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

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pandashttps://pandas.pydata.org/
numpyhttps://www.numpy.org/
scikit-learnhttps://scikit-learn.org/stable/
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https://patch-diff.githubusercontent.com/beinghorizontal/datascience#pandas-and-jupyter
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modinhttps://github.com/modin-project/modin
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xarrayhttps://github.com/pydata/xarray/
blackcellmagichttps://github.com/csurfer/blackcellmagic
pivottablejshttps://github.com/nicolaskruchten/jupyter_pivottablejs
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https://patch-diff.githubusercontent.com/beinghorizontal/datascience#extraction
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https://patch-diff.githubusercontent.com/beinghorizontal/datascience#big-data
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talk2https://www.youtube.com/watch?v=RA_2qdipVng
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cuDFhttps://github.com/rapidsai/cudf
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https://patch-diff.githubusercontent.com/beinghorizontal/datascience#command-line-tools
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csvkithttps://csvkit.readthedocs.io/en/1.0.3/
csvsorthttps://pypi.org/project/csvsort/
https://patch-diff.githubusercontent.com/beinghorizontal/datascience#statistics
Null Hypothesis Significance Testing (NHST)https://rpsychologist.com/d3/NHST/
Correlationhttps://rpsychologist.com/d3/correlation/
Cohen's dhttps://rpsychologist.com/d3/cohend/
Confidence Intervalhttps://rpsychologist.com/d3/CI/
Equivalence, non-inferiority and superiority testinghttps://rpsychologist.com/d3/equivalence/
Bayesian two-sample t testhttps://rpsychologist.com/d3/bayes/
Distribution of p-values when comparing two groupshttps://rpsychologist.com/d3/pdist/
Understanding the t-distribution and its normal approximationhttps://rpsychologist.com/d3/tdist/
Common statistical tests explainedhttps://lindeloev.github.io/tests-as-linear/
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https://patch-diff.githubusercontent.com/beinghorizontal/datascience#exploration-and-cleaning
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https://patch-diff.githubusercontent.com/beinghorizontal/datascience#feature-engineering
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mediumhttps://towardsdatascience.com/the-next-level-of-data-visualization-in-python-dd6e99039d5e
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https://patch-diff.githubusercontent.com/beinghorizontal/datascience#dashboards
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1https://www.youtube.com/watch?v=J_Cy_QjG6NE
2https://www.youtube.com/watch?v=hRH01ZzT2NI
3https://www.youtube.com/watch?v=wv2MXJIdKRY
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5https://www.youtube.com/watch?v=luixWRpp6Jo
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https://patch-diff.githubusercontent.com/beinghorizontal/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
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https://patch-diff.githubusercontent.com/beinghorizontal/datascience#decision-tree-models
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dochttps://sites.google.com/view/lauraepp/parameters
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dochttps://sites.google.com/view/lauraepp/parameters
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link2https://towardsdatascience.com/regression-prediction-intervals-with-xgboost-428e0a018b
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thundergbmhttps://github.com/Xtra-Computing/thundergbm
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scikit-gardenhttps://github.com/scikit-garden/scikit-garden
grfhttps://github.com/grf-labs/grf
dtreevizhttps://github.com/parrt/dtreeviz
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
https://patch-diff.githubusercontent.com/beinghorizontal/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/
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
https://patch-diff.githubusercontent.com/beinghorizontal/datascience#papers
Search Engine Correlationhttps://arxiv.org/pdf/1107.2691.pdf
https://patch-diff.githubusercontent.com/beinghorizontal/datascience#image-processing
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
mahotashttp://luispedro.org/software/mahotas/
examplehttps://github.com/luispedro/python-image-tutorial/blob/master/Segmenting%20cell%20images%20(fluorescent%20microscopy).ipynb
https://patch-diff.githubusercontent.com/beinghorizontal/datascience#neural-networks
https://patch-diff.githubusercontent.com/beinghorizontal/datascience#reading
Convolutional Neural Networks for Visual Recognitionhttps://cs231n.github.io/
Cell Segmentation Talkhttps://www.youtube.com/watch?v=dVFZpodqJiI
Cell Segmentation Blog Posthttps://www.thomasjpfan.com/2018/07/nuclei-image-segmentation-tutorial/
2https://www.thomasjpfan.com/2017/08/hassle-free-unets/
Deep Learning Bookhttp://www.deeplearningbook.org/
Tutorialhttps://github.com/lexfridman/mit-deep-learning
Bloghttps://distill.pub/2017/feature-visualization/
PPThttp://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture12.pdf
Talkhttps://www.youtube.com/watch?v=EK61htlw8hY
Visualization of optimization algorithmshttps://vis.ensmallen.org/
https://patch-diff.githubusercontent.com/beinghorizontal/datascience#image-related
keras preprocessinghttps://keras.io/preprocessing/image/
imgaughttps://github.com/aleju/imgaug
imgaug_extensionhttps://github.com/cadenai/imgaug_extension
albumentationshttps://github.com/albu/albumentations
Augmentorhttps://github.com/mdbloice/Augmentor
tcavhttps://github.com/tensorflow/tcav
cutouts-explorerhttps://github.com/mgckind/cutouts-explorer
https://patch-diff.githubusercontent.com/beinghorizontal/datascience#text-related
ktexthttps://github.com/hamelsmu/ktext
textgenrnnhttps://github.com/minimaxir/textgenrnn
https://patch-diff.githubusercontent.com/beinghorizontal/datascience#libs
kerashttps://keras.io/
tensorflowhttps://www.tensorflow.org/
keras-contribhttps://github.com/keras-team/keras-contrib
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
ignitehttps://github.com/pytorch/ignite
skorchhttps://github.com/dnouri/skorch
Detectronhttps://github.com/facebookresearch/Detectron
autokerashttps://github.com/jhfjhfj1/autokeras
simpledethttps://github.com/TuSimple/simpledet
PlotNeuralNethttps://github.com/HarisIqbal88/PlotNeuralNet
lucidhttps://github.com/tensorflow/lucid
AdaBoundhttps://github.com/Luolc/AdaBound
caffehttps://github.com/BVLC/caffe
pretrained modelshttps://github.com/BVLC/caffe/wiki/Model-Zoo
foolboxhttps://github.com/bethgelab/foolbox
hiddenlayerhttps://github.com/waleedka/hiddenlayer
imgclsmobhttps://github.com/osmr/imgclsmob
https://patch-diff.githubusercontent.com/beinghorizontal/datascience#snippets
Simple Keras modelshttps://gist.github.com/candlewill/552fa102352ccce42fd829ae26277d24
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
https://patch-diff.githubusercontent.com/beinghorizontal/datascience#gpu
cuMLhttps://github.com/rapidsai/cuml
thundergbmhttps://github.com/Xtra-Computing/thundergbm
thundersvmhttps://github.com/Xtra-Computing/thundersvm
https://patch-diff.githubusercontent.com/beinghorizontal/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
https://patch-diff.githubusercontent.com/beinghorizontal/datascience#classification
All classification metricshttp://rali.iro.umontreal.ca/rali/sites/default/files/publis/SokolovaLapalme-JIPM09.pdf
DESlibhttps://github.com/scikit-learn-contrib/DESlib
https://patch-diff.githubusercontent.com/beinghorizontal/datascience#clustering
pyclusteringhttps://github.com/annoviko/pyclustering
somocluhttps://github.com/peterwittek/somoclu
hdbscanhttps://github.com/scikit-learn-contrib/hdbscan
nmslibhttps://github.com/nmslib/nmslib
buckshotpphttps://github.com/zjohn77/buckshotpp
merfhttps://github.com/manifoldai/merf
videohttps://www.youtube.com/watch?v=gWj4ZwB7f3o
https://patch-diff.githubusercontent.com/beinghorizontal/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/beinghorizontal/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/beinghorizontal/datascience#time-series
Signal Processing Bookhttps://www.analog.com/en/education/education-library/scientist_engineers_guide.html
Articlehttps://tomroelandts.com/articles/how-to-create-a-simple-high-pass-filter
Interactive Toolhttps://fiiir.com/
Filter exampleshttps://plot.ly/python/fft-filters/
Talkhttps://www.youtube.com/watch?v=0zpg9ODE6Ww
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
pyramidhttps://github.com/tgsmith61591/pyramid
pmdarimahttps://github.com/tgsmith61591/pmdarima
pyfluxhttps://github.com/RJT1990/pyflux
prophethttps://github.com/facebook/prophet
htsprophethttps://github.com/CollinRooney12/htsprophet
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
obspyhttps://github.com/obspy/obspy
RobustSTLhttps://github.com/LeeDoYup/RobustSTL
seglearnhttps://github.com/dmbee/seglearn
https://patch-diff.githubusercontent.com/beinghorizontal/datascience#financial-data
pyfoliohttps://github.com/quantopian/pyfolio
ziplinehttps://github.com/quantopian/zipline
alphalenshttps://github.com/quantopian/alphalens
https://patch-diff.githubusercontent.com/beinghorizontal/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
https://patch-diff.githubusercontent.com/beinghorizontal/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
https://patch-diff.githubusercontent.com/beinghorizontal/datascience#ranking
lightninghttps://github.com/scikit-learn-contrib/lightning
https://patch-diff.githubusercontent.com/beinghorizontal/datascience#scoring
SLIMhttps://github.com/ustunb/slim-python
https://patch-diff.githubusercontent.com/beinghorizontal/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
https://patch-diff.githubusercontent.com/beinghorizontal/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
https://patch-diff.githubusercontent.com/beinghorizontal/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/beinghorizontal/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
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
https://patch-diff.githubusercontent.com/beinghorizontal/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
https://patch-diff.githubusercontent.com/beinghorizontal/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
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
https://patch-diff.githubusercontent.com/beinghorizontal/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
hyperopthttps://github.com/hyperopt/hyperopt
hyperopt-sklearnhttps://github.com/hyperopt/hyperopt-sklearn
skopthttps://scikit-optimize.github.io/
tunehttps://ray.readthedocs.io/en/latest/tune.html
optunahttps://github.com/pfnet/optuna
hypergraphhttps://github.com/aljabr0/hypergraph
bbopthttps://github.com/evhub/bbopt
dragonflyhttps://github.com/dragonfly/dragonfly
https://patch-diff.githubusercontent.com/beinghorizontal/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
Kagglerhttps://github.com/jeongyoonlee/Kaggler
https://patch-diff.githubusercontent.com/beinghorizontal/datascience#active-learning
Talkhttps://www.youtube.com/watch?v=0efyjq5rWS4
modALhttps://github.com/modAL-python/modAL
https://patch-diff.githubusercontent.com/beinghorizontal/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/beinghorizontal/datascience#frameworks
h2ohttps://github.com/h2oai/h2o-3
turicreatehttps://github.com/apple/turicreate
astromlhttps://github.com/astroML/astroML
https://patch-diff.githubusercontent.com/beinghorizontal/datascience#deployment-and-lifecycle-management
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
https://patch-diff.githubusercontent.com/beinghorizontal/datascience#other
dvchttps://github.com/iterative/dvc
dafthttps://github.com/dfm/daft
unythttps://github.com/yt-project/unyt
scrapyhttps://github.com/scrapy/scrapy
VowpalWabbithttps://github.com/VowpalWabbit/vowpal_wabbit
metric-learnhttps://github.com/metric-learn/metric-learn
https://patch-diff.githubusercontent.com/beinghorizontal/datascience#general-python-programming
funcyhttps://github.com/Suor/funcy
more_itertoolshttps://more-itertools.readthedocs.io/en/latest/
dillhttps://pypi.org/project/dill/
attrshttps://github.com/python-attrs/attrs
dateparserhttps://dateparser.readthedocs.io/en/latest/
jellyfishhttps://github.com/jamesturk/jellyfish
https://patch-diff.githubusercontent.com/beinghorizontal/datascience#blogs
PocketClusterhttps://blog.pocketcluster.io/
Distill.pubhttps://distill.pub/
https://patch-diff.githubusercontent.com/beinghorizontal/datascience#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 Business Machine Learninghttps://github.com/firmai/business-machine-learning
Awesome Data Science with Rubyhttps://github.com/arbox/data-science-with-ruby
Awesome Deep Learninghttps://github.com/ChristosChristofidis/awesome-deep-learning
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 Network Embeddinghttps://github.com/chihming/awesome-network-embedding
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 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
Recommender Systems (Microsoft)https://github.com/Microsoft/Recommenders
https://patch-diff.githubusercontent.com/beinghorizontal/datascience#things-i-google-a-lot
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/beinghorizontal/datascience#contributing
contribution guidelineshttps://patch-diff.githubusercontent.com/beinghorizontal/datascience/blob/master/CONTRIBUTING.md
https://patch-diff.githubusercontent.com/beinghorizontal/datascience#license
https://creativecommons.org/publicdomain/zero/1.0/
Readme https://patch-diff.githubusercontent.com/beinghorizontal/datascience#readme-ov-file
CC0-1.0 license https://patch-diff.githubusercontent.com/beinghorizontal/datascience#CC0-1.0-1-ov-file
Contributing https://patch-diff.githubusercontent.com/beinghorizontal/datascience#contributing-ov-file
Please reload this pagehttps://patch-diff.githubusercontent.com/beinghorizontal/datascience
Activityhttps://patch-diff.githubusercontent.com/beinghorizontal/datascience/activity
13 starshttps://patch-diff.githubusercontent.com/beinghorizontal/datascience/stargazers
1 watchinghttps://patch-diff.githubusercontent.com/beinghorizontal/datascience/watchers
8 forkshttps://patch-diff.githubusercontent.com/beinghorizontal/datascience/forks
Report repository https://patch-diff.githubusercontent.com/contact/report-content?content_url=https%3A%2F%2Fgithub.com%2Fbeinghorizontal%2Fdatascience&report=beinghorizontal+%28user%29
Releaseshttps://patch-diff.githubusercontent.com/beinghorizontal/datascience/releases
Packages 0https://patch-diff.githubusercontent.com/users/beinghorizontal/packages?repo_name=datascience
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