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Title: GitHub - FerMatPy/datascience: Curated list of Python resources for data science.

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Repohttps://github.com/Yimeng-Zhang/feature-engineering-and-feature-selection
1http://blog.datadive.net/selecting-good-features-part-i-univariate-selection/
2http://blog.datadive.net/selecting-good-features-part-ii-linear-models-and-regularization/
3http://blog.datadive.net/selecting-good-features-part-iii-random-forests/
4http://blog.datadive.net/selecting-good-features-part-iv-stability-selection-rfe-and-everything-side-by-side/
1https://www.kaggle.com/residentmario/automated-feature-selection-with-sklearn
2https://machinelearningmastery.com/feature-selection-machine-learning-python/
sklearnhttps://scikit-learn.org/stable/modules/classes.html#module-sklearn.feature_selection
eli5https://eli5.readthedocs.io/en/latest/blackbox/permutation_importance.html#feature-selection
scikit-featurehttps://github.com/jundongl/scikit-feature
stability-selectionhttps://github.com/scikit-learn-contrib/stability-selection
scikit-rebatehttps://github.com/EpistasisLab/scikit-rebate
scikit-genetichttps://github.com/manuel-calzolari/sklearn-genetic
boruta_pyhttps://github.com/scikit-learn-contrib/boruta_py
explainationhttps://stats.stackexchange.com/questions/264360/boruta-all-relevant-feature-selection-vs-random-forest-variables-of-importanc/264467
examplehttps://www.kaggle.com/tilii7/boruta-feature-elimination
linselecthttps://github.com/efavdb/linselect
mlxtendhttps://rasbt.github.io/mlxtend/user_guide/feature_selection/ExhaustiveFeatureSelector/
BoostARootahttps://github.com/chasedehan/BoostARoota
INVASEhttps://github.com/jsyoon0823/INVASE
https://patch-diff.githubusercontent.com/FerMatPy/datascience#dimensionality-reduction--representation-learning
https://patch-diff.githubusercontent.com/FerMatPy/datascience#selection
Reviewhttps://members.loria.fr/moberger/Enseignement/AVR/Exposes/TR_Dimensiereductie.pdf
linkhttps://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html
linkhttps://blog.keras.io/building-autoencoders-in-keras.html
linkhttps://scikit-learn.org/stable/modules/generated/sklearn.manifold.Isomap.html#sklearn.manifold.Isomap
linkhttps://scikit-learn.org/stable/modules/generated/sklearn.manifold.LocallyLinearEmbedding.html
linkhttps://scanpy.readthedocs.io/en/stable/api/scanpy.tl.draw_graph.html#scanpy.tl.draw_graph
linkhttps://scikit-learn.org/stable/modules/generated/sklearn.manifold.MDS.html
linkhttps://scanpy.readthedocs.io/en/stable/api/scanpy.tl.diffmap.html
linkhttps://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html#sklearn.manifold.TSNE
linkhttps://github.com/ziyuang/pynerv
paperhttps://www.jmlr.org/papers/volume11/venna10a/venna10a.pdf
linkhttps://github.com/EpistasisLab/scikit-mdr
linkhttps://github.com/lmcinnes/umap
linkhttps://scikit-learn.org/stable/modules/random_projection.html
linkhttps://github.com/beringresearch/ivis
linkhttps://github.com/lightly-ai/lightly
https://patch-diff.githubusercontent.com/FerMatPy/datascience#neural-network-based
lightlyhttps://github.com/lightly-ai/lightly
esvithttps://github.com/microsoft/esvit
MCMLhttps://github.com/pachterlab/MCML
paperhttps://www.biorxiv.org/content/10.1101/2021.08.25.457696v1
https://patch-diff.githubusercontent.com/FerMatPy/datascience#packages
Talkhttps://www.youtube.com/watch?v=9iol3Lk6kyU
tsne introhttps://distill.pub/2016/misread-tsne/
sklearn.manifoldhttps://scikit-learn.org/stable/modules/classes.html#module-sklearn.manifold
sklearn.decompositionhttps://scikit-learn.org/stable/modules/classes.html#module-sklearn.decomposition
sklearn.random_projectionhttps://scikit-learn.org/stable/modules/random_projection.html
princehttps://github.com/MaxHalford/prince
lvdmaatenhttps://lvdmaaten.github.io/tsne/
MulticoreTSNEhttps://github.com/DmitryUlyanov/Multicore-TSNE
FIt-SNEhttps://github.com/KlugerLab/FIt-SNE
umaphttps://github.com/lmcinnes/umap
talkhttps://www.youtube.com/watch?v=nq6iPZVUxZU
explorerhttps://github.com/GrantCuster/umap-explorer
explanationhttps://pair-code.github.io/understanding-umap/
parallel versionhttps://docs.rapids.ai/api/cuml/stable/api.html
sleepwalkhttps://github.com/anders-biostat/sleepwalk/
somocluhttps://github.com/peterwittek/somoclu
scikit-tdahttps://github.com/scikit-tda/scikit-tda
paperhttps://www.nature.com/articles/srep01236
talkhttps://www.youtube.com/watch?v=F2t_ytTLrQ4
talkhttps://www.youtube.com/watch?v=AWoeBzJd7uQ
paperhttps://www.uncg.edu/mat/faculty/cdsmyth/topological-approaches-skin.pdf
giotto-tdahttps://github.com/giotto-ai/giotto-tda
ivishttps://github.com/beringresearch/ivis
trimaphttps://github.com/eamid/trimap
scanpyhttps://github.com/theislab/scanpy
Force-directed graph drawinghttps://scanpy.readthedocs.io/en/stable/api/scanpy.tl.draw_graph.html#scanpy.tl.draw_graph
Diffusion Mapshttps://scanpy.readthedocs.io/en/stable/api/scanpy.tl.diffmap.html
direpackhttps://github.com/SvenSerneels/direpack
DBShttps://cran.r-project.org/web/packages/DatabionicSwarm/vignettes/DatabionicSwarm.html
https://patch-diff.githubusercontent.com/FerMatPy/datascience#training-related
iterative-stratificationhttps://github.com/trent-b/iterative-stratification
livelossplothttps://github.com/stared/livelossplot
https://patch-diff.githubusercontent.com/FerMatPy/datascience#visualization
All chartshttps://datavizproject.com/
Austrian monumentshttps://github.com/njanakiev/austrian-monuments-visualization
cufflinkshttps://github.com/santosjorge/cufflinks
plotlyhttps://plot.ly/
mediumhttps://towardsdatascience.com/the-next-level-of-data-visualization-in-python-dd6e99039d5e
examplehttps://github.com/WillKoehrsen/Data-Analysis/blob/master/plotly/Plotly%20Whirlwind%20Introduction.ipynb
physthttps://github.com/janpipek/physt
talkhttps://www.youtube.com/watch?v=ZG-wH3-Up9Y
notebookhttps://nbviewer.jupyter.org/github/janpipek/pydata2018-berlin/blob/master/notebooks/talk.ipynb
fast-histogramhttps://github.com/astrofrog/fast-histogram
matplotlib_vennhttps://github.com/konstantint/matplotlib-venn
alternativehttps://github.com/penrose/penrose
joypyhttps://github.com/sbebo/joypy
Ridge plots in seabornhttps://seaborn.pydata.org/examples/kde_ridgeplot.html
mosaic plotshttps://www.statsmodels.org/dev/generated/statsmodels.graphics.mosaicplot.mosaic.html
examplehttps://sukhbinder.wordpress.com/2018/09/18/mosaic-plot-in-python/
scikit-plothttps://github.com/reiinakano/scikit-plot
yellowbrickhttps://github.com/DistrictDataLabs/yellowbrick
bokehhttps://bokeh.pydata.org/en/latest/
Exampleshttps://bokeh.pydata.org/en/latest/docs/user_guide/server.html
Exampleshttps://github.com/WillKoehrsen/Bokeh-Python-Visualization
lets-plothttps://github.com/JetBrains/lets-plot/blob/master/README_PYTHON.md
animatplothttps://github.com/t-makaro/animatplot
plotninehttps://github.com/has2k1/plotnine
altairhttps://altair-viz.github.io/
bqplothttps://github.com/bloomberg/bqplot
hvplothttps://github.com/pyviz/hvplot
holoviewshttp://holoviews.org/
dtreevizhttps://github.com/parrt/dtreeviz
chartifyhttps://github.com/spotify/chartify/
VivaGraphJShttps://github.com/anvaka/VivaGraphJS
pmhttps://github.com/anvaka/pm
examplehttps://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-ternaryhttps://github.com/marcharper/python-ternary
falconhttps://github.com/uwdata/falcon
hiplothttps://github.com/facebookresearch/hiplot
visdomhttps://github.com/fossasia/visdom
mpl-scatter-densityhttps://github.com/astrofrog/mpl-scatter-density
ComplexHeatmaphttps://github.com/jokergoo/ComplexHeatmap
largeVishttps://github.com/elbamos/largeVis
https://patch-diff.githubusercontent.com/FerMatPy/datascience#colors
palettablehttps://github.com/jiffyclub/palettable
colorbrewer2https://colorbrewer2.org/#type=sequential&scheme=BuGn&n=3
colorcethttps://github.com/holoviz/colorcet
https://patch-diff.githubusercontent.com/FerMatPy/datascience#dashboards
supersethttps://github.com/apache/superset
streamlithttps://github.com/streamlit/streamlit
Resourceshttps://github.com/marcskovmadsen/awesome-streamlit
Galleryhttps://awesome-streamlit.org/
Componentshttps://www.streamlit.io/components
bokeh-eventshttps://github.com/ash2shukla/streamlit-bokeh-events
dashhttps://dash.plot.ly/gallery
Resourceshttps://github.com/ucg8j/awesome-dash
visdomhttps://github.com/facebookresearch/visdom
panelhttps://panel.pyviz.org/index.html
altair examplehttps://github.com/xhochy/altair-vue-vega-example
Videohttps://www.youtube.com/watch?v=4L568emKOvs
voilahttps://github.com/QuantStack/voila
https://patch-diff.githubusercontent.com/FerMatPy/datascience#survey-tools
samplicshttps://github.com/samplics-org/samplics
https://patch-diff.githubusercontent.com/FerMatPy/datascience#geographical-tools
foliumhttps://github.com/python-visualization/folium
jupyter pluginhttps://github.com/jupyter-widgets/ipyleaflet
gmapshttps://github.com/pbugnion/gmaps
stadiamapshttps://stadiamaps.com/
datashaderhttps://github.com/bokeh/datashader
sklearnhttps://scikit-learn.org/stable/modules/generated/sklearn.neighbors.BallTree.html
Examplehttps://tech.minodes.com/experiments-with-in-memory-spatial-radius-queries-in-python-e40c9e66cf63
pynndescenthttps://github.com/lmcinnes/pynndescent
geocoderhttps://github.com/DenisCarriere/geocoder
talkhttps://www.youtube.com/watch?v=eHRggqAvczE
repohttps://github.com/dillongardner/PyDataSpatialAnalysis
geopandashttps://github.com/geopandas/geopandas
ipynbhttps://github.com/njanakiev/osm-predict-economic-measurements/blob/master/osm-predict-economic-indicators.ipynb
PySalhttps://github.com/pysal/pysal
geographyhttps://github.com/ushahidi/geograpy
cartogramhttps://go-cart.io/cartogram
https://patch-diff.githubusercontent.com/FerMatPy/datascience#recommender-systems
1https://lazyprogrammer.me/tutorial-on-collaborative-filtering-and-matrix-factorization-in-python/
2https://medium.com/@james_aka_yale/the-4-recommendation-engines-that-can-predict-your-movie-tastes-bbec857b8223
2-ipynbhttps://github.com/khanhnamle1994/movielens/blob/master/Content_Based_and_Collaborative_Filtering_Models.ipynb
3https://www.kaggle.com/morrisb/how-to-recommend-anything-deep-recommender
surprisehttps://github.com/NicolasHug/Surprise
talkhttps://www.youtube.com/watch?v=d7iIb_XVkZs
turicreatehttps://github.com/apple/turicreate
implicithttps://github.com/benfred/implicit
spotlighthttps://github.com/maciejkula/spotlight
lightfmhttps://github.com/lyst/lightfm
funk-svdhttps://github.com/gbolmier/funk-svd
pywFMhttps://github.com/jfloff/pywFM
https://patch-diff.githubusercontent.com/FerMatPy/datascience#decision-tree-models
Intro to Decision Trees and Random Forestshttps://victorzhou.com/blog/intro-to-random-forests/
Intro to Gradient Boostinghttp://blog.kaggle.com/2017/01/23/a-kaggle-master-explains-gradient-boosting/
lightgbmhttps://github.com/Microsoft/LightGBM
dochttps://sites.google.com/view/lauraepp/parameters
xgboosthttps://github.com/dmlc/xgboost
dochttps://sites.google.com/view/lauraepp/parameters
link1https://stats.stackexchange.com/questions/255783/confidence-interval-for-xgb-forecast
link2https://towardsdatascience.com/regression-prediction-intervals-with-xgboost-428e0a018b
catboosthttps://github.com/catboost/catboost
h2ohttps://github.com/h2oai/h2o-3
snapmlhttps://www.zurich.ibm.com/snapml/
PyPIhttps://pypi.org/project/snapml/
pycarethttps://github.com/pycaret/pycaret
thundergbmhttps://github.com/Xtra-Computing/thundergbm
h2ohttps://github.com/h2oai/h2o-3
forestcihttps://github.com/scikit-learn-contrib/forest-confidence-interval
scikit-gardenhttps://github.com/scikit-garden/scikit-garden
grfhttps://github.com/grf-labs/grf
dtreevizhttps://github.com/parrt/dtreeviz
Nuancehttps://github.com/SauceCat/Nuance
rfpimphttps://github.com/parrt/random-forest-importances
linkhttp://explained.ai/rf-importance/index.html
treeinterpreterhttps://github.com/andosa/treeinterpreter
bartpyhttps://github.com/JakeColtman/bartpy
infiniteboosthttps://github.com/arogozhnikov/infiniteboost
merfhttps://github.com/manifoldai/merf
videohttps://www.youtube.com/watch?v=gWj4ZwB7f3o
rrcfhttps://github.com/kLabUM/rrcf
groothttps://github.com/tudelft-cda-lab/GROOT
linear-treehttps://github.com/cerlymarco/linear-tree
https://patch-diff.githubusercontent.com/FerMatPy/datascience#natural-language-processing-nlp--text-processing
talkhttps://www.youtube.com/watch?v=6zm9NC9uRkk
nbhttps://nbviewer.jupyter.org/github/skipgram/modern-nlp-in-python/blob/master/executable/Modern_NLP_in_Python.ipynb
nb2https://ahmedbesbes.com/how-to-mine-newsfeed-data-and-extract-interactive-insights-in-python.html
talkhttps://www.youtube.com/watch?time_continue=2&v=sI7VpFNiy_I
Text classification Introhttps://mlwhiz.com/blog/2018/12/17/text_classification/
Preprocessing blog posthttps://mlwhiz.com/blog/2019/01/17/deeplearning_nlp_preprocess/
gensimhttps://radimrehurek.com/gensim/
Examplehttps://markroxor.github.io/gensim/static/notebooks/gensim_news_classification.html
Coherence Modelhttps://radimrehurek.com/gensim/models/coherencemodel.html
GloVehttps://nlp.stanford.edu/projects/glove/
1https://www.kaggle.com/jhoward/improved-lstm-baseline-glove-dropout
2https://www.kaggle.com/sbongo/do-pretrained-embeddings-give-you-the-extra-edge
StarSpacehttps://github.com/facebookresearch/StarSpace
wikipedia2vechttps://wikipedia2vec.github.io/wikipedia2vec/pretrained/
visualizationhttps://projector.tensorflow.org/
magnitudehttps://github.com/plasticityai/magnitude
pyldavishttps://github.com/bmabey/pyLDAvis
spaCyhttps://spacy.io/
NTLKhttps://www.nltk.org/
pytexthttps://github.com/facebookresearch/PyText
fastTexthttps://github.com/facebookresearch/fastText
annoyhttps://github.com/spotify/annoy
faisshttps://github.com/facebookresearch/faiss
pysparnnhttps://github.com/facebookresearch/pysparnn
infomaphttps://github.com/mapequation/infomap
examplehttps://github.com/mapequation/infomap/blob/master/examples/python/infomap-examples.ipynb
datasketchhttps://github.com/ekzhu/datasketch
flairhttps://github.com/zalandoresearch/flair
stanfordnlphttps://github.com/stanfordnlp/stanfordnlp
Chatisticshttps://github.com/MasterScrat/Chatistics
textvechttps://github.com/textvec/textvec
https://patch-diff.githubusercontent.com/FerMatPy/datascience#papers
Search Engine Correlationhttps://arxiv.org/pdf/1107.2691.pdf
https://patch-diff.githubusercontent.com/FerMatPy/datascience#biology
https://patch-diff.githubusercontent.com/FerMatPy/datascience#sequencing
scanpyhttps://github.com/theislab/scanpy
tutorialhttps://github.com/theislab/single-cell-tutorial
https://patch-diff.githubusercontent.com/FerMatPy/datascience#image-related
mahotashttp://luispedro.org/software/mahotas/
examplehttps://github.com/luispedro/python-image-tutorial/blob/master/Segmenting%20cell%20images%20(fluorescent%20microscopy).ipynb
imagepyhttps://github.com/Image-Py/imagepy
CellProfilerhttps://github.com/CellProfiler/CellProfiler
imglybhttps://github.com/imglib/imglyb
talkhttps://www.youtube.com/watch?v=Ddo5z5qGMb8
slideshttps://github.com/hanslovsky/scipy-2019/blob/master/scipy-2019-imglyb.pdf
microscopiumhttps://github.com/microscopium/microscopium
talkhttps://www.youtube.com/watch?v=ytEQl9xs8FQ
cytokithttps://github.com/hammerlab/cytokit
https://patch-diff.githubusercontent.com/FerMatPy/datascience#image-processing
Talkhttps://www.youtube.com/watch?v=Y5GJmnIhvFk
cv2https://github.com/skvark/opencv-python
Gaussian Filterhttps://docs.opencv.org/3.1.0/d4/d13/tutorial_py_filtering.html
Morphological Transformationshttps://docs.opencv.org/3.0-beta/doc/py_tutorials/py_imgproc/py_morphological_ops/py_morphological_ops.html
scikit-imagehttps://github.com/scikit-image/scikit-image
https://patch-diff.githubusercontent.com/FerMatPy/datascience#neural-networks
https://patch-diff.githubusercontent.com/FerMatPy/datascience#tutorials--viewer
Convolutional Neural Networks for Visual Recognitionhttps://cs231n.github.io/
Lessons 1-7https://course.fast.ai/videos/?lesson=1
Lessons 8-14http://course18.fast.ai/lessons/lessons2.html
Tensorflow without a PhDhttps://github.com/GoogleCloudPlatform/tensorflow-without-a-phd
Bloghttps://distill.pub/2017/feature-visualization/
PPThttp://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture12.pdf
Tensorflow Playgroundhttps://playground.tensorflow.org/
Visualization of optimization algorithmshttps://vis.ensmallen.org/
Another visualizationhttps://github.com/jettify/pytorch-optimizer
cutouts-explorerhttps://github.com/mgckind/cutouts-explorer
https://patch-diff.githubusercontent.com/FerMatPy/datascience#image-related-1
imgaughttps://github.com/aleju/imgaug
Augmentorhttps://github.com/mdbloice/Augmentor
keras preprocessinghttps://keras.io/preprocessing/image/
albumentationshttps://github.com/albu/albumentations
augmixhttps://github.com/google-research/augmix
korniahttps://github.com/kornia/kornia
auglyhttps://github.com/facebookresearch/AugLy
https://patch-diff.githubusercontent.com/FerMatPy/datascience#lossfunction-related
SegLosshttps://github.com/JunMa11/SegLoss
https://patch-diff.githubusercontent.com/FerMatPy/datascience#text-related
ktexthttps://github.com/hamelsmu/ktext
textgenrnnhttps://github.com/minimaxir/textgenrnn
ctrlhttps://github.com/salesforce/ctrl
https://patch-diff.githubusercontent.com/FerMatPy/datascience#libs
kerashttps://keras.io/
tensorflowhttps://www.tensorflow.org/
exampleshttps://gist.github.com/candlewill/552fa102352ccce42fd829ae26277d24
timmhttps://github.com/rwightman/pytorch-image-models
keras-contribhttps://github.com/keras-team/keras-contrib
keras-tunerhttps://github.com/keras-team/keras-tuner
hyperashttps://github.com/maxpumperla/hyperas
elephashttps://github.com/maxpumperla/elephas
tflearnhttps://github.com/tflearn/tflearn
tensorlayerhttps://github.com/tensorlayer/tensorlayer
trickshttps://github.com/wagamamaz/tensorlayer-tricks
tensorforcehttps://github.com/reinforceio/tensorforce
fastaihttps://github.com/fastai/fastai
pytorch-optimizerhttps://github.com/jettify/pytorch-optimizer
ignitehttps://github.com/pytorch/ignite
skorchhttps://github.com/dnouri/skorch
talkhttps://www.youtube.com/watch?v=0J7FaLk0bmQ
slideshttps://github.com/thomasjpfan/skorch_talk
autokerashttps://github.com/jhfjhfj1/autokeras
PlotNeuralNethttps://github.com/HarisIqbal88/PlotNeuralNet
lucidhttps://github.com/tensorflow/lucid
Activation Mapshttps://openai.com/blog/introducing-activation-atlases/
tcavhttps://github.com/tensorflow/tcav
AdaBoundhttps://github.com/Luolc/AdaBound
althttps://github.com/titu1994/keras-adabound
foolboxhttps://github.com/bethgelab/foolbox
hiddenlayerhttps://github.com/waleedka/hiddenlayer
imgclsmobhttps://github.com/osmr/imgclsmob
netronhttps://github.com/lutzroeder/netron
torchcvhttps://github.com/donnyyou/torchcv
pytorch-lightninghttps://github.com/PyTorchLightning/PyTorch-lightning
lightlyhttps://github.com/lightly-ai/lightly
https://patch-diff.githubusercontent.com/FerMatPy/datascience#distributed-libs
flexflowhttps://github.com/flexflow/FlexFlow
https://patch-diff.githubusercontent.com/FerMatPy/datascience#architecture-visualization
netronhttps://github.com/lutzroeder/netron
https://patch-diff.githubusercontent.com/FerMatPy/datascience#object-detection--instance-segmentation
segmentation_modelshttps://github.com/qubvel/segmentation_models
yolacthttps://github.com/dbolya/yolact
EfficientDet Pytorchhttps://github.com/toandaominh1997/EfficientDet.Pytorch
EfficientDet Kerashttps://github.com/xuannianz/EfficientDet
detectron2https://github.com/facebookresearch/detectron2
simpledethttps://github.com/TuSimple/simpledet
CenterNethttps://github.com/xingyizhou/CenterNet
FCOShttps://github.com/tianzhi0549/FCOS
norfairhttps://github.com/tryolabs/norfair
https://patch-diff.githubusercontent.com/FerMatPy/datascience#image-annotation
cvathttps://github.com/openvinotoolkit/cvat
pigeonhttps://github.com/agermanidis/pigeon
https://patch-diff.githubusercontent.com/FerMatPy/datascience#image-classification
nfnetshttps://github.com/ypeleg/nfnets-keras
efficientnethttps://github.com/lukemelas/EfficientNet-PyTorch
pyclshttps://github.com/facebookresearch/pycls
https://patch-diff.githubusercontent.com/FerMatPy/datascience#applications-and-snippets
CycleGAN and Pix2pixhttps://github.com/junyanz/pytorch-CycleGAN-and-pix2pix
SPADEhttps://github.com/nvlabs/spade
Entity Embeddings of Categorical Variableshttps://arxiv.org/abs/1604.06737
codehttps://github.com/entron/entity-embedding-rossmann
kagglehttps://www.kaggle.com/aquatic/entity-embedding-neural-net/code
Image Super-Resolutionhttps://github.com/idealo/image-super-resolution
Talkhttps://www.youtube.com/watch?v=dVFZpodqJiI
1https://www.thomasjpfan.com/2018/07/nuclei-image-segmentation-tutorial/
2https://www.thomasjpfan.com/2017/08/hassle-free-unets/
deeplearning-modelshttps://github.com/rasbt/deeplearning-models
https://patch-diff.githubusercontent.com/FerMatPy/datascience#variational-autoencoders-vae
disentanglement_libhttps://github.com/google-research/disentanglement_lib
https://patch-diff.githubusercontent.com/FerMatPy/datascience#graph-based-neural-networks
How to do Deep Learning on Graphs with Graph Convolutional Networkshttps://towardsdatascience.com/how-to-do-deep-learning-on-graphs-with-graph-convolutional-networks-7d2250723780
Introduction To Graph Convolutional Networkshttp://tkipf.github.io/graph-convolutional-networks/
An attempt at demystifying graph deep learninghttps://ericmjl.github.io/essays-on-data-science/machine-learning/graph-nets/
ogbhttps://ogb.stanford.edu/
networkxhttps://github.com/networkx/networkx
cugraphhttps://github.com/rapidsai/cugraph
pytorch-geometrichttps://github.com/rusty1s/pytorch_geometric
dglhttps://github.com/dmlc/dgl
graph_netshttps://github.com/deepmind/graph_nets
https://patch-diff.githubusercontent.com/FerMatPy/datascience#other-neural-network-and-deep-learning-frameworks
caffehttps://github.com/BVLC/caffe
pretrained modelshttps://github.com/BVLC/caffe/wiki/Model-Zoo
mxnethttps://github.com/apache/incubator-mxnet
bookhttps://d2l.ai/index.html
https://patch-diff.githubusercontent.com/FerMatPy/datascience#model-conversion
hummingbirdhttps://github.com/microsoft/hummingbird
https://patch-diff.githubusercontent.com/FerMatPy/datascience#gpu
cuMLhttps://github.com/rapidsai/cuml
Introhttps://www.youtube.com/watch?v=6XzS5XcpicM&t=2m50s
thundergbmhttps://github.com/Xtra-Computing/thundergbm
thundersvmhttps://github.com/Xtra-Computing/thundersvm
videohttps://www.youtube.com/watch?v=Jxxs_moibog
https://patch-diff.githubusercontent.com/FerMatPy/datascience#regression
slideshttps://cs.adelaide.edu.au/~chhshen/teaching/ML_SVR.pdf
forumhttps://www.quora.com/How-does-support-vector-regression-work
paperhttp://alex.smola.org/papers/2003/SmoSch03b.pdf
pyearthhttps://github.com/scikit-learn-contrib/py-earth
tutorialhttps://uc-r.github.io/mars
pygamhttps://github.com/dswah/pyGAM
Explanationhttps://multithreaded.stitchfix.com/blog/2015/07/30/gam/
GLRMhttps://github.com/madeleineudell/LowRankModels.jl
tweediehttps://xgboost.readthedocs.io/en/latest/parameter.html#parameters-for-tweedie-regression-objective-reg-tweedie
Talkhttps://www.youtube.com/watch?v=-o0lpHBq85I
https://patch-diff.githubusercontent.com/FerMatPy/datascience#classification
Talkhttps://www.youtube.com/watch?v=DkLPYccEJ8Y
Notebookhttps://github.com/ianozsvald/data_science_delivered/blob/master/ml_creating_correct_capable_classifiers.ipynb
Blog post: Probability Scoringhttps://machinelearningmastery.com/how-to-score-probability-predictions-in-python/
All classification metricshttp://rali.iro.umontreal.ca/rali/sites/default/files/publis/SokolovaLapalme-JIPM09.pdf
DESlibhttps://github.com/scikit-learn-contrib/DESlib
human-learnhttps://github.com/koaning/human-learn
https://patch-diff.githubusercontent.com/FerMatPy/datascience#metric-learning
Contrastive Representation Learninghttps://lilianweng.github.io/lil-log/2021/05/31/contrastive-representation-learning.html
metric-learnhttps://github.com/scikit-learn-contrib/metric-learn
pytorch-metric-learninghttps://github.com/KevinMusgrave/pytorch-metric-learning
deep_metric_learninghttps://github.com/ronekko/deep_metric_learning
ivishttps://bering-ivis.readthedocs.io/en/latest/supervised.html
https://patch-diff.githubusercontent.com/FerMatPy/datascience#distance-functions
scipy.spatialhttps://docs.scipy.org/doc/scipy/reference/spatial.distance.html
pyemdhttps://github.com/wmayner/pyemd
OpenCV implementationhttps://docs.opencv.org/3.4/d6/dc7/group__imgproc__hist.html
POT implementationhttps://pythonot.github.io/auto_examples/plot_OT_2D_samples.html
dcorhttps://github.com/vnmabus/dcor
GeomLosshttps://www.kernel-operations.io/geomloss/
https://patch-diff.githubusercontent.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 Methodshttps://arxiv.org/pdf/1801.07648.pdf
hdbscanhttps://github.com/scikit-learn-contrib/hdbscan
talkhttps://www.youtube.com/watch?v=dGsxd67IFiU
bloghttps://towardsdatascience.com/understanding-hdbscan-and-density-based-clustering-121dbee1320e
pyclusteringhttps://github.com/annoviko/pyclustering
FCPShttps://github.com/Mthrun/FCPS
GaussianMixturehttps://scikit-learn.org/stable/modules/generated/sklearn.mixture.GaussianMixture.html
videohttps://www.youtube.com/watch?v=aICqoAG5BXQ
nmslibhttps://github.com/nmslib/nmslib
buckshotpphttps://github.com/zjohn77/buckshotpp
merfhttps://github.com/manifoldai/merf
videohttps://www.youtube.com/watch?v=gWj4ZwB7f3o
tree-SNEhttps://github.com/isaacrob/treesne
MiniSomhttps://github.com/JustGlowing/minisom
distribution_clusteringhttps://github.com/EricElmoznino/distribution_clustering
paperhttps://arxiv.org/abs/1804.02624
related paperhttps://arxiv.org/abs/2003.07770
althttps://github.com/r0f1/distribution_clustering
phenographhttps://github.com/dpeerlab/phenograph
https://patch-diff.githubusercontent.com/FerMatPy/datascience#clustering-evalutation
Wagner, Wagner - Comparing Clusterings - An Overviewhttps://publikationen.bibliothek.kit.edu/1000011477/812079
Adjusted Rand Indexhttps://scikit-learn.org/stable/modules/generated/sklearn.metrics.adjusted_rand_score.html
Normalized Mutual Informationhttps://scikit-learn.org/stable/modules/generated/sklearn.metrics.normalized_mutual_info_score.html
Adjusted Mutual Informationhttps://scikit-learn.org/stable/modules/generated/sklearn.metrics.adjusted_mutual_info_score.html
Fowlkes-Mallows Scorehttps://scikit-learn.org/stable/modules/generated/sklearn.metrics.fowlkes_mallows_score.html
Silhouette Coefficienthttps://scikit-learn.org/stable/modules/generated/sklearn.metrics.silhouette_score.html
Variation of Informationhttps://gist.github.com/jwcarr/626cbc80e0006b526688
Juliahttps://clusteringjl.readthedocs.io/en/latest/varinfo.html
Pair Confusion Matrixhttps://scikit-learn.org/stable/modules/generated/sklearn.metrics.cluster.pair_confusion_matrix.html
Consensus Scorehttps://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
fpchttps://cran.r-project.org/web/packages/fpc/index.html
https://patch-diff.githubusercontent.com/FerMatPy/datascience#interpretable-classifiers-and-regressors
skope-ruleshttps://github.com/scikit-learn-contrib/skope-rules
sklearn-expertsyshttps://github.com/tmadl/sklearn-expertsys
https://patch-diff.githubusercontent.com/FerMatPy/datascience#multi-label-classification
scikit-multilearnhttps://github.com/scikit-multilearn/scikit-multilearn
talkhttps://www.youtube.com/watch?v=m-tAASQA7XQ&t=18m57s
https://patch-diff.githubusercontent.com/FerMatPy/datascience#signal-processing-and-filtering
Stanford Lecture Series on Fourier Transformationhttps://see.stanford.edu/Course/EE261
Youtubehttps://www.youtube.com/watch?v=gZNm7L96pfY&list=PLB24BC7956EE040CD&index=1
Lecture Noteshttps://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 bookhttps://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python
Interactive Toolhttps://fiiir.com/
Exampleshttps://plot.ly/python/fft-filters/
filterpyhttps://github.com/rlabbe/filterpy
https://patch-diff.githubusercontent.com/FerMatPy/datascience#time-series
statsmodelshttps://www.statsmodels.org/dev/tsa.html
seasonal decomposehttps://www.statsmodels.org/dev/generated/statsmodels.tsa.seasonal.seasonal_decompose.html
examplehttps://gist.github.com/balzer82/5cec6ad7adc1b550e7ee
SARIMAhttps://www.statsmodels.org/dev/generated/statsmodels.tsa.statespace.sarimax.SARIMAX.html
granger causalityhttp://www.statsmodels.org/dev/generated/statsmodels.tsa.stattools.grangercausalitytests.html
katshttps://github.com/facebookresearch/kats
prophethttps://github.com/facebook/prophet
pyramidhttps://github.com/tgsmith61591/pyramid
pmdarimahttps://github.com/tgsmith61591/pmdarima
pyfluxhttps://github.com/RJT1990/pyflux
atspyhttps://github.com/firmai/atspy
pm-prophethttps://github.com/luke14free/pm-prophet
htsprophethttps://github.com/CollinRooney12/htsprophet
nupichttps://github.com/numenta/nupic
tensorflowhttps://github.com/tensorflow/tensorflow/
linkhttps://machinelearningmastery.com/time-series-forecasting-long-short-term-memory-network-python/
linkhttps://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/timeseries
linkhttps://github.com/hzy46/TensorFlow-Time-Series-Examples
Explain LSTMhttps://github.com/slundberg/shap/blob/master/notebooks/deep_explainer/Keras%20LSTM%20for%20IMDB%20Sentiment%20Classification.ipynb
1https://machinelearningmastery.com/how-to-develop-lstm-models-for-multi-step-time-series-forecasting-of-household-power-consumption/
2https://github.com/guillaume-chevalier/seq2seq-signal-prediction
3https://github.com/JEddy92/TimeSeries_Seq2Seq/blob/master/notebooks/TS_Seq2Seq_Intro.ipynb
4https://github.com/LukeTonin/keras-seq-2-seq-signal-prediction
tspreprocesshttps://github.com/MaxBenChrist/tspreprocess
tsfreshhttps://github.com/blue-yonder/tsfresh
thunderhttps://github.com/thunder-project/thunder
gatspyhttps://www.astroml.org/gatspy/
talkhttps://www.youtube.com/watch?v=E4NMZyfao2c
gendishttps://github.com/IBCNServices/GENDIS
examplehttps://github.com/IBCNServices/GENDIS/blob/master/gendis/example.ipynb
tslearnhttps://github.com/rtavenar/tslearn
pastashttps://pastas.readthedocs.io/en/latest/examples.html
fastdtwhttps://github.com/slaypni/fastdtw
fablehttps://www.rdocumentation.org/packages/fable/versions/0.0.0.9000
CausalImpacthttps://github.com/tcassou/causal_impact
R packagehttps://google.github.io/CausalImpact/CausalImpact.html
pydlmhttps://github.com/wwrechard/pydlm
R packagehttps://cran.r-project.org/web/packages/bsts/index.html
Blog posthttp://www.unofficialgoogledatascience.com/2017/07/fitting-bayesian-structural-time-series.html
PyAFhttps://github.com/antoinecarme/pyaf
luminolhttps://github.com/linkedin/luminol
matrixprofile-tshttps://github.com/target/matrixprofile-ts
websitehttps://www.cs.ucr.edu/~eamonn/MatrixProfile.html
ppthttps://www.cs.ucr.edu/~eamonn/Matrix_Profile_Tutorial_Part1.pdf
alternativehttps://github.com/matrix-profile-foundation/mass-ts
stumpyhttps://github.com/TDAmeritrade/stumpy
obspyhttps://github.com/obspy/obspy
RobustSTLhttps://github.com/LeeDoYup/RobustSTL
seglearnhttps://github.com/dmbee/seglearn
pytshttps://github.com/johannfaouzi/pyts
Imaging time serieshttps://pyts.readthedocs.io/en/latest/auto_examples/index.html#imaging-time-series
examplehttps://gist.github.com/oguiza/c9c373aec07b96047d1ba484f23b7b47
examplehttps://github.com/kiss90/time-series-classification
sktimehttps://github.com/alan-turing-institute/sktime
sktime-dlhttps://github.com/uea-machine-learning/sktime-dl
adtkhttps://github.com/arundo/adtk
rockethttps://github.com/angus924/rocket
luminairehttps://github.com/zillow/luminaire
https://patch-diff.githubusercontent.com/FerMatPy/datascience#time-series-evaluation
TimeSeriesSplithttps://scikit-learn.org/stable/modules/generated/sklearn.model_selection.TimeSeriesSplit.html
tscvhttps://github.com/WenjieZ/TSCV
https://patch-diff.githubusercontent.com/FerMatPy/datascience#financial-data
1https://calmcode.io/cvxpy-one/the-stigler-diet.html
2https://calmcode.io/cvxpy-two/introduction.html
pandas-datareaderhttps://pandas-datareader.readthedocs.io/en/latest/whatsnew.html
yfinancehttps://github.com/ranaroussi/yfinance
findatapyhttps://github.com/cuemacro/findatapy
tahttps://github.com/bukosabino/ta
backtraderhttps://github.com/mementum/backtrader
surpriverhttps://github.com/tradytics/surpriver
ffnhttps://github.com/pmorissette/ffn
bthttps://github.com/pmorissette/bt
alpaca-trade-api-pythonhttps://github.com/alpacahq/alpaca-trade-api-python
eitenhttps://github.com/tradytics/eiten
tf-quant-financehttps://github.com/google/tf-quant-finance
quantstatshttps://github.com/ranaroussi/quantstats
https://patch-diff.githubusercontent.com/FerMatPy/datascience#quantopian-stack
pyfoliohttps://github.com/quantopian/pyfolio
ziplinehttps://github.com/quantopian/zipline
alphalenshttps://github.com/quantopian/alphalens
empyricalhttps://github.com/quantopian/empyrical
trading_calendarshttps://github.com/quantopian/trading_calendars
https://patch-diff.githubusercontent.com/FerMatPy/datascience#survival-analysis
Time-dependent Cox Model in Rhttps://stats.stackexchange.com/questions/101353/cox-regression-with-time-varying-covariates
lifelineshttps://lifelines.readthedocs.io/en/latest/
talkhttps://www.youtube.com/watch?v=aKZQUaNHYb0
talk2https://www.youtube.com/watch?v=fli-yE5grtY
scikit-survivalhttps://github.com/sebp/scikit-survival
xgboosthttps://github.com/dmlc/xgboost
NHANES examplehttps://slundberg.github.io/shap/notebooks/NHANES%20I%20Survival%20Model.html
survivalstanhttps://github.com/hammerlab/survivalstan
introhttp://www.hammerlab.org/2017/06/26/introducing-survivalstan/
convoyshttps://github.com/better/convoys
pysurvivalhttps://github.com/square/pysurvival
https://patch-diff.githubusercontent.com/FerMatPy/datascience#outlier-detection--anomaly-detection
sklearnhttps://scikit-learn.org/stable/modules/outlier_detection.html
pyodhttps://pyod.readthedocs.io/en/latest/pyod.html
eifhttps://github.com/sahandha/eif
AnomalyDetectionhttps://github.com/twitter/AnomalyDetection
luminolhttps://github.com/linkedin/luminol
Talkhttps://www.youtube.com/watch?v=U7xdiGc7IRU
Kolmogorov-Smirnovhttps://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.stats.ks_2samp.html
Wassersteinhttps://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 divergencehttps://scipy.github.io/devdocs/generated/scipy.stats.entropy.html
banpeihttps://github.com/tsurubee/banpei
telemanomhttps://github.com/khundman/telemanom
luminairehttps://github.com/zillow/luminaire
https://patch-diff.githubusercontent.com/FerMatPy/datascience#ranking
lightninghttps://github.com/scikit-learn-contrib/lightning
https://patch-diff.githubusercontent.com/FerMatPy/datascience#scoring
SLIMhttps://github.com/ustunb/slim-python
https://patch-diff.githubusercontent.com/FerMatPy/datascience#probabilistic-modeling-and-bayes
Introhttps://erikbern.com/2018/10/08/the-hackers-guide-to-uncertainty-estimates.html
Guidehttps://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers
PyMC3https://docs.pymc.io/
introhttps://docs.pymc.io/notebooks/getting_started
pomegranatehttps://github.com/jmschrei/pomegranate
talkhttps://www.youtube.com/watch?v=dE5j6NW-Kzg
pmlearnhttps://github.com/pymc-learn/pymc-learn
arvizhttps://github.com/arviz-devs/arviz
zhusuanhttps://github.com/thu-ml/zhusuan
dowhyhttps://github.com/Microsoft/dowhy
edwardhttps://github.com/blei-lab/edward
Mixture Density Networks (MNDs)http://edwardlib.org/tutorials/mixture-density-network
MDN Explanationhttps://towardsdatascience.com/a-hitchhikers-guide-to-mixture-density-networks-76b435826cca
Pyrohttps://github.com/pyro-ppl/pyro
tensorflow probabilityhttps://github.com/tensorflow/probability
talkhttps://www.youtube.com/watch?v=BrwKURU-wpk
examplehttps://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/blob/master/Chapter1_Introduction/Ch1_Introduction_TFP.ipynb
bambihttps://github.com/bambinos/bambi
neural-tangentshttps://github.com/google/neural-tangents
https://patch-diff.githubusercontent.com/FerMatPy/datascience#gaussian-processes
Visualizationhttp://www.infinitecuriosity.org/vizgp/
Articlehttps://distill.pub/2019/visual-exploration-gaussian-processes/
GPyOpthttps://github.com/SheffieldML/GPyOpt
GPflowhttps://github.com/GPflow/GPflow
gpytorchhttps://gpytorch.ai/
https://patch-diff.githubusercontent.com/FerMatPy/datascience#stacking-models-and-ensembles
Model Stacking Blog Posthttp://blog.kaggle.com/2017/06/15/stacking-made-easy-an-introduction-to-stacknet-by-competitions-grandmaster-marios-michailidis-kazanova/
mlxtendhttps://github.com/rasbt/mlxtend
vecstackhttps://github.com/vecxoz/vecstack
StackNethttps://github.com/kaz-Anova/StackNet
mlenshttps://github.com/flennerhag/mlens
combohttps://github.com/yzhao062/combo
https://patch-diff.githubusercontent.com/FerMatPy/datascience#model-evaluation
pycmhttps://github.com/sepandhaghighi/pycm
pandas_mlhttps://github.com/pandas-ml/pandas-ml
linkhttp://www.ritchieng.com/machinelearning-learning-curve/
yellowbrickhttp://www.scikit-yb.org/en/latest/api/model_selection/learning_curve.html
https://patch-diff.githubusercontent.com/FerMatPy/datascience#model-uncertainty
uncertainty-toolboxhttps://github.com/uncertainty-toolbox/uncertainty-toolbox
https://patch-diff.githubusercontent.com/FerMatPy/datascience#model-explanation-interpretability-feature-importance
Bookhttps://christophm.github.io/interpretable-ml-book/agnostic.html
Exampleshttps://github.com/jphall663/interpretable_machine_learning_with_python
shaphttps://github.com/slundberg/shap
talkhttps://www.youtube.com/watch?v=C80SQe16Rao
treeinterpreterhttps://github.com/andosa/treeinterpreter
limehttps://github.com/marcotcr/lime
talkhttps://www.youtube.com/watch?v=C80SQe16Rao
Warning (Myth 7)https://crazyoscarchang.github.io/2019/02/16/seven-myths-in-machine-learning-research/
lime_xgboosthttps://github.com/jphall663/lime_xgboost
eli5https://github.com/TeamHG-Memex/eli5
lofo-importancehttps://github.com/aerdem4/lofo-importance
talkhttps://www.youtube.com/watch?v=zqsQ2ojj7sE
1https://www.kaggle.com/divrikwicky/pf-f-lofo-importance-on-adversarial-validation
2https://www.kaggle.com/divrikwicky/lofo-importance
3https://www.kaggle.com/divrikwicky/santanderctp-lofo-feature-importance
pybreakdownhttps://github.com/MI2DataLab/pyBreakDown
FairMLhttps://github.com/adebayoj/fairml
pyceboxhttps://github.com/AustinRochford/PyCEbox
pdpboxhttps://github.com/SauceCat/PDPbox
examplehttps://www.kaggle.com/dansbecker/partial-plots
partial_dependencehttps://github.com/nyuvis/partial_dependence
skaterhttps://github.com/datascienceinc/Skater
anchorhttps://github.com/marcotcr/anchor
l2xhttps://github.com/Jianbo-Lab/L2X
contrastive_explanationhttps://github.com/MarcelRobeer/ContrastiveExplanation
DrWhyhttps://github.com/ModelOriented/DrWhy
lucidhttps://github.com/tensorflow/lucid
xaihttps://github.com/EthicalML/XAI
innvestigatehttps://github.com/albermax/innvestigate
dalexhttps://github.com/pbiecek/DALEX
interprethttps://github.com/microsoft/interpret
causalmlhttps://github.com/uber/causalml
https://patch-diff.githubusercontent.com/FerMatPy/datascience#automated-machine-learning
AdaNethttps://github.com/tensorflow/adanet
tpothttps://github.com/EpistasisLab/tpot
auto_mlhttps://github.com/ClimbsRocks/auto_ml
autokerashttps://github.com/jhfjhfj1/autokeras
nnihttps://github.com/Microsoft/nni
automl-gshttps://github.com/minimaxir/automl-gs
mljarhttps://github.com/mljar/mljar-supervised
automl_zerohttps://github.com/google-research/google-research/tree/master/automl_zero
https://patch-diff.githubusercontent.com/FerMatPy/datascience#graph-representation-learning
Karate Clubhttps://github.com/benedekrozemberczki/karateclub
Pytorch Geometrichttps://github.com/rusty1s/pytorch_geometric
DLGhttps://github.com/dmlc/dgl
https://patch-diff.githubusercontent.com/FerMatPy/datascience#convex-optimization
cvxpyhttps://github.com/cvxgrp/cvxpy
1https://calmcode.io/cvxpy-one/the-stigler-diet.html
2https://calmcode.io/cvxpy-two/introduction.html
https://patch-diff.githubusercontent.com/FerMatPy/datascience#evolutionary-algorithms--optimization
deaphttps://github.com/DEAP/deap
evolhttps://github.com/godatadriven/evol
talkhttps://www.youtube.com/watch?v=68ABAU_V8qI&t=11m49s
platypushttps://github.com/Project-Platypus/Platypus
autogradhttps://github.com/HIPS/autograd
nevergradhttps://github.com/facebookresearch/nevergrad
gplearnhttps://gplearn.readthedocs.io/en/stable/
blackboxhttps://github.com/paulknysh/blackbox
paperhttps://www.nature.com/articles/s41598-017-06645-7
DeepSwarmhttps://github.com/Pattio/DeepSwarm
https://patch-diff.githubusercontent.com/FerMatPy/datascience#hyperparameter-tuning
sklearnhttps://scikit-learn.org/stable/index.html
GridSearchCVhttps://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html
RandomizedSearchCVhttps://scikit-learn.org/stable/modules/generated/sklearn.model_selection.RandomizedSearchCV.html
sklearn-deaphttps://github.com/rsteca/sklearn-deap
hyperopthttps://github.com/hyperopt/hyperopt
hyperopt-sklearnhttps://github.com/hyperopt/hyperopt-sklearn
optunahttps://github.com/pfnet/optuna
Talkhttps://www.youtube.com/watch?v=tcrcLRopTX0
skopthttps://scikit-optimize.github.io/
tunehttps://ray.readthedocs.io/en/latest/tune.html
hypergraphhttps://github.com/aljabr0/hypergraph
bbopthttps://github.com/evhub/bbopt
dragonflyhttps://github.com/dragonfly/dragonfly
botorchhttps://github.com/pytorch/botorch
axhttps://github.com/facebook/Ax
https://patch-diff.githubusercontent.com/FerMatPy/datascience#incremental-learning-online-learning
PassiveAggressiveClassifierhttps://scikit-learn.org/stable/modules/generated/sklearn.linear_model.PassiveAggressiveClassifier.html
PassiveAggressiveRegressorhttps://scikit-learn.org/stable/modules/generated/sklearn.linear_model.PassiveAggressiveRegressor.html
creme-mlhttps://github.com/creme-ml/creme
talkhttps://www.youtube.com/watch?v=P3M6dt7bY9U
Kagglerhttps://github.com/jeongyoonlee/Kaggler
onelearnhttps://github.com/onelearn/onelearn
https://patch-diff.githubusercontent.com/FerMatPy/datascience#active-learning
Talkhttps://www.youtube.com/watch?v=0efyjq5rWS4
modALhttps://github.com/modAL-python/modAL
https://patch-diff.githubusercontent.com/FerMatPy/datascience#reinforcement-learning
YouTubehttps://www.youtube.com/playlist?list=PL7-jPKtc4r78-wCZcQn5IqyuWhBZ8fOxT
YouTubehttps://www.youtube.com/playlist?list=PLqYmG7hTraZDNJre23vqCGIVpfZ_K2RZs
1https://jeffbradberry.com/posts/2015/09/intro-to-monte-carlo-tree-search/
2http://mcts.ai/about/index.html
3https://medium.com/@quasimik/monte-carlo-tree-search-applied-to-letterpress-34f41c86e238
1https://github.com/AppliedDataSciencePartners/DeepReinforcementLearning
2https://web.stanford.edu/~surag/posts/alphazero.html
3https://github.com/suragnair/alpha-zero-general
Cheat Sheethttps://medium.com/applied-data-science/alphago-zero-explained-in-one-diagram-365f5abf67e0
RLLibhttps://ray.readthedocs.io/en/latest/rllib.html
Horizonhttps://github.com/facebookresearch/Horizon/
https://patch-diff.githubusercontent.com/FerMatPy/datascience#deployment-and-lifecycle-management
https://patch-diff.githubusercontent.com/FerMatPy/datascience#docker
Reduce size of docker images (video)https://www.youtube.com/watch?v=Z1Al4I4Os_A
https://patch-diff.githubusercontent.com/FerMatPy/datascience#dependency-management
dephellhttps://github.com/dephell/dephell
poetryhttps://github.com/python-poetry/poetry
pyuphttps://github.com/pyupio/pyup
pypi-timemachinehttps://github.com/astrofrog/pypi-timemachine
https://patch-diff.githubusercontent.com/FerMatPy/datascience#data-versioning-and-pipelines
dvchttps://github.com/iterative/dvc
hangarhttps://github.com/tensorwerk/hangar-py
kedrohttps://github.com/quantumblacklabs/kedro
https://patch-diff.githubusercontent.com/FerMatPy/datascience#data-science-related
m2cgenhttps://github.com/BayesWitnesses/m2cgen
sklearn-porterhttps://github.com/nok/sklearn-porter
mlflowhttps://mlflow.org/
modelchimphttps://github.com/ModelChimp/modelchimp
skllhttps://github.com/EducationalTestingService/skll
BentoMLhttps://github.com/bentoml/BentoML
dagsterhttps://github.com/dagster-io/dagster
knockknockhttps://github.com/huggingface/knockknock
metaflowhttps://github.com/Netflix/metaflow
cortexhttps://github.com/cortexlabs/cortex
https://patch-diff.githubusercontent.com/FerMatPy/datascience#math-and-background
All kinds of math and statistics resourceshttps://realnotcomplex.com/
Linear Algebrahttps://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://patch-diff.githubusercontent.com/FerMatPy/datascience#other
dafthttps://github.com/dfm/daft
unythttps://github.com/yt-project/unyt
scrapyhttps://github.com/scrapy/scrapy
VowpalWabbithttps://github.com/VowpalWabbit/vowpal_wabbit
https://patch-diff.githubusercontent.com/FerMatPy/datascience#general-python-programming
more_itertoolshttps://more-itertools.readthedocs.io/en/latest/
funcyhttps://github.com/Suor/funcy
dateparserhttps://dateparser.readthedocs.io/en/latest/
jellyfishhttps://github.com/jamesturk/jellyfish
coloredlogshttps://github.com/xolox/python-coloredlogs
https://patch-diff.githubusercontent.com/FerMatPy/datascience#resources
Distill.pubhttps://distill.pub/
Machine Learning Videoshttps://github.com/dustinvtran/ml-videos
Data Science Notebookshttps://github.com/donnemartin/data-science-ipython-notebooks
Recommender Systems (Microsoft)https://github.com/Microsoft/Recommenders
The GAN Zoohttps://github.com/hindupuravinash/the-gan-zoo
Datascience Cheatsheetshttps://github.com/FavioVazquez/ds-cheatsheets
https://patch-diff.githubusercontent.com/FerMatPy/datascience#list-of-books
Mat Kelceys list of cool machine learning bookshttp://matpalm.com/blog/cool_machine_learning_books/
https://patch-diff.githubusercontent.com/FerMatPy/datascience#other-awesome-lists
Awesome Adversarial Machine Learninghttps://github.com/yenchenlin/awesome-adversarial-machine-learning
Awesome AI Booksmarkshttps://github.com/goodrahstar/my-awesome-AI-bookmarks
Awesome AI on Kuberneteshttps://github.com/CognonicLabs/awesome-AI-kubernetes
Awesome Big Datahttps://github.com/onurakpolat/awesome-bigdata
Awesome Business Machine Learninghttps://github.com/firmai/business-machine-learning
Awesome Causalityhttps://github.com/rguo12/awesome-causality-algorithms
Awesome Community Detectionhttps://github.com/benedekrozemberczki/awesome-community-detection
Awesome CSVhttps://github.com/secretGeek/AwesomeCSV
Awesome Data Science with Rubyhttps://github.com/arbox/data-science-with-ruby
Awesome Dashhttps://github.com/ucg8j/awesome-dash
Awesome Decision Treeshttps://github.com/benedekrozemberczki/awesome-decision-tree-papers
Awesome Deep Learninghttps://github.com/ChristosChristofidis/awesome-deep-learning
Awesome ETLhttps://github.com/pawl/awesome-etl
Awesome Financial Machine Learninghttps://github.com/firmai/financial-machine-learning
Awesome Fraud Detectionhttps://github.com/benedekrozemberczki/awesome-fraud-detection-papers
Awesome GAN Applicationshttps://github.com/nashory/gans-awesome-applications
Awesome Graph Classificationhttps://github.com/benedekrozemberczki/awesome-graph-classification
Awesome Gradient Boostinghttps://github.com/benedekrozemberczki/awesome-gradient-boosting-papers
Awesome Machine Learninghttps://github.com/josephmisiti/awesome-machine-learning#python
Awesome Machine Learning Interpretabilityhttps://github.com/jphall663/awesome-machine-learning-interpretability
Awesome Machine Learning Operationshttps://github.com/EthicalML/awesome-machine-learning-operations
Awesome Metric Learninghttps://github.com/kdhht2334/Survey_of_Deep_Metric_Learning
Awesome Monte Carlo Tree Searchhttps://github.com/benedekrozemberczki/awesome-monte-carlo-tree-search-papers
Awesome Online Machine Learninghttps://github.com/MaxHalford/awesome-online-machine-learning
Awesome Pipelinehttps://github.com/pditommaso/awesome-pipeline
Awesome Public APIshttps://github.com/public-apis/public-apis
Awesome Pythonhttps://github.com/vinta/awesome-python
Awesome Python Data Sciencehttps://github.com/krzjoa/awesome-python-datascience
Awesome Python Data Sciencehttps://github.com/thomasjpfan/awesome-python-data-science
Awesome Python Data Sciencehttps://github.com/amitness/toolbox
Awesome Pytorchhttps://github.com/bharathgs/Awesome-pytorch-list
Awesome Recommender Systemshttps://github.com/grahamjenson/list_of_recommender_systems
Awesome Semantic Segmentationhttps://github.com/mrgloom/awesome-semantic-segmentation
Awesome Sentence Embeddinghttps://github.com/Separius/awesome-sentence-embedding
Awesome Time Serieshttps://github.com/MaxBenChrist/awesome_time_series_in_python
Awesome Time Series Anomaly Detectionhttps://github.com/rob-med/awesome-TS-anomaly-detection
https://patch-diff.githubusercontent.com/FerMatPy/datascience#things-i-google-a-lot
Color codeshttps://github.com/d3/d3-3.x-api-reference/blob/master/Ordinal-Scales.md#categorical-colors
Frequency codes for time serieshttps://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases
Date parsing codeshttps://docs.python.org/3/library/datetime.html#strftime-and-strptime-behavior
Feature Calculators tsfreshhttps://github.com/blue-yonder/tsfresh/blob/master/tsfresh/feature_extraction/feature_calculators.py
https://patch-diff.githubusercontent.com/FerMatPy/datascience#contributing
contribution guidelineshttps://patch-diff.githubusercontent.com/FerMatPy/datascience/blob/master/CONTRIBUTING.md
https://patch-diff.githubusercontent.com/FerMatPy/datascience#license
https://creativecommons.org/publicdomain/zero/1.0/
Readme https://patch-diff.githubusercontent.com/FerMatPy/datascience#readme-ov-file
CC0-1.0 license https://patch-diff.githubusercontent.com/FerMatPy/datascience#CC0-1.0-1-ov-file
Contributing https://patch-diff.githubusercontent.com/FerMatPy/datascience#contributing-ov-file
Please reload this pagehttps://patch-diff.githubusercontent.com/FerMatPy/datascience
Activityhttps://patch-diff.githubusercontent.com/FerMatPy/datascience/activity
0 starshttps://patch-diff.githubusercontent.com/FerMatPy/datascience/stargazers
0 watchinghttps://patch-diff.githubusercontent.com/FerMatPy/datascience/watchers
0 forkshttps://patch-diff.githubusercontent.com/FerMatPy/datascience/forks
Report repository https://patch-diff.githubusercontent.com/contact/report-content?content_url=https%3A%2F%2Fgithub.com%2FFerMatPy%2Fdatascience&report=FerMatPy+%28user%29
Releaseshttps://patch-diff.githubusercontent.com/FerMatPy/datascience/releases
Packages 0https://patch-diff.githubusercontent.com/users/FerMatPy/packages?repo_name=datascience
https://github.com
Termshttps://docs.github.com/site-policy/github-terms/github-terms-of-service
Privacyhttps://docs.github.com/site-policy/privacy-policies/github-privacy-statement
Securityhttps://github.com/security
Statushttps://www.githubstatus.com/
Communityhttps://github.community/
Docshttps://docs.github.com/
Contacthttps://support.github.com?tags=dotcom-footer

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