6/dc7/group__imgproc__hist.html
POT implementation	https://pythonot.github.io/auto_examples/plot_OT_2D_samples.html
dcor	https://github.com/vnmabus/dcor
GeomLoss	https://www.kernel-operations.io/geomloss/
	https://github.com/FerMatPy/datascience#clustering
Overview of clustering algorithms applied image data (= Deep Clustering)	https://deepnotes.io/deep-clustering
Clustering with Deep Learning: Taxonomy and New Methods	https://arxiv.org/pdf/1801.07648.pdf
hdbscan	https://github.com/scikit-learn-contrib/hdbscan
talk	https://www.youtube.com/watch?v=dGsxd67IFiU
blog	https://towardsdatascience.com/understanding-hdbscan-and-density-based-clustering-121dbee1320e
pyclustering	https://github.com/annoviko/pyclustering
FCPS	https://github.com/Mthrun/FCPS
GaussianMixture	https://scikit-learn.org/stable/modules/generated/sklearn.mixture.GaussianMixture.html
video	https://www.youtube.com/watch?v=aICqoAG5BXQ
nmslib	https://github.com/nmslib/nmslib
buckshotpp	https://github.com/zjohn77/buckshotpp
merf	https://github.com/manifoldai/merf
video	https://www.youtube.com/watch?v=gWj4ZwB7f3o
tree-SNE	https://github.com/isaacrob/treesne
MiniSom	https://github.com/JustGlowing/minisom
distribution_clustering	https://github.com/EricElmoznino/distribution_clustering
paper	https://arxiv.org/abs/1804.02624
related paper	https://arxiv.org/abs/2003.07770
alt	https://github.com/r0f1/distribution_clustering
phenograph	https://github.com/dpeerlab/phenograph
	https://github.com/FerMatPy/datascience#clustering-evalutation
Wagner, Wagner - Comparing Clusterings - An Overview	https://publikationen.bibliothek.kit.edu/1000011477/812079
Adjusted Rand Index	https://scikit-learn.org/stable/modules/generated/sklearn.metrics.adjusted_rand_score.html
Normalized Mutual Information	https://scikit-learn.org/stable/modules/generated/sklearn.metrics.normalized_mutual_info_score.html
Adjusted Mutual Information	https://scikit-learn.org/stable/modules/generated/sklearn.metrics.adjusted_mutual_info_score.html
Fowlkes-Mallows Score	https://scikit-learn.org/stable/modules/generated/sklearn.metrics.fowlkes_mallows_score.html
Silhouette Coefficient	https://scikit-learn.org/stable/modules/generated/sklearn.metrics.silhouette_score.html
Variation of Information	https://gist.github.com/jwcarr/626cbc80e0006b526688
Julia	https://clusteringjl.readthedocs.io/en/latest/varinfo.html
Pair Confusion Matrix	https://scikit-learn.org/stable/modules/generated/sklearn.metrics.cluster.pair_confusion_matrix.html
Consensus Score	https://scikit-learn.org/stable/modules/generated/sklearn.metrics.consensus_score.html
Assessing the quality of a clustering (video)	https://www.youtube.com/watch?v=Mf6MqIS2ql4
fpc	https://cran.r-project.org/web/packages/fpc/index.html
	https://github.com/FerMatPy/datascience#interpretable-classifiers-and-regressors
skope-rules	https://github.com/scikit-learn-contrib/skope-rules
sklearn-expertsys	https://github.com/tmadl/sklearn-expertsys
	https://github.com/FerMatPy/datascience#multi-label-classification
scikit-multilearn	https://github.com/scikit-multilearn/scikit-multilearn
talk	https://www.youtube.com/watch?v=m-tAASQA7XQ&t=18m57s
	https://github.com/FerMatPy/datascience#signal-processing-and-filtering
Stanford Lecture Series on Fourier Transformation	https://see.stanford.edu/Course/EE261
Youtube	https://www.youtube.com/watch?v=gZNm7L96pfY&list=PLB24BC7956EE040CD&index=1
Lecture Notes	https://see.stanford.edu/materials/lsoftaee261/book-fall-07.pdf
The Scientist & Engineer's Guide to Digital Signal Processing (1999)	https://www.analog.com/en/education/education-library/scientist_engineers_guide.html
Kalman Filter book	https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python
Interactive Tool	https://fiiir.com/
Examples	https://plot.ly/python/fft-filters/
filterpy	https://github.com/rlabbe/filterpy
	https://github.com/FerMatPy/datascience#time-series
statsmodels	https://www.statsmodels.org/dev/tsa.html
seasonal decompose	https://www.statsmodels.org/dev/generated/statsmodels.tsa.seasonal.seasonal_decompose.html
example	https://gist.github.com/balzer82/5cec6ad7adc1b550e7ee
SARIMA	https://www.statsmodels.org/dev/generated/statsmodels.tsa.statespace.sarimax.SARIMAX.html
granger causality	http://www.statsmodels.org/dev/generated/statsmodels.tsa.stattools.grangercausalitytests.html
kats	https://github.com/facebookresearch/kats
prophet	https://github.com/facebook/prophet
pyramid	https://github.com/tgsmith61591/pyramid
pmdarima	https://github.com/tgsmith61591/pmdarima
pyflux	https://github.com/RJT1990/pyflux
atspy	https://github.com/firmai/atspy
pm-prophet	https://github.com/luke14free/pm-prophet
htsprophet	https://github.com/CollinRooney12/htsprophet
nupic	https://github.com/numenta/nupic
tensorflow	https://github.com/tensorflow/tensorflow/
link	https://machinelearningmastery.com/time-series-forecasting-long-short-term-memory-network-python/
link	https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/timeseries
link	https://github.com/hzy46/TensorFlow-Time-Series-Examples
Explain LSTM	https://github.com/slundberg/shap/blob/master/notebooks/deep_explainer/Keras%20LSTM%20for%20IMDB%20Sentiment%20Classification.ipynb
1	https://machinelearningmastery.com/how-to-develop-lstm-models-for-multi-step-time-series-forecasting-of-household-power-consumption/
2	https://github.com/guillaume-chevalier/seq2seq-signal-prediction
3	https://github.com/JEddy92/TimeSeries_Seq2Seq/blob/master/notebooks/TS_Seq2Seq_Intro.ipynb
4	https://github.com/LukeTonin/keras-seq-2-seq-signal-prediction
tspreprocess	https://github.com/MaxBenChrist/tspreprocess
tsfresh	https://github.com/blue-yonder/tsfresh
thunder	https://github.com/thunder-project/thunder
gatspy	https://www.astroml.org/gatspy/
talk	https://www.youtube.com/watch?v=E4NMZyfao2c
gendis	https://github.com/IBCNServices/GENDIS
example	https://github.com/IBCNServices/GENDIS/blob/master/gendis/example.ipynb
tslearn	https://github.com/rtavenar/tslearn
pastas	https://pastas.readthedocs.io/en/latest/examples.html
fastdtw	https://github.com/slaypni/fastdtw
fable	https://www.rdocumentation.org/packages/fable/versions/0.0.0.9000
CausalImpact	https://github.com/tcassou/causal_impact
R package	https://google.github.io/CausalImpact/CausalImpact.html
pydlm	https://github.com/wwrechard/pydlm
R package	https://cran.r-project.org/web/packages/bsts/index.html
Blog post	http://www.unofficialgoogledatascience.com/2017/07/fitting-bayesian-structural-time-series.html
PyAF	https://github.com/antoinecarme/pyaf
luminol	https://github.com/linkedin/luminol
matrixprofile-ts	https://github.com/target/matrixprofile-ts
website	https://www.cs.ucr.edu/~eamonn/MatrixProfile.html
ppt	https://www.cs.ucr.edu/~eamonn/Matrix_Profile_Tutorial_Part1.pdf
alternative	https://github.com/matrix-profile-foundation/mass-ts
stumpy	https://github.com/TDAmeritrade/stumpy
obspy	https://github.com/obspy/obspy
RobustSTL	https://github.com/LeeDoYup/RobustSTL
seglearn	https://github.com/dmbee/seglearn
pyts	https://github.com/johannfaouzi/pyts
Imaging time series	https://pyts.readthedocs.io/en/latest/auto_examples/index.html#imaging-time-series
example	https://gist.github.com/oguiza/c9c373aec07b96047d1ba484f23b7b47
example	https://github.com/kiss90/time-series-classification
sktime	https://github.com/alan-turing-institute/sktime
sktime-dl	https://github.com/uea-machine-learning/sktime-dl
adtk	https://github.com/arundo/adtk
rocket	https://github.com/angus924/rocket
luminaire	https://github.com/zillow/luminaire
	https://github.com/FerMatPy/datascience#time-series-evaluation
TimeSeriesSplit	https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.TimeSeriesSplit.html
tscv	https://github.com/WenjieZ/TSCV
	https://github.com/FerMatPy/datascience#financial-data
1	https://calmcode.io/cvxpy-one/the-stigler-diet.html
2	https://calmcode.io/cvxpy-two/introduction.html
pandas-datareader	https://pandas-datareader.readthedocs.io/en/latest/whatsnew.html
yfinance	https://github.com/ranaroussi/yfinance
findatapy	https://github.com/cuemacro/findatapy
ta	https://github.com/bukosabino/ta
backtrader	https://github.com/mementum/backtrader
surpriver	https://github.com/tradytics/surpriver
ffn	https://github.com/pmorissette/ffn
bt	https://github.com/pmorissette/bt
alpaca-trade-api-python	https://github.com/alpacahq/alpaca-trade-api-python
eiten	https://github.com/tradytics/eiten
tf-quant-finance	https://github.com/google/tf-quant-finance
quantstats	https://github.com/ranaroussi/quantstats
	https://github.com/FerMatPy/datascience#quantopian-stack
pyfolio	https://github.com/quantopian/pyfolio
zipline	https://github.com/quantopian/zipline
alphalens	https://github.com/quantopian/alphalens
empyrical	https://github.com/quantopian/empyrical
trading_calendars	https://github.com/quantopian/trading_calendars
	https://github.com/FerMatPy/datascience#survival-analysis
Time-dependent Cox Model in R	https://stats.stackexchange.com/questions/101353/cox-regression-with-time-varying-covariates
lifelines	https://lifelines.readthedocs.io/en/latest/
talk	https://www.youtube.com/watch?v=aKZQUaNHYb0
talk2	https://www.youtube.com/watch?v=fli-yE5grtY
scikit-survival	https://github.com/sebp/scikit-survival
xgboost	https://github.com/dmlc/xgboost
NHANES example	https://slundberg.github.io/shap/notebooks/NHANES%20I%20Survival%20Model.html
survivalstan	https://github.com/hammerlab/survivalstan
intro	http://www.hammerlab.org/2017/06/26/introducing-survivalstan/
convoys	https://github.com/better/convoys
pysurvival	https://github.com/square/pysurvival
	https://github.com/FerMatPy/datascience#outlier-detection--anomaly-detection
sklearn	https://scikit-learn.org/stable/modules/outlier_detection.html
pyod	https://pyod.readthedocs.io/en/latest/pyod.html
eif	https://github.com/sahandha/eif
AnomalyDetection	https://github.com/twitter/AnomalyDetection
luminol	https://github.com/linkedin/luminol
Talk	https://www.youtube.com/watch?v=U7xdiGc7IRU
Kolmogorov-Smirnov	https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.stats.ks_2samp.html
Wasserstein	https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.wasserstein_distance.html
Energy Distance (Cramer)	https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.energy_distance.html
Kullback-Leibler divergence	https://scipy.github.io/devdocs/generated/scipy.stats.entropy.html
banpei	https://github.com/tsurubee/banpei
telemanom	https://github.com/khundman/telemanom
luminaire	https://github.com/zillow/luminaire
	https://github.com/FerMatPy/datascience#ranking
lightning	https://github.com/scikit-learn-contrib/lightning
	https://github.com/FerMatPy/datascience#scoring
SLIM	https://github.com/ustunb/slim-python
	https://github.com/FerMatPy/datascience#probabilistic-modeling-and-bayes
Intro	https://erikbern.com/2018/10/08/the-hackers-guide-to-uncertainty-estimates.html
Guide	https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers
PyMC3	https://docs.pymc.io/
intro	https://docs.pymc.io/notebooks/getting_started
pomegranate	https://github.com/jmschrei/pomegranate
talk	https://www.youtube.com/watch?v=dE5j6NW-Kzg
pmlearn	https://github.com/pymc-learn/pymc-learn
arviz	https://github.com/arviz-devs/arviz
zhusuan	https://github.com/thu-ml/zhusuan
dowhy	https://github.com/Microsoft/dowhy
edward	https://github.com/blei-lab/edward
Mixture Density Networks (MNDs)	http://edwardlib.org/tutorials/mixture-density-network
MDN Explanation	https://towardsdatascience.com/a-hitchhikers-guide-to-mixture-density-networks-76b435826cca
Pyro	https://github.com/pyro-ppl/pyro
tensorflow probability	https://github.com/tensorflow/probability
talk	https://www.youtube.com/watch?v=BrwKURU-wpk
example	https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/blob/master/Chapter1_Introduction/Ch1_Introduction_TFP.ipynb
bambi	https://github.com/bambinos/bambi
neural-tangents	https://github.com/google/neural-tangents
	https://github.com/FerMatPy/datascience#gaussian-processes
Visualization	http://www.infinitecuriosity.org/vizgp/
Article	https://distill.pub/2019/visual-exploration-gaussian-processes/
GPyOpt	https://github.com/SheffieldML/GPyOpt
GPflow	https://github.com/GPflow/GPflow
gpytorch	https://gpytorch.ai/
	https://github.com/FerMatPy/datascience#stacking-models-and-ensembles
Model Stacking Blog Post	http://blog.kaggle.com/2017/06/15/stacking-made-easy-an-introduction-to-stacknet-by-competitions-grandmaster-marios-michailidis-kazanova/
mlxtend	https://github.com/rasbt/mlxtend
vecstack	https://github.com/vecxoz/vecstack
StackNet	https://github.com/kaz-Anova/StackNet
mlens	https://github.com/flennerhag/mlens
combo	https://github.com/yzhao062/combo
	https://github.com/FerMatPy/datascience#model-evaluation
pycm	https://github.com/sepandhaghighi/pycm
pandas_ml	https://github.com/pandas-ml/pandas-ml
link	http://www.ritchieng.com/machinelearning-learning-curve/
yellowbrick	http://www.scikit-yb.org/en/latest/api/model_selection/learning_curve.html
	https://github.com/FerMatPy/datascience#model-uncertainty
uncertainty-toolbox	https://github.com/uncertainty-toolbox/uncertainty-toolbox
	https://github.com/FerMatPy/datascience#model-explanation-interpretability-feature-importance
Book	https://christophm.github.io/interpretable-ml-book/agnostic.html
Examples	https://github.com/jphall663/interpretable_machine_learning_with_python
shap	https://github.com/slundberg/shap
talk	https://www.youtube.com/watch?v=C80SQe16Rao
treeinterpreter	https://github.com/andosa/treeinterpreter
lime	https://github.com/marcotcr/lime
talk	https://www.youtube.com/watch?v=C80SQe16Rao
Warning (Myth 7)	https://crazyoscarchang.github.io/2019/02/16/seven-myths-in-machine-learning-research/
lime_xgboost	https://github.com/jphall663/lime_xgboost
eli5	https://github.com/TeamHG-Memex/eli5
lofo-importance	https://github.com/aerdem4/lofo-importance
talk	https://www.youtube.com/watch?v=zqsQ2ojj7sE
1	https://www.kaggle.com/divrikwicky/pf-f-lofo-importance-on-adversarial-validation
2	https://www.kaggle.com/divrikwicky/lofo-importance
3	https://www.kaggle.com/divrikwicky/santanderctp-lofo-feature-importance
pybreakdown	https://github.com/MI2DataLab/pyBreakDown
FairML	https://github.com/adebayoj/fairml
pycebox	https://github.com/AustinRochford/PyCEbox
pdpbox	https://github.com/SauceCat/PDPbox
example	https://www.kaggle.com/dansbecker/partial-plots
partial_dependence	https://github.com/nyuvis/partial_dependence
skater	https://github.com/datascienceinc/Skater
anchor	https://github.com/marcotcr/anchor
l2x	https://github.com/Jianbo-Lab/L2X
contrastive_explanation	https://github.com/MarcelRobeer/ContrastiveExplanation
DrWhy	https://github.com/ModelOriented/DrWhy
lucid	https://github.com/tensorflow/lucid
xai	https://github.com/EthicalML/XAI
innvestigate	https://github.com/albermax/innvestigate
dalex	https://github.com/pbiecek/DALEX
interpret	https://github.com/microsoft/interpret
causalml	https://github.com/uber/causalml
	https://github.com/FerMatPy/datascience#automated-machine-learning
AdaNet	https://github.com/tensorflow/adanet
tpot	https://github.com/EpistasisLab/tpot
auto_ml	https://github.com/ClimbsRocks/auto_ml
autokeras	https://github.com/jhfjhfj1/autokeras
nni	https://github.com/Microsoft/nni
automl-gs	https://github.com/minimaxir/automl-gs
mljar	https://github.com/mljar/mljar-supervised
automl_zero	https://github.com/google-research/google-research/tree/master/automl_zero
	https://github.com/FerMatPy/datascience#graph-representation-learning
Karate Club	https://github.com/benedekrozemberczki/karateclub
Pytorch Geometric	https://github.com/rusty1s/pytorch_geometric
DLG	https://github.com/dmlc/dgl
	https://github.com/FerMatPy/datascience#convex-optimization
cvxpy	https://github.com/cvxgrp/cvxpy
1	https://calmcode.io/cvxpy-one/the-stigler-diet.html
2	https://calmcode.io/cvxpy-two/introduction.html
	https://github.com/FerMatPy/datascience#evolutionary-algorithms--optimization
deap	https://github.com/DEAP/deap
evol	https://github.com/godatadriven/evol
talk	https://www.youtube.com/watch?v=68ABAU_V8qI&t=11m49s
platypus	https://github.com/Project-Platypus/Platypus
autograd	https://github.com/HIPS/autograd
nevergrad	https://github.com/facebookresearch/nevergrad
gplearn	https://gplearn.readthedocs.io/en/stable/
blackbox	https://github.com/paulknysh/blackbox
paper	https://www.nature.com/articles/s41598-017-06645-7
DeepSwarm	https://github.com/Pattio/DeepSwarm
	https://github.com/FerMatPy/datascience#hyperparameter-tuning
sklearn	https://scikit-learn.org/stable/index.html
GridSearchCV	https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html
RandomizedSearchCV	https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.RandomizedSearchCV.html
sklearn-deap	https://github.com/rsteca/sklearn-deap
hyperopt	https://github.com/hyperopt/hyperopt
hyperopt-sklearn	https://github.com/hyperopt/hyperopt-sklearn
optuna	https://github.com/pfnet/optuna
Talk	https://www.youtube.com/watch?v=tcrcLRopTX0
skopt	https://scikit-optimize.github.io/
tune	https://ray.readthedocs.io/en/latest/tune.html
hypergraph	https://github.com/aljabr0/hypergraph
bbopt	https://github.com/evhub/bbopt
dragonfly	https://github.com/dragonfly/dragonfly
botorch	https://github.com/pytorch/botorch
ax	https://github.com/facebook/Ax
	https://github.com/FerMatPy/datascience#incremental-learning-online-learning
PassiveAggressiveClassifier	https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.PassiveAggressiveClassifier.html
PassiveAggressiveRegressor	https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.PassiveAggressiveRegressor.html
creme-ml	https://github.com/creme-ml/creme
talk	https://www.youtube.com/watch?v=P3M6dt7bY9U
Kaggler	https://github.com/jeongyoonlee/Kaggler
onelearn	https://github.com/onelearn/onelearn
	https://github.com/FerMatPy/datascience#active-learning
Talk	https://www.youtube.com/watch?v=0efyjq5rWS4
modAL	https://github.com/modAL-python/modAL
	https://github.com/FerMatPy/datascience#reinforcement-learning
YouTube	https://www.youtube.com/playlist?list=PL7-jPKtc4r78-wCZcQn5IqyuWhBZ8fOxT
YouTube	https://www.youtube.com/playlist?list=PLqYmG7hTraZDNJre23vqCGIVpfZ_K2RZs
1	https://jeffbradberry.com/posts/2015/09/intro-to-monte-carlo-tree-search/
2	http://mcts.ai/about/index.html
3	https://medium.com/@quasimik/monte-carlo-tree-search-applied-to-letterpress-34f41c86e238
1	https://github.com/AppliedDataSciencePartners/DeepReinforcementLearning
2	https://web.stanford.edu/~surag/posts/alphazero.html
3	https://github.com/suragnair/alpha-zero-general
Cheat Sheet	https://medium.com/applied-data-science/alphago-zero-explained-in-one-diagram-365f5abf67e0
RLLib	https://ray.readthedocs.io/en/latest/rllib.html
Horizon	https://github.com/facebookresearch/Horizon/
	https://github.com/FerMatPy/datascience#deployment-and-lifecycle-management
	https://github.com/FerMatPy/datascience#docker
Reduce size of docker images (video)	https://www.youtube.com/watch?v=Z1Al4I4Os_A
	https://github.com/FerMatPy/datascience#dependency-management
dephell	https://github.com/dephell/dephell
poetry	https://github.com/python-poetry/poetry
pyup	https://github.com/pyupio/pyup
pypi-timemachine	https://github.com/astrofrog/pypi-timemachine
	https://github.com/FerMatPy/datascience#data-versioning-and-pipelines
dvc	https://github.com/iterative/dvc
hangar	https://github.com/tensorwerk/hangar-py
kedro	https://github.com/quantumblacklabs/kedro
	https://github.com/FerMatPy/datascience#data-science-related
m2cgen	https://github.com/BayesWitnesses/m2cgen
sklearn-porter	https://github.com/nok/sklearn-porter
mlflow	https://mlflow.org/
modelchimp	https://github.com/ModelChimp/modelchimp
skll	https://github.com/EducationalTestingService/skll
BentoML	https://github.com/bentoml/BentoML
dagster	https://github.com/dagster-io/dagster
knockknock	https://github.com/huggingface/knockknock
metaflow	https://github.com/Netflix/metaflow
cortex	https://github.com/cortexlabs/cortex
	https://github.com/FerMatPy/datascience#math-and-background
All kinds of math and statistics resources	https://realnotcomplex.com/
Linear Algebra	https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/index.htm
Matrix Methods in Data Analysis, Signal Processing, and Machine Learning	https://ocw.mit.edu/courses/mathematics/18-065-matrix-methods-in-data-analysis-signal-processing-and-machine-learning-spring-2018/
	https://github.com/FerMatPy/datascience#other
daft	https://github.com/dfm/daft
unyt	https://github.com/yt-project/unyt
scrapy	https://github.com/scrapy/scrapy
VowpalWabbit	https://github.com/VowpalWabbit/vowpal_wabbit
	https://github.com/FerMatPy/datascience#general-python-programming
more_itertools	https://more-itertools.readthedocs.io/en/latest/
funcy	https://github.com/Suor/funcy
dateparser	https://dateparser.readthedocs.io/en/latest/
jellyfish	https://github.com/jamesturk/jellyfish
coloredlogs	https://github.com/xolox/python-coloredlogs
	https://github.com/FerMatPy/datascience#resources
Distill.pub	https://distill.pub/
Machine Learning Videos	https://github.com/dustinvtran/ml-videos
Data Science Notebooks	https://github.com/donnemartin/data-science-ipython-notebooks
Recommender Systems (Microsoft)	https://github.com/Microsoft/Recommenders
The GAN Zoo	https://github.com/hindupuravinash/the-gan-zoo
Datascience Cheatsheets	https://github.com/FavioVazquez/ds-cheatsheets
	https://github.com/FerMatPy/datascience#list-of-books
Mat Kelceys list of cool machine learning books	http://matpalm.com/blog/cool_machine_learning_books/
	https://github.com/FerMatPy/datascience#other-awesome-lists
Awesome Adversarial Machine Learning	https://github.com/yenchenlin/awesome-adversarial-machine-learning
Awesome AI Booksmarks	https://github.com/goodrahstar/my-awesome-AI-bookmarks
Awesome AI on Kubernetes	https://github.com/CognonicLabs/awesome-AI-kubernetes
Awesome Big Data	https://github.com/onurakpolat/awesome-bigdata
Awesome Business Machine Learning	https://github.com/firmai/business-machine-learning
Awesome Causality	https://github.com/rguo12/awesome-causality-algorithms
Awesome Community Detection	https://github.com/benedekrozemberczki/awesome-community-detection
Awesome CSV	https://github.com/secretGeek/AwesomeCSV
Awesome Data Science with Ruby	https://github.com/arbox/data-science-with-ruby
Awesome Dash	https://github.com/ucg8j/awesome-dash
Awesome Decision Trees	https://github.com/benedekrozemberczki/awesome-decision-tree-papers
Awesome Deep Learning	https://github.com/ChristosChristofidis/awesome-deep-learning
Awesome ETL	https://github.com/pawl/awesome-etl
Awesome Financial Machine Learning	https://github.com/firmai/financial-machine-learning
Awesome Fraud Detection	https://github.com/benedekrozemberczki/awesome-fraud-detection-papers
Awesome GAN Applications	https://github.com/nashory/gans-awesome-applications
Awesome Graph Classification	https://github.com/benedekrozemberczki/awesome-graph-classification
Awesome Gradient Boosting	https://github.com/benedekrozemberczki/awesome-gradient-boosting-papers
Awesome Machine Learning	https://github.com/josephmisiti/awesome-machine-learning#python
Awesome Machine Learning Interpretability	https://github.com/jphall663/awesome-machine-learning-interpretability
Awesome Machine Learning Operations	https://github.com/EthicalML/awesome-machine-learning-operations
Awesome Metric Learning	https://github.com/kdhht2334/Survey_of_Deep_Metric_Learning
Awesome Monte Carlo Tree Search	https://github.com/benedekrozemberczki/awesome-monte-carlo-tree-search-papers
Awesome Online Machine Learning	https://github.com/MaxHalford/awesome-online-machine-learning
Awesome Pipeline	https://github.com/pditommaso/awesome-pipeline
Awesome Public APIs	https://github.com/public-apis/public-apis
Awesome Python	https://github.com/vinta/awesome-python
Awesome Python Data Science	https://github.com/krzjoa/awesome-python-datascience
Awesome Python Data Science	https://github.com/thomasjpfan/awesome-python-data-science
Awesome Python Data Science	https://github.com/amitness/toolbox
Awesome Pytorch	https://github.com/bharathgs/Awesome-pytorch-list
Awesome Recommender Systems	https://github.com/grahamjenson/list_of_recommender_systems
Awesome Semantic Segmentation	https://github.com/mrgloom/awesome-semantic-segmentation
Awesome Sentence Embedding	https://github.com/Separius/awesome-sentence-embedding
Awesome Time Series	https://github.com/MaxBenChrist/awesome_time_series_in_python
Awesome Time Series Anomaly Detection	https://github.com/rob-med/awesome-TS-anomaly-detection
	https://github.com/FerMatPy/datascience#things-i-google-a-lot
Color codes	https://github.com/d3/d3-3.x-api-reference/blob/master/Ordinal-Scales.md#categorical-colors
Frequency codes for time series	https://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases
Date parsing codes	https://docs.python.org/3/library/datetime.html#strftime-and-strptime-behavior
Feature Calculators tsfresh	https://github.com/blue-yonder/tsfresh/blob/master/tsfresh/feature_extraction/feature_calculators.py
	https://github.com/FerMatPy/datascience#contributing
contribution guidelines	https://github.com/FerMatPy/datascience/blob/master/CONTRIBUTING.md
	https://github.com/FerMatPy/datascience#license
	https://creativecommons.org/publicdomain/zero/1.0/
Readme	https://github.com/FerMatPy/datascience#readme-ov-file
CC0-1.0 license	https://github.com/FerMatPy/datascience#CC0-1.0-1-ov-file
Contributing	https://github.com/FerMatPy/datascience#contributing-ov-file
Please reload this page	https://github.com/FerMatPy/datascience
Activity	https://github.com/FerMatPy/datascience/activity
0 forks	https://github.com/FerMatPy/datascience/forks
Report repository	https://github.com/contact/report-content?content_url=https%3A%2F%2Fgithub.com%2FFerMatPy%2Fdatascience&report=FerMatPy+%28user%29
Releases	https://github.com/FerMatPy/datascience/releases
Packages 0	https://github.com/users/FerMatPy/packages?repo_name=datascience
Please reload this page	https://github.com/FerMatPy/datascience
Contributors	https://github.com/FerMatPy/datascience/graphs/contributors
Please reload this page	https://github.com/FerMatPy/datascience
	https://github.com
Terms	https://docs.github.com/site-policy/github-terms/github-terms-of-service
Privacy	https://docs.github.com/site-policy/privacy-policies/github-privacy-statement
Security	https://github.com/security
Status	https://www.githubstatus.com/
Community	https://github.community/
Docs	https://docs.github.com/
Contact	https://support.github.com?tags=dotcom-footer

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