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| README | https://github.com/r0f1/datascience |
| Contributing | https://github.com/r0f1/datascience |
| CC0-1.0 license | https://github.com/r0f1/datascience |
| https://github.com/r0f1/datascience#awesome-data-science-with-python |
| https://github.com/r0f1/datascience#core |
| pandas | https://pandas.pydata.org/ |
| numpy | https://www.numpy.org/ |
| scikit-learn | https://scikit-learn.org/stable/ |
| intelex | https://github.com/intel/scikit-learn-intelex |
| matplotlib | https://matplotlib.org/ |
| seaborn | https://seaborn.pydata.org/ |
| ydata-profiling | https://github.com/ydataai/ydata-profiling |
| sklearn_pandas | https://github.com/scikit-learn-contrib/sklearn-pandas |
| missingno | https://github.com/ResidentMario/missingno |
| rainbow-csv | https://marketplace.visualstudio.com/items?itemName=mechatroner.rainbow-csv |
| https://github.com/r0f1/datascience#general-python-programming |
| Advanced Python Features | https://blog.edward-li.com/tech/advanced-python-features/ |
| uv | https://github.com/astral-sh/uv |
| pdm | https://pdm-project.org/en/latest/ |
| just | https://github.com/casey/just |
| python-dotenv | https://github.com/theskumar/python-dotenv |
| structlog | https://github.com/hynek/structlog |
| more_itertools | https://more-itertools.readthedocs.io/en/latest/ |
| tqdm | https://github.com/tqdm/tqdm |
| pandas apply() | https://stackoverflow.com/a/34365537/1820480 |
| hydra | https://github.com/facebookresearch/hydra |
| https://github.com/r0f1/datascience#pandas-tricks-alternatives-and-additions |
| duckdb | https://github.com/duckdb/duckdb |
| duckplyr | https://github.com/tidyverse/duckplyr/ |
| Great Intro | https://codecut.ai/deep-dive-into-duckdb-data-scientists/ |
| ducklake | https://github.com/duckdb/ducklake |
| fireducks | https://github.com/fireducks-dev/fireducks |
| pandasvault | https://github.com/firmai/pandasvault |
| polars | https://github.com/pola-rs/polars |
| xarray | https://github.com/pydata/xarray/ |
| mlx | https://github.com/ml-explore/mlx |
| pandas_flavor | https://github.com/Zsailer/pandas_flavor |
| daft | https://github.com/Eventual-Inc/Daft |
| vaex | https://github.com/vaexio/vaex |
| modin | https://github.com/modin-project/modin |
| swifter | https://github.com/jmcarpenter2/swifter |
| https://github.com/r0f1/datascience#tables |
| great-tables | https://github.com/posit-dev/great-tables |
| https://github.com/r0f1/datascience#interactive-dataframe-visualization |
| pygwalker | https://github.com/Kanaries/pygwalker |
| marimo | https://github.com/marimo-team/marimo |
| lux | https://github.com/lux-org/lux |
| dtale | https://github.com/man-group/dtale |
| pandasgui | https://github.com/adamerose/pandasgui |
| quak | https://github.com/manzt/quak |
| twitter | https://x.com/trevmanz/status/1816760923949809982 |
| https://github.com/r0f1/datascience#environment-and-jupyter |
| Jupyter Tricks | https://www.dataquest.io/blog/jupyter-notebook-tips-tricks-shortcuts/ |
| nteract | https://nteract.io/ |
| papermill | https://github.com/nteract/papermill |
| tutorial | https://pbpython.com/papermil-rclone-report-1.html |
| nbdime | https://github.com/jupyter/nbdime |
| ReviewNB | https://www.reviewnb.com/ |
| RISE | https://github.com/damianavila/RISE |
| handcalcs | https://github.com/connorferster/handcalcs |
| notebooker | https://github.com/man-group/notebooker |
| voila | https://github.com/QuantStack/voila |
| Voila grid layout | https://github.com/voila-dashboards/voila-gridstack |
| https://github.com/r0f1/datascience#jupyter-alternatives |
| positron | https://github.com/posit-dev/positron |
| Deepnote | https://deepnote.com |
| https://github.com/r0f1/datascience#extraction |
| textract | https://github.com/deanmalmgren/textract |
| https://github.com/r0f1/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 |
| dask | https://github.com/dask/dask |
| dask-ml | http://ml.dask.org/ |
| 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 |
| h2o | https://github.com/h2oai/h2o-3 |
| cuDF | https://github.com/rapidsai/cudf |
| Intro | https://www.youtube.com/watch?v=6XzS5XcpicM&t=2m50s |
| cupy | https://github.com/cupy/cupy |
| ray | https://github.com/ray-project/ray/ |
| bottleneck | https://github.com/kwgoodman/bottleneck |
| petastorm | https://github.com/uber/petastorm |
| zarr | https://github.com/zarr-developers/zarr-python |
| NVTabular | https://github.com/NVIDIA/NVTabular |
| tensorstore | https://github.com/google/tensorstore |
| https://github.com/r0f1/datascience#command-line-tools-csv |
| csvkit | https://github.com/wireservice/csvkit |
| csvsort | https://pypi.org/project/csvsort/ |
| https://github.com/r0f1/datascience#classical-statistics |
| https://github.com/r0f1/datascience#books |
| Lakens - Improving Your Statistical Inferences | https://lakens.github.io/statistical_inferences/ |
| Github | https://github.com/Lakens/statistical_inferences |
| Models Demystified | https://m-clark.github.io/book-of-models/ |
| Github | https://github.com/m-clark/book-of-models |
| https://github.com/r0f1/datascience#datasets |
| Rdatasets | https://vincentarelbundock.github.io/Rdatasets/articles/data.html |
| crimedatasets | https://lightbluetitan.github.io/crimedatasets/ |
| educationr | https://lightbluetitan.github.io/educationr/ |
| MedDataSets | https://lightbluetitan.github.io/meddatasets/index.html |
| oncodatasets | https://lightbluetitan.github.io/oncodatasets/ |
| timeseriesdatasets_R | https://lightbluetitan.github.io/timeseriesdatasets_R/ |
| usdatasets | https://lightbluetitan.github.io/usdatasets/ |
| economic datasets | https://captgouda24.github.io/nicholas-decker.github.io/datasets.html |
| https://github.com/r0f1/datascience#p-values |
| The ASA Statement on p-Values: Context, Process, and Purpose | https://amstat.tandfonline.com/doi/full/10.1080/00031305.2016.1154108#.Vt2XIOaE2MN |
| Greenland - Statistical tests, P-values, confidence intervals, and power: a guide to misinterpretations | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4877414/ |
| Rubin - Inconsistent multiple testing corrections: The fallacy of using family-based error rates to make inferences about individual hypotheses | https://www.sciencedirect.com/science/article/pii/S2590260124000067?via%3Dihub |
| Gigerenzer - Mindless Statistics | https://library.mpib-berlin.mpg.de/ft/gg/GG_Mindless_2004.pdf |
| Rubin - That's not a two-sided test! It's two one-sided tests! (TOST) | https://rss.onlinelibrary.wiley.com/doi/full/10.1111/1740-9713.01405 |
| Lakens - How were we supposed to move beyond p < .05, and why didn’t we? | https://errorstatistics.com/2024/07/01/guest-post-daniel-lakens-how-were-we-supposed-to-move-beyond-p-05-and-why-didnt-we-thoughts-on-abandon-statistical-significance-5-years-on/ |
| McShane et al. - Abandon Statistical Significance | https://www.tandfonline.com/doi/full/10.1080/00031305.2018.1527253 |
| Ho et al. - Moving beyond P values data analysis with estimation graphics | https://www.researchgate.net/publication/333884529_Moving_beyond_P_values_data_analysis_with_estimation_graphics |
| Lakens - The probability of p-values as a function of the statistical power of a test | https://daniellakens.blogspot.com/2014/05/the-probability-of-p-values-as-function.html |
| https://github.com/r0f1/datascience#correlation |
| Guess the Correlation | https://www.guessthecorrelation.com/ |
| phik | https://github.com/kaveio/phik |
| hoeffd | https://search.r-project.org/CRAN/refmans/Hmisc/html/hoeffd.html |
| https://github.com/r0f1/datascience#confidence-intervals |
| Morey - The fallacy of placing confidence in confidence intervals | https://link.springer.com/article/10.3758/s13423-015-0947-8 |
| https://github.com/r0f1/datascience#packages |
| statsmodels | https://www.statsmodels.org/stable/index.html |
| linearmodels | https://github.com/bashtage/linearmodels |
| nomograms | https://hbiostat.org/bbr/rmsintro.html#nomograms-overall-depiction-of-fitted-models |
| explanation | https://stats.stackexchange.com/a/155433/285504 |
| pingouin | https://github.com/raphaelvallat/pingouin |
| Pairwise correlation between columns of pandas DataFrame | https://pingouin-stats.org/generated/pingouin.pairwise_corr.html |
| scipy.stats | https://docs.scipy.org/doc/scipy/reference/stats.html#statistical-tests |
| scikit-posthocs | https://github.com/maximtrp/scikit-posthocs |
| 1 | https://pingouin-stats.org/generated/pingouin.plot_blandaltman.html |
| 2 | http://www.statsmodels.org/dev/generated/statsmodels.graphics.agreement.mean_diff_plot.html |
| ANOVA | https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.f_oneway.html |
| StatCheck | https://statcheck.steveharoz.com/ |
| tost | https://pingouin-stats.org/build/html/generated/pingouin.tost.html |
| DABEST-python | https://github.com/ACCLAB/DABEST-python |
| Durga | https://github.com/KhanKawsar/EstimationPlot |
| https://github.com/r0f1/datascience#effect-size |
| MOTE Effect Size Calculator | https://www.aggieerin.com/shiny-server/ |
| Shiny App | https://doomlab.shinyapps.io/mote/ |
| R package | https://github.com/doomlab/MOTE |
| Estimating Effect Sizes From Pretest-Posttest-Control Group Designs | https://journals.sagepub.com/doi/epdf/10.1177/1094428106291059 |
| Twitter | https://twitter.com/MatthewBJane/status/1742588609025200557 |
| https://github.com/r0f1/datascience#statistical-tests |
| test_proportions_2indep | https://www.statsmodels.org/dev/generated/statsmodels.stats.proportion.test_proportions_2indep.html |
| G-Test | https://en.wikipedia.org/wiki/G-test |
| power_divergence | https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.power_divergence.html |
| https://github.com/r0f1/datascience#comparing-two-populations |
| torch-two-sample | https://github.com/josipd/torch-two-sample |
| Explanation | https://www.real-statistics.com/multivariate-statistics/multivariate-normal-distribution/friedman-rafsky-test/ |
| Application | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5014134/ |
| https://github.com/r0f1/datascience#power-and-sample-size-calculations |
| pwrss | https://cran.r-project.org/web/packages/pwrss/index.html |
| Tutorial with t-test | https://rpubs.com/metinbulus/welch |
| https://github.com/r0f1/datascience#interim-analyses--sequential-analysis--stopping |
| Stop Early Stopping | https://stop-early-stopping.osc.garden/ |
| Sequential Analysis | https://en.wikipedia.org/wiki/Sequential_analysis |
| sequential | https://cran.r-project.org/web/packages/Sequential/Sequential.pdf |
| confseq | https://github.com/gostevehoward/confseq |
| https://github.com/r0f1/datascience#visualizations |
| Friends don't let friends make certain types of data visualization | https://github.com/cxli233/FriendsDontLetFriends |
| Great Overview over Visualizations | https://textvis.lnu.se/ |
| 1 dataset, 100 visualizations | https://100.datavizproject.com/ |
| Dependent Propabilities | https://static.laszlokorte.de/stochastic/ |
| Null Hypothesis Significance Testing (NHST) and Sample Size Calculation | https://rpsychologist.com/d3/NHST/ |
| estimationstats | https://www.estimationstats.com/ |
| Sample Size / Duration Calculator | https://calculator.osc.garden/ |
| 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/ |
| Statistical Power and Sample Size Calculation Tools | https://pwrss.shinyapps.io/index/ |
| https://github.com/r0f1/datascience#tidy-tuesday |
| The Art of Data Visualization with ggplot2, The TidyTuesday Cookbook | https://nrennie.rbind.io/art-of-viz/ |
| Best Practices for Data Visualization | https://royal-statistical-society.github.io/datavisguide/ |
| tidytuesday | https://github.com/rfordatascience/tidytuesday |
| z3tt/TidyTuesday | https://github.com/z3tt/TidyTuesday |
| nrennie/tidytuesday | https://github.com/nrennie/tidytuesday |
| poncest/tidytuesday | https://github.com/poncest/tidytuesday |
| https://github.com/r0f1/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/r0f1/datascience#texts |
| Modes, Medians and Means: A Unifying Perspective | https://www.johnmyleswhite.com/notebook/2013/03/22/modes-medians-and-means-an-unifying-perspective/ |
| Using Norms to Understand Linear Regression | https://www.johnmyleswhite.com/notebook/2013/03/22/using-norms-to-understand-linear-regression/ |
| Verifying the Assumptions of Linear Models | https://github.com/erykml/medium_articles/blob/master/Statistics/linear_regression_assumptions.ipynb |
| Mediation and Moderation Intro | https://ademos.people.uic.edu/Chapter14.html |
| Montgomery et al. - How conditioning on post-treatment variables can ruin your experiment and what to do about it | https://cpb-us-e1.wpmucdn.com/sites.dartmouth.edu/dist/5/2293/files/2021/03/post-treatment-bias.pdf |
| Lindeløv - Common statistical tests are linear models | https://lindeloev.github.io/tests-as-linear/ |
| Chatruc - The Central Limit Theorem and its misuse | https://web.archive.org/web/20191229234155/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://nsmn1.uh.edu/dgraur/niv/themostdangerousequation.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/ |
| Same Stats, Different Graphs: Generating Datasets with Varied Appearance and Identical Statistics through Simulated Annealing | https://www.researchgate.net/publication/316652618_Same_Stats_Different_Graphs_Generating_Datasets_with_Varied_Appearance_and_Identical_Statistics_through_Simulated_Annealing |
| Youtube | https://www.youtube.com/watch?v=DbJyPELmhJc |
| How large is that number in the Law of Large Numbers? | https://thepalindrome.org/p/how-large-that-number-in-the-law |
| The Prosecutor's Fallacy | https://www.cebm.ox.ac.uk/news/views/the-prosecutors-fallacy |
| The Dunning-Kruger Effect is Autocorrelation | https://economicsfromthetopdown.com/2022/04/08/the-dunning-kruger-effect-is-autocorrelation/ |
| Rafi, Greenland - Semantic and cognitive tools to aid statistical science: replace confidence and significance by compatibility and surprise | https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/s12874-020-01105-9 |
| Carlin et al. - On the uses and abuses of regression models: a call for reform of statistical practice and teaching | https://arxiv.org/abs/2309.06668 |
| Chen, Roth - Logs with zeros? Some problems and solutions | https://arxiv.org/abs/2212.06080 |
| Wigboldus et al. - Encourage Playing with Data and Discourage Questionable Reporting Practices | https://link.springer.com/article/10.1007/s11336-015-9445-1 |
| Simmons et al. - False-Positive Psychology: Undisclosed Flexibility in Data Collection and Analysis Allows Presenting Anything as Significant | https://journals.sagepub.com/doi/10.1177/0956797611417632?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%20%200pubmed |
| Zhang - An illusion of predictability in scientific results: Even experts confuse inferential uncertainty and outcome variability | https://www.pnas.org/doi/10.1073/pnas.2302491120 |
| https://github.com/r0f1/datascience#evaluation |
| Collins et al. - Evaluation of clinical prediction models (part 1): from development to external validation | https://www.bmj.com/content/384/bmj-2023-074819.full |
| Twitter | https://twitter.com/GSCollins/status/1744309712995098624 |
| https://github.com/r0f1/datascience#epidemiology |
| Lesko et al. - A Framework for Descriptive Epidemiology | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10144679/ |
| 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 |
| Tutorial | https://github.com/pzivich/Python-for-Epidemiologists |
| tipr | https://github.com/LucyMcGowan/tipr |
| quartets | https://github.com/r-causal/quartets |
| Datasaurus Dozen | https://github.com/jumpingrivers/datasauRus |
| episensr | https://cran.r-project.org/web/packages/episensr/vignettes/episensr.html |
| https://github.com/r0f1/datascience#machine-learning-tutorials |
| Statistical Inference and Regression | https://mattblackwell.github.io/gov2002-book/ |
| Applied Machine Learning in Python | https://geostatsguy.github.io/MachineLearningDemos_Book/intro.html |
| Convolutional Neural Networks for Visual Recognition | https://cs231n.github.io/ |
| Intuition for the Algorithms in Machine Learning | https://www.youtube.com/watch?v=7o9TMQAHgkQ&list=PLNeXFnYrCJneoY_rKtWJy833YiMrCRi5f&index=1 |
| https://github.com/r0f1/datascience#exploration-and-cleaning |
| Checklist | https://github.com/r0f1/ml_checklist |
| pyjanitor | https://github.com/pyjanitor-devs/pyjanitor |
| skimpy | https://github.com/aeturrell/skimpy |
| pandera | https://github.com/unionai-oss/pandera |
| dataframely | https://github.com/Quantco/dataframely |
| pointblank | https://github.com/posit-dev/pointblank |
| 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 |
| skrub | https://github.com/skrub-data/skrub |
| https://github.com/r0f1/datascience#noisy-labels |
| cleanlab | https://github.com/cleanlab/cleanlab |
| doubtlab | https://github.com/koaning/doubtlab |
| https://github.com/r0f1/datascience#train--test-split |
| iterative-stratification | https://github.com/trent-b/iterative-stratification |
| https://github.com/r0f1/datascience#feature-engineering |
| Vincent Warmerdam: Untitled12.ipynb | https://www.youtube.com/watch?v=yXGCKqo5cEY |
| Vincent Warmerdam: Winning with Simple, even Linear, Models | 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 |
| categorical-encoding | https://github.com/scikit-learn-contrib/categorical-encoding |
| vtreat (R package) | https://cran.r-project.org/web/packages/vtreat/vignettes/vtreat.html |
| 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 |
| temporian | https://github.com/google/temporian |
| pypeln | https://github.com/cgarciae/pypeln |
| feature-engine | https://github.com/feature-engine/feature_engine |
| https://github.com/r0f1/datascience#feature-selection |
| Overview Paper | https://www.sciencedirect.com/science/article/pii/S016794731930194X |
| 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 |
| Boruta-Shap | https://github.com/Ekeany/Boruta-Shap |
| 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 |
| SubTab | https://github.com/AstraZeneca/SubTab |
| mrmr | https://github.com/smazzanti/mrmr |
| Website | http://home.penglab.com/proj/mRMR/ |
| arfs | https://github.com/ThomasBury/arfs |
| VSURF | https://github.com/robingenuer/VSURF |
| doc | https://www.rdocumentation.org/packages/VSURF/versions/1.1.0/topics/VSURF |
| FeatureSelectionGA | https://github.com/kaushalshetty/FeatureSelectionGA |
| https://github.com/r0f1/datascience#subset-selection |
| apricot | https://github.com/jmschrei/apricot |
| ducks | https://github.com/manimino/ducks |
| https://github.com/r0f1/datascience#dimensionality-reduction--representation-learning |
| https://github.com/r0f1/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 |
| link | https://github.com/cvxgrp/pymde |
| https://github.com/r0f1/datascience#neural-network-based |
| 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/r0f1/datascience#packages-1 |
| Dangers of PCA (paper) | https://www.nature.com/articles/s41598-022-14395-4 |
| Phantom oscillations in PCA | https://www.biorxiv.org/content/10.1101/2023.06.20.545619v1.full |
| What to use instead of PCA | https://www.pnas.org/doi/10.1073/pnas.2319169120 |
| 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 |
| Correlation Circle Plot | http://rasbt.github.io/mlxtend/user_guide/plotting/plot_pca_correlation_graph/ |
| Tweet | https://twitter.com/rasbt/status/1555999903398219777/photo/1 |
| sklearn.random_projection | https://scikit-learn.org/stable/modules/random_projection.html |
| sklearn.cross_decomposition | https://scikit-learn.org/stable/modules/cross_decomposition.html#cross-decomposition |
| 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 |
| humap | https://github.com/wilsonjr/humap |
| 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 |
| contrastive | https://github.com/abidlabs/contrastive |
| scPCA | https://github.com/PhilBoileau/scPCA |
| generalized_contrastive_PCA | https://github.com/SjulsonLab/generalized_contrastive_PCA |
| tmap | https://github.com/reymond-group/tmap |
| lollipop | https://github.com/neurodata/lollipop |
| linearsdr | https://github.com/HarrisQ/linearsdr |
| PHATE | https://github.com/KrishnaswamyLab/PHATE |
| datamapplot | https://github.com/TutteInstitute/datamapplot |
| https://github.com/r0f1/datascience#visualization |
| All charts | https://datavizproject.com/ |
| 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 |
| penrose | https://github.com/penrose/penrose |
| ridgeplot | https://github.com/tpvasconcelos/ridgeplot |
| 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/ |
| yellowbrick | https://github.com/DistrictDataLabs/yellowbrick |
| bokeh | https://github.com/bokeh/bokeh |
| 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 |
| plotnine | https://github.com/has2k1/plotnine |
| altair | https://github.com/vega/altair |
| hvplot | https://github.com/pyviz/hvplot |
| holoviews | http://holoviews.org/ |
| dtreeviz | https://github.com/parrt/dtreeviz |
| mpl-scatter-density | https://github.com/astrofrog/mpl-scatter-density |
| ComplexHeatmap | https://github.com/jokergoo/ComplexHeatmap |
| morpheus | https://software.broadinstitute.org/morpheus/ |
| Source | https://github.com/cmap/morpheus.js |
| 1 | https://www.youtube.com/watch?v=0nkYDeekhtQ |
| 2 | https://www.youtube.com/watch?v=r9mN6MsxUb0 |
| Code | https://github.com/broadinstitute/BBBC021_Morpheus_Exercise |
| jupyter-scatter | https://github.com/flekschas/jupyter-scatter |
| fastplotlib | https://github.com/fastplotlib/fastplotlib |
| datamapplot | https://github.com/TutteInstitute/datamapplot |
| SandDance | https://github.com/microsoft/SandDance |
| https://github.com/r0f1/datascience#colors |
| palettable | https://github.com/jiffyclub/palettable |
| colorbrewer2 | https://colorbrewer2.org/#type=sequential&scheme=BuGn&n=3 |
| colorcet | https://github.com/holoviz/colorcet |
| Named Colors Wheel | https://arantius.github.io/web-color-wheel/ |
| https://github.com/r0f1/datascience#dashboards |
| py-shiny | https://github.com/rstudio/py-shiny |
| talk | https://www.youtube.com/watch?v=ijRBbtT2tgc |
| superset | https://github.com/apache/superset |
| streamlit | https://github.com/streamlit/streamlit |
| Resources | https://github.com/marcskovmadsen/awesome-streamlit |
| Gallery | http://awesome-streamlit.org/ |
| Components | https://www.streamlit.io/components |
| bokeh-events | https://github.com/ash2shukla/streamlit-bokeh-events |
| mercury | https://github.com/mljar/mercury |
| Example | https://github.com/pplonski/dashboard-python-jupyter-notebook |
| 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 |
| voila-gridstack | https://github.com/voila-dashboards/voila-gridstack |
| https://github.com/r0f1/datascience#ui |
| gradio | https://github.com/gradio-app/gradio |
| https://github.com/r0f1/datascience#survey-tools |
| samplics | https://github.com/samplics-org/samplics |
| https://github.com/r0f1/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 |
| 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 |
| Predict economic indicators from Open Street Map | https://janakiev.com/blog/osm-predict-economic-indicators/ |
| PySal | https://github.com/pysal/pysal |
| geography | https://github.com/ushahidi/geograpy |
| cartogram | https://go-cart.io/cartogram |
| https://github.com/r0f1/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 |
| 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 |
| https://github.com/r0f1/datascience#decision-tree-models |
| Intro to Decision Trees and Random Forests | https://victorzhou.com/blog/intro-to-random-forests/ |
| 1 | https://explained.ai/gradient-boosting/ |
| 2 | https://www.gormanalysis.com/blog/gradient-boosting-explained/ |
| Decision Tree Visualization | https://explained.ai/decision-tree-viz/index.html |
| 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 |
| pycaret | https://github.com/pycaret/pycaret |
| forestci | https://github.com/scikit-learn-contrib/forest-confidence-interval |
| 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 |
| bartpy | https://github.com/JakeColtman/bartpy |
| merf | https://github.com/manifoldai/merf |
| video | https://www.youtube.com/watch?v=gWj4ZwB7f3o |
| groot | https://github.com/tudelft-cda-lab/GROOT |
| linear-tree | https://github.com/cerlymarco/linear-tree |
| supertree | https://github.com/mljar/supertree |
| https://github.com/r0f1/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 |
| infomap | https://github.com/mapequation/infomap |
| datasketch | https://github.com/ekzhu/datasketch |
| flair | https://github.com/zalandoresearch/flair |
| stanza | https://github.com/stanfordnlp/stanza |
| Chatistics | https://github.com/MasterScrat/Chatistics |
| textdistance | https://github.com/life4/textdistance |
| https://github.com/r0f1/datascience#bio-image-analysis |
| Lee et al. - A beginner's guide to rigor and reproducibility in fluorescence imaging experiments | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6080651/ |
| Awesome Cytodata | https://github.com/cytodata/awesome-cytodata |
| https://github.com/r0f1/datascience#tutorials |
| MIT 7.016 Introductory Biology, Fall 2018 | https://www.youtube.com/playlist?list=PLUl4u3cNGP63LmSVIVzy584-ZbjbJ-Y63 |
| Bio-image Analysis Notebooks | https://haesleinhuepf.github.io/BioImageAnalysisNotebooks/intro.html |
| point-spread-function estimation | https://haesleinhuepf.github.io/BioImageAnalysisNotebooks/18a_deconvolution/extract_psf.html |
| deconvolution | https://haesleinhuepf.github.io/BioImageAnalysisNotebooks/18a_deconvolution/introduction_deconvolution.html |
| 3D cell segmentation | https://haesleinhuepf.github.io/BioImageAnalysisNotebooks/20_image_segmentation/Segmentation_3D.html |
| feature extraction | https://haesleinhuepf.github.io/BioImageAnalysisNotebooks/22_feature_extraction/statistics_with_pyclesperanto.html |
| pyclesperanto | https://github.com/clEsperanto/pyclesperanto_prototype |
| python_for_microscopists | https://github.com/bnsreenu/python_for_microscopists |
| youtube channel | https://www.youtube.com/channel/UC34rW-HtPJulxr5wp2Xa04w/videos |
| https://github.com/r0f1/datascience#datasets-1 |
| jump-cellpainting | https://github.com/jump-cellpainting/datasets |
| MedMNIST | https://github.com/MedMNIST/MedMNIST |
| CytoImageNet | https://github.com/stan-hua/CytoImageNet |
| Haghighi | https://github.com/carpenterlab/2021_Haghighi_NatureMethods |
| broadinstitute/lincs-profiling-complementarity | https://github.com/broadinstitute/lincs-profiling-complementarity |
| https://github.com/r0f1/datascience#biostatistics--robust-statistics |
| MinCovDet | https://scikit-learn.org/stable/modules/generated/sklearn.covariance.MinCovDet.html |
| Paper | https://wires.onlinelibrary.wiley.com/doi/full/10.1002/wics.1421 |
| App1 | https://journals.sagepub.com/doi/10.1177/1087057112469257?url_ver=Z39.88-2003&rfr_id=ori%3Arid%3Acrossref.org&rfr_dat=cr_pub++0pubmed& |
| App2 | https://www.cell.com/cell-reports/pdf/S2211-1247(21)00694-X.pdf |
| moderated z-score | https://clue.io/connectopedia/replicate_collapse |
| winsorize | https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.mstats.winsorize.html#scipy.stats.mstats.winsorize |
| https://github.com/r0f1/datascience#high-content-screening-assay-design |
| Zhang XHD (2008) - Novel analytic criteria and effective plate designs for quality control in genome-wide RNAi screens | https://slas-discovery.org/article/S2472-5552(22)08204-1/pdf |
| Iversen - A Comparison of Assay Performance Measures in Screening Assays, Signal Window, Z′ Factor, and Assay Variability Ratio | https://www.slas-discovery.org/article/S2472-5552(22)08460-X/pdf |
| Z-factor | https://en.wikipedia.org/wiki/Z-factor |
| Z'-factor | https://link.springer.com/referenceworkentry/10.1007/978-3-540-47648-1_6298 |
| CV | https://en.wikipedia.org/wiki/Coefficient_of_variation |
| SSMD | https://en.wikipedia.org/wiki/Strictly_standardized_mean_difference |
| Signal Window | https://www.intechopen.com/chapters/48130 |
| https://github.com/r0f1/datascience#microscopy--assay |
| BD Spectrum Viewer | https://www.bdbiosciences.com/en-us/resources/bd-spectrum-viewer |
| SpectraViewer | https://www.perkinelmer.com/lab-products-and-services/spectraviewer |
| Thermofisher Spectrum Viewer | https://www.thermofisher.com/order/stain-it |
| Microscopy Resolution Calculator | https://www.microscope.healthcare.nikon.com/microtools/resolution-calculator |
| PlateEditor | https://github.com/vindelorme/PlateEditor |
| app | https://plateeditor.sourceforge.io/ |
| zip | https://sourceforge.net/projects/plateeditor/ |
| paper | https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0252488 |
| https://github.com/r0f1/datascience#image-formats-and-converters |
| paper | https://www.biorxiv.org/content/10.1101/2023.02.17.528834v1.full |
| standard | https://ngff.openmicroscopy.org/latest/ |
| bioformats2raw | https://github.com/glencoesoftware/bioformats2raw |
| raw2ometiff | https://github.com/glencoesoftware/raw2ometiff |
| BatchConvert | https://github.com/Euro-BioImaging/BatchConvert |
| video | https://www.youtube.com/watch?v=DeCWV274l0c |
| Study Component Guidance | https://www.ebi.ac.uk/bioimage-archive/rembi-help-examples/ |
| File List Guide | https://www.ebi.ac.uk/bioimage-archive/help-file-list/ |
| paper | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8606015/ |
| video | https://www.youtube.com/watch?v=GVmfOpuP2_c |
| spreadsheet | https://docs.google.com/spreadsheets/d/1Ck1NeLp-ZN4eMGdNYo2nV6KLEdSfN6oQBKnnWU6Npeo/edit#gid=1023506919 |
| https://github.com/r0f1/datascience#matrix-formats |
| anndata | https://github.com/scverse/anndata |
| Docs | https://anndata.readthedocs.io/en/latest/index.html |
| muon | https://github.com/scverse/muon |
| mudata | https://github.com/scverse/mudata |
| bdz | https://github.com/openssbd/bdz |
| https://github.com/r0f1/datascience#image-viewers |
| napari | https://github.com/napari/napari |
| Fiji | https://fiji.sc/ |
| vizarr | https://github.com/hms-dbmi/vizarr |
| avivator | https://github.com/hms-dbmi/viv |
| OMERO | https://www.openmicroscopy.org/omero/ |
| IDR | https://idr.openmicroscopy.org/ |
| Intro | https://www.youtube.com/watch?v=nSCrMO_c-5s |
| fiftyone | https://github.com/voxel51/fiftyone |
| Shiny App | https://shiny-portal.embl.de/shinyapps/app/01_image-data-explorer |
| Video | https://www.youtube.com/watch?v=H8zIZvOt1MA |
| ImSwitch | https://github.com/ImSwitch/ImSwitch |
| Doc | https://imswitch.readthedocs.io/en/stable/gui.html |
| Video | https://www.youtube.com/watch?v=XsbnMkGSPQQ |
| pixmi | https://github.com/piximi/piximi |
| App | https://www.piximi.app/ |
| DeepCell Label | https://label.deepcell.org/ |
| Video | https://www.youtube.com/watch?v=zfsvUBkEeow |
| https://github.com/r0f1/datascience#napari-plugins |
| napari-sam | https://github.com/MIC-DKFZ/napari-sam |
| napari-chatgpt | https://github.com/royerlab/napari-chatgpt |
| https://github.com/r0f1/datascience#image-restoration-and-denoising |
| aydin | https://github.com/royerlab/aydin |
| DivNoising | https://github.com/juglab/DivNoising |
| CSBDeep | https://github.com/CSBDeep/CSBDeep |
| Project page | https://csbdeep.bioimagecomputing.com/tools/ |
| gibbs-diffusion | https://github.com/rubenohana/gibbs-diffusion |
| https://github.com/r0f1/datascience#illumination-correction |
| skimage | https://scikit-image.org/docs/dev/api/skimage.exposure.html#skimage.exposure.equalize_adapthist |
| cidre | https://github.com/smithk/cidre |
| BaSiCPy | https://github.com/peng-lab/BaSiCPy |
| BaSiC | https://github.com/marrlab/BaSiC |
| https://github.com/r0f1/datascience#bleedthrough-correction--spectral-unmixing |
| PICASSO | https://github.com/nygctech/PICASSO |
| Paper | https://www.biorxiv.org/content/10.1101/2021.01.27.428247v1.full |
| cytoflow | https://github.com/cytoflow/cytoflow |
| Youtube | https://www.youtube.com/watch?v=W90qs0J29v8 |
| Link | https://imagej.net/plugins/lumos-spectral-unmixing |
| Link | https://www.biorxiv.org/content/10.1101/2023.05.30.542836v1.full |
| https://github.com/r0f1/datascience#platforms-and-pipelines |
| CellProfiler | https://github.com/CellProfiler/CellProfiler |
| CellProfilerAnalyst | https://github.com/CellProfiler/CellProfiler-Analyst |
| fractal | https://fractal-analytics-platform.github.io/ |
| Github | https://github.com/fractal-analytics-platform |
| atomai | https://github.com/pycroscopy/atomai |
| py-clesperanto | https://github.com/clesperanto/pyclesperanto_prototype/ |
| deskewing | https://github.com/clEsperanto/pyclesperanto_prototype/blob/master/demo/transforms/deskew.ipynb |
| qupath | https://github.com/qupath/qupath |
| https://github.com/r0f1/datascience#microscopy-pipelines |
| BiaPy | https://github.com/danifranco/BiaPy |
| paper | https://www.biorxiv.org/content/10.1101/2024.02.03.576026v2.full |
| SCIP | https://scalable-cytometry-image-processing.readthedocs.io/en/latest/usage.html |
| DeepCell Kiosk | https://github.com/vanvalenlab/kiosk-console/tree/master |
| IMCWorkflow | https://github.com/BodenmillerGroup/IMCWorkflow/ |
| steinbock | https://github.com/BodenmillerGroup/steinbock |
| Twitter | https://twitter.com/NilsEling/status/1715020265963258087 |
| Paper | https://www.nature.com/articles/s41596-023-00881-0 |
| workflow | https://bodenmillergroup.github.io/IMCDataAnalysis/ |
| https://github.com/r0f1/datascience#labsyspharm |
| mcmicro | https://github.com/labsyspharm/mcmicro |
| Website | https://mcmicro.org/overview/ |
| Paper | https://www.nature.com/articles/s41592-021-01308-y |
| MCQuant | https://github.com/labsyspharm/quantification |
| cylinter | https://github.com/labsyspharm/cylinter |
| Website | https://labsyspharm.github.io/cylinter/ |
| ashlar | https://github.com/labsyspharm/ashlar |
| scimap | https://github.com/labsyspharm/scimap |
| https://github.com/r0f1/datascience#cell-segmentation |
| microscopy-tree | https://biomag-lab.github.io/microscopy-tree/ |
| Paper | https://www.sciencedirect.com/science/article/abs/pii/S0962892421002518 |
| Paper | https://arxiv.org/ftp/arxiv/papers/2301/2301.02341.pdf |
| BioImage.IO | https://bioimage.io/#/ |
| MEDIAR | https://github.com/Lee-Gihun/MEDIAR |
| cellpose | https://github.com/mouseland/cellpose |
| Paper | https://www.biorxiv.org/content/10.1101/2020.02.02.931238v1 |
| Dataset | https://www.cellpose.org/dataset |
| stardist | https://github.com/stardist/stardist |
| instanseg | https://github.com/instanseg/instanseg |
| UnMicst | https://github.com/HMS-IDAC/UnMicst |
| ilastik | https://github.com/ilastik/ilastik |
| ImageJ Plugin | https://github.com/ilastik/ilastik4ij |
| nnUnet | https://github.com/MIC-DKFZ/nnUNet |
| allencell | https://www.allencell.org/segmenter.html |
| Cell-ACDC | https://github.com/SchmollerLab/Cell_ACDC |
| ZeroCostDL4Mic | https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki |
| DL4MicEverywhere | https://github.com/HenriquesLab/DL4MicEverywhere |
| EmbedSeg | https://github.com/juglab/EmbedSeg |
| segment-anything | https://github.com/facebookresearch/segment-anything |
| micro-sam | https://github.com/computational-cell-analytics/micro-sam |
| Segment-Everything-Everywhere-All-At-Once | https://github.com/UX-Decoder/Segment-Everything-Everywhere-All-At-Once |
| deepcell-tf | https://github.com/vanvalenlab/deepcell-tf/tree/master |
| DeepCell | https://deepcell.org/ |
| labkit | https://github.com/juglab/labkit-ui |
| MedImageInsight | https://arxiv.org/abs/2410.06542 |
| CHIEF | https://github.com/hms-dbmi/CHIEF |
| https://github.com/r0f1/datascience#cell-segmentation-datasets |
| cellpose | https://www.cellpose.org/dataset |
| omnipose | http://www.cellpose.org/dataset_omnipose |
| LIVECell | https://github.com/sartorius-research/LIVECell |
| Sartorius | https://www.kaggle.com/competitions/sartorius-cell-instance-segmentation/overview |
| EmbedSeg | https://github.com/juglab/EmbedSeg/releases/tag/v0.1.0 |
| connectomics | https://sites.google.com/view/connectomics/ |
| ZeroCostDL4Mic | https://www.ebi.ac.uk/biostudies/BioImages/studies/S-BIAD895 |
| https://github.com/r0f1/datascience#evaluation-1 |
| seg-eval | https://github.com/lstrgar/seg-eval |
| Paper | https://www.biorxiv.org/content/10.1101/2023.02.23.529809v1.full.pdf |
| https://github.com/r0f1/datascience#feature-engineering-images |
| Computer vision challenges in drug discovery - Maciej Hermanowicz | https://www.youtube.com/watch?v=Y5GJmnIhvFk |
| CellProfiler | https://github.com/CellProfiler/CellProfiler |
| scikit-image | https://github.com/scikit-image/scikit-image |
| scikit-image regionprops | https://scikit-image.org/docs/dev/api/skimage.measure.html#skimage.measure.regionprops |
| mahotas | https://github.com/luispedro/mahotas |
| example | https://github.com/luispedro/python-image-tutorial/blob/master/Segmenting%20cell%20images%20(fluorescent%20microscopy).ipynb |
| pyradiomics | https://github.com/AIM-Harvard/pyradiomics |
| pyefd | https://github.com/hbldh/pyefd |
| pyvips | https://github.com/libvips/pyvips/tree/master |
| https://github.com/r0f1/datascience#domain-adaptation--batch-effect-correction |
| Tran - A benchmark of batch-effect correction methods for single-cell RNA sequencing data | https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1850-9 |
| Code | https://github.com/JinmiaoChenLab/Batch-effect-removal-benchmarking |
| R Tutorial on correcting batch effects | https://broadinstitute.github.io/2019_scWorkshop/correcting-batch-effects.html |
| harmonypy | https://github.com/slowkow/harmonypy |
| pyliger | https://github.com/welch-lab/pyliger |
| R package | https://github.com/welch-lab/liger |
| nimfa | https://github.com/mims-harvard/nimfa |
| scgen | https://github.com/theislab/scgen |
| Doc | https://scgen.readthedocs.io/en/stable/ |
| CORAL | https://github.com/google-research/google-research/tree/30e54523f08d963ced3fbb37c00e9225579d2e1d/correct_batch_effects_wdn |
| Code | https://github.com/google-research/google-research/blob/30e54523f08d963ced3fbb37c00e9225579d2e1d/correct_batch_effects_wdn/transform.py#L152 |
| Paper | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7050548/ |
| adapt | https://github.com/adapt-python/adapt |
| pytorch-adapt | https://github.com/KevinMusgrave/pytorch-adapt |
| https://github.com/r0f1/datascience#sequencing |
| Single cell tutorial | https://github.com/theislab/single-cell-tutorial |
| PyDESeq2 | https://github.com/owkin/PyDESeq2 |
| cellxgene | https://github.com/chanzuckerberg/cellxgene |
| scanpy | https://github.com/theislab/scanpy |
| tutorial | https://github.com/theislab/single-cell-tutorial |
| besca | https://github.com/bedapub/besca |
| janggu | https://github.com/BIMSBbioinfo/janggu |
| gdsctools | https://github.com/CancerRxGene/gdsctools |
| doc | https://gdsctools.readthedocs.io/en/master/ |
| monkeybread | https://github.com/immunitastx/monkeybread |
| https://github.com/r0f1/datascience#drug-discovery |
| TDC | https://github.com/mims-harvard/TDC/tree/main |
| DeepPurpose | https://github.com/kexinhuang12345/DeepPurpose |
| https://github.com/r0f1/datascience#neural-networks |
| mit6874 | https://mit6874.github.io/ |
| ConvNet Shape Calculator | https://madebyollin.github.io/convnet-calculator/ |
| Great Gradient Descent Article | https://towardsdatascience.com/10-gradient-descent-optimisation-algorithms-86989510b5e9 |
| Intro to semi-supervised learning | https://lilianweng.github.io/lil-log/2021/12/05/semi-supervised-learning.html |
| https://github.com/r0f1/datascience#tutorials--viewer |
| Google Tuning Playbook | https://github.com/google-research/tuning_playbook |
| fast.ai course | https://course.fast.ai/ |
| 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 | http://vis.ensmallen.org/ |
| Another visualization | https://github.com/jettify/pytorch-optimizer |
| cutouts-explorer | https://github.com/mgckind/cutouts-explorer |
| https://github.com/r0f1/datascience#image-related |
| 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 |
| pyvips | https://github.com/libvips/pyvips/tree/master |
| https://github.com/r0f1/datascience#lossfunction-related |
| SegLoss | https://github.com/JunMa11/SegLoss |
| https://github.com/r0f1/datascience#activation-functions |
| rational_activations | https://github.com/ml-research/rational_activations |
| https://github.com/r0f1/datascience#text-related |
| ktext | https://github.com/hamelsmu/ktext |
| textgenrnn | https://github.com/minimaxir/textgenrnn |
| ctrl | https://github.com/salesforce/ctrl |
| https://github.com/r0f1/datascience#neural-network-and-deep-learning-frameworks |
| OpenMMLab | https://github.com/open-mmlab |
| 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/r0f1/datascience#libs-general |
| keras | https://keras.io/ |
| tensorflow | https://www.tensorflow.org/ |
| examples | https://gist.github.com/candlewill/552fa102352ccce42fd829ae26277d24 |
| 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 |
| 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 |
| ffcv | https://github.com/libffcv/ffcv |
| https://github.com/r0f1/datascience#libs-pytorch |
| Good PyTorch Introduction | https://cs230.stanford.edu/blog/pytorch/ |
| skorch | https://github.com/dnouri/skorch |
| talk | https://www.youtube.com/watch?v=0J7FaLk0bmQ |
| slides | https://github.com/thomasjpfan/skorch_talk |
| fastai | https://github.com/fastai/fastai |
| timm | https://github.com/rwightman/pytorch-image-models |
| ignite | https://github.com/pytorch/ignite |
| torchcv | https://github.com/donnyyou/torchcv |
| pytorch-optimizer | https://github.com/jettify/pytorch-optimizer |
| pytorch-lightning | https://github.com/PyTorchLightning/PyTorch-lightning |
| litserve | https://github.com/Lightning-AI/LitServe |
| lightly | https://github.com/lightly-ai/lightly |
| MONAI | https://github.com/project-monai/monai |
| kornia | https://github.com/kornia/kornia |
| torchinfo | https://github.com/Tylep/torchinfo |
| lovely-tensors | https://github.com/xl0/lovely-tensors/ |
| https://github.com/r0f1/datascience#distributed-libs |
| flexflow | https://github.com/flexflow/FlexFlow |
| horovod | https://github.com/horovod/horovod |
| https://github.com/r0f1/datascience#architecture-visualization |
| Awesome List | https://github.com/ashishpatel26/Tools-to-Design-or-Visualize-Architecture-of-Neural-Network |
| netron | https://github.com/lutzroeder/netron |
| visualkeras | https://github.com/paulgavrikov/visualkeras |
| https://github.com/r0f1/datascience#computer-vision-general |
| roboflow | https://github.com/roboflow/supervision |
| https://github.com/r0f1/datascience#object-detection--instance-segmentation |
| Metrics reloaded: Recommendations for image analysis validation | https://arxiv.org/abs/2206.01653 |
| Code | https://github.com/Project-MONAI/MetricsReloaded |
| Twitter Thread | https://twitter.com/lena_maierhein/status/1625450342006521857 |
| Good Yolo Explanation | https://jonathan-hui.medium.com/real-time-object-detection-with-yolo-yolov2-28b1b93e2088 |
| ultralytics | https://github.com/ultralytics/ultralytics |
| 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 |
| Detic | https://github.com/facebookresearch/Detic |
| EasyCV | https://github.com/alibaba/EasyCV |
| https://github.com/r0f1/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/r0f1/datascience#applications-and-snippets |
| 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/r0f1/datascience#variational-autoencoders-vaes |
| Variational Autoencoder Explanation Video | https://www.youtube.com/watch?v=9zKuYvjFFS8 |
| disentanglement_lib | https://github.com/google-research/disentanglement_lib |
| ladder-vae-pytorch | https://github.com/addtt/ladder-vae-pytorch |
| benchmark_VAE | https://github.com/clementchadebec/benchmark_VAE |
| https://github.com/r0f1/datascience#generative-adversarial-networks-gans |
| Awesome GAN Applications | https://github.com/nashory/gans-awesome-applications |
| The GAN Zoo | https://github.com/hindupuravinash/the-gan-zoo |
| CycleGAN and Pix2pix | https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix |
| TensorFlow GAN implementations | https://github.com/hwalsuklee/tensorflow-generative-model-collections |
| PyTorch GAN implementations | https://github.com/znxlwm/pytorch-generative-model-collections |
| PyTorch GAN implementations | https://github.com/eriklindernoren/PyTorch-GAN#adversarial-autoencoder |
| StudioGAN | https://github.com/POSTECH-CVLab/PyTorch-StudioGAN |
| https://github.com/r0f1/datascience#transformers |
| The Annotated Transformer | https://nlp.seas.harvard.edu/annotated-transformer/ |
| Transformers from Scratch | https://e2eml.school/transformers.html |
| Neural Networks: Zero to Hero | https://karpathy.ai/zero-to-hero.html |
| SegFormer | https://github.com/NVlabs/SegFormer |
| esvit | https://github.com/microsoft/esvit |
| nystromformer | https://github.com/Rishit-dagli/Nystromformer |
| https://github.com/r0f1/datascience#deep-learning-on-structured-data |
| Great overview for deep learning for tabular data | https://sebastianraschka.com/blog/2022/deep-learning-for-tabular-data.html |
| https://github.com/r0f1/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/r0f1/datascience#model-conversion |
| hummingbird | https://github.com/microsoft/hummingbird |
| https://github.com/r0f1/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/r0f1/datascience#regression |
| paper | https://onlinelibrary.wiley.com/doi/10.1002/sim.10208 |
| 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 |
| Generalized Additive Models | https://m-clark.github.io/generalized-additive-models/ |
| 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 |
| MAPIE | https://github.com/scikit-learn-contrib/MAPIE |
| https://github.com/r0f1/datascience#polynomials |
| orthopy | https://github.com/nschloe/orthopy |
| https://github.com/r0f1/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/r0f1/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 |
| TensorFlow similarity | https://github.com/tensorflow/similarity |
| https://github.com/r0f1/datascience#distance-functions |
| Steck et al. - Is Cosine-Similarity of Embeddings Really About Similarity? | https://arxiv.org/abs/2403.05440 |
| scipy.spatial | https://docs.scipy.org/doc/scipy/reference/spatial.distance.html |
| vegdist | https://rdrr.io/cran/vegan/man/vegdist.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/r0f1/datascience#self-supervised-learning |
| lightly | https://github.com/lightly-ai/lightly |
| vissl | https://github.com/facebookresearch/vissl |
| https://github.com/r0f1/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 |
| Hierarchical Cluster Analysis (R Tutorial) | https://uc-r.github.io/hc_clustering |
| Schubert - Stop using the elbow criterion for k-means and how to choose the number of clusters instead | https://arxiv.org/abs/2212.12189 |
| 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 |
| 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 |
| FastPG | https://github.com/sararselitsky/FastPG |
| Paper | https://www.researchgate.net/publication/342339899_FastPG_Fast_clustering_of_millions_of_single_cells |
| HypHC | https://github.com/HazyResearch/HypHC |
| BanditPAM | https://github.com/ThrunGroup/BanditPAM |
| dendextend | https://github.com/talgalili/dendextend |
| DeepDPM | https://github.com/BGU-CS-VIL/DeepDPM |
| https://github.com/r0f1/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/r0f1/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/r0f1/datascience#critical-ai-texts |
| Sublime - The Return of Pseudosciences in Artificial Intelligence: Have Machine Learning and Deep Learning Forgotten Lessons from Statistics and History? | https://arxiv.org/abs/2411.18656 |
| https://github.com/r0f1/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 |
| Visual Fourier explanation | https://dsego.github.io/demystifying-fourier/ |
| The Scientist & Engineer's Guide to Digital Signal Processing (1999) | https://www.analog.com/en/education/education-library/scientist_engineers_guide.html |
| Kalman Filter article | https://www.bzarg.com/p/how-a-kalman-filter-works-in-pictures |
| 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/r0f1/datascience#filtering-in-python |
| scipy.signal | https://docs.scipy.org/doc/scipy/reference/signal.html |
| Butterworth low-pass filter example | https://github.com/guillaume-chevalier/filtering-stft-and-laplace-transform |
| Savitzky–Golay filter | https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.savgol_filter.html |
| W | https://en.wikipedia.org/wiki/Savitzky%E2%80%93Golay_filter |
| pandas.Series.rolling | https://pandas.pydata.org/docs/reference/api/pandas.Series.rolling.html |
| https://github.com/r0f1/datascience#geometry |
| geomstats | https://github.com/geomstats/geomstats |
| https://github.com/r0f1/datascience#time-series |
| Time Series Anomaly Detection Review Paper | https://arxiv.org/abs/2412.20512 |
| 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 |
| darts | https://github.com/unit8co/darts |
| kats | https://github.com/facebookresearch/kats |
| prophet | https://github.com/facebook/prophet |
| neural_prophet | https://github.com/ourownstory/neural_prophet |
| pmdarima | https://github.com/alkaline-ml/pmdarima |
| modeltime | https://cran.r-project.org/web/packages/modeltime/index.html |
| 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/hzy46/TensorFlow-Time-Series-Examples |
| 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 |
| tsfel | https://github.com/fraunhoferportugal/tsfel |
| 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://github.com/pastas/pastas |
| fastdtw | https://github.com/slaypni/fastdtw |
| fable | https://www.rdocumentation.org/packages/fable/versions/0.0.0.9000 |
| 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 |
| etna | https://github.com/tinkoff-ai/etna |
| Chaos Genius | https://github.com/chaos-genius/chaos_genius |
| https://github.com/r0f1/datascience#time-series---nixla |
| nixtla | https://github.com/Nixtla/nixtla |
| statsforecast | https://github.com/Nixtla/statsforecast |
| neuralforecast | https://github.com/Nixtla/neuralforecast |
| mlforecast | https://github.com/Nixtla/mlforecast |
| hierarchicalforecast | https://github.com/Nixtla/hierarchicalforecast |
| https://github.com/r0f1/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/r0f1/datascience#financial-data-and-trading |
| 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 |
| Riskfolio-Lib | https://github.com/dcajasn/Riskfolio-Lib |
| OpenBBTerminal | https://github.com/OpenBB-finance/OpenBBTerminal |
| mplfinance | https://github.com/matplotlib/mplfinance |
| https://github.com/r0f1/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/r0f1/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://shap.readthedocs.io/en/latest/example_notebooks/tabular_examples/tree_based_models/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 |
| DeepSurvivalMachines | https://github.com/autonlab/DeepSurvivalMachines |
| auton-survival | https://github.com/autonlab/auton-survival |
| https://github.com/r0f1/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://docs.scipy.org/doc/scipy/reference/generated/scipy.special.kl_div.html |
| banpei | https://github.com/tsurubee/banpei |
| telemanom | https://github.com/khundman/telemanom |
| luminaire | https://github.com/zillow/luminaire |
| rrcf | https://github.com/kLabUM/rrcf |
| https://github.com/r0f1/datascience#concept-drift--domain-shift |
| TorchDrift | https://github.com/TorchDrift/TorchDrift |
| alibi-detect | https://github.com/SeldonIO/alibi-detect |
| evidently | https://github.com/evidentlyai/evidently |
| Lipton et al. - Detecting and Correcting for Label Shift with Black Box Predictors | https://arxiv.org/abs/1802.03916 |
| Bu et al. - A pdf-Free Change Detection Test Based on Density Difference Estimation | https://ieeexplore.ieee.org/document/7745962 |
| https://github.com/r0f1/datascience#ranking |
| lightning | https://github.com/scikit-learn-contrib/lightning |
| https://github.com/r0f1/datascience#causal-inference |
| https://github.com/r0f1/datascience#texts-1 |
| Chatton et al. - The Causal Cookbook: Recipes for Propensity Scores, G-Computation, and Doubly Robust Standardization | https://journals.sagepub.com/doi/10.1177/25152459241236149 |
| Statistical Rethinking | https://github.com/rmcelreath/stat_rethinking_2022 |
| R | https://bookdown.org/content/4857/ |
| python | https://github.com/pymc-devs/resources/tree/master/Rethinking_2 |
| numpyro1 | https://github.com/asuagar/statrethink-course-numpyro-2019 |
| numpyro2 | https://fehiepsi.github.io/rethinking-numpyro/ |
| tensorflow-probability | https://github.com/ksachdeva/rethinking-tensorflow-probability |
| Naimi et al. - An introduction to g methods | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6074945/ |
| CS 594 Causal Inference and Learning | https://www.cs.uic.edu/~elena/courses/fall19/cs594cil.html |
| Marginal Effects Tutorial | https://marginaleffects.com/vignettes/gcomputation.html |
| Python Causality Handbook | https://github.com/matheusfacure/python-causality-handbook |
| The Effect: An Introduction to Research Design and Causality | https://theeffectbook.net/index.html |
| Structual Equation Modeling | https://m-clark.github.io/sem/ |
| https://github.com/r0f1/datascience#tools |
| pecan | https://pecan-tool.rpsychologist.com/ |
| dagitty | https://www.dagitty.net/ |
| dowhy | https://github.com/py-why/dowhy |
| CausalImpact | https://github.com/tcassou/causal_impact |
| R package | https://google.github.io/CausalImpact/CausalImpact.html |
| causallib | https://github.com/IBM/causallib |
| examples | https://github.com/IBM/causallib/tree/master/examples |
| causalml | https://github.com/uber/causalml |
| upliftml | https://github.com/bookingcom/upliftml |
| causality | https://github.com/akelleh/causality |
| DoubleML | https://github.com/DoubleML/doubleml-for-py |
| Tweet | https://twitter.com/ChristophMolnar/status/1574338002305880068 |
| Presentation | https://scholar.princeton.edu/sites/default/files/bstewart/files/felton.chern_.slides.20190318.pdf |
| Paper | https://arxiv.org/abs/1608.00060v1 |
| EconML | https://github.com/py-why/EconML |
| https://github.com/r0f1/datascience#papers |
| Bours - Confounding | https://edisciplinas.usp.br/pluginfile.php/5625667/mod_resource/content/3/Nontechnicalexplanation-counterfactualdefinition-confounding.pdf |
| Bours - Effect Modification and Interaction | https://www.sciencedirect.com/science/article/pii/S0895435621000330 |
| https://github.com/r0f1/datascience#probabilistic-modelling-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://www.pymc.io/projects/docs/en/stable/learn.html |
| numpyro | https://github.com/pyro-ppl/numpyro |
| pyro | https://github.com/pyro-ppl/pyro |
| 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 |
| 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 |
| talk1 | https://www.youtube.com/watch?v=KJxmC5GCWe4 |
| notebook talk1 | https://github.com/AlxndrMlk/PyDataGlobal2021/blob/main/00_PyData_Global_2021_nb_full.ipynb |
| talk2 | 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 |
| bnlearn | https://github.com/erdogant/bnlearn |
| https://github.com/r0f1/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/r0f1/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/r0f1/datascience#model-evaluation |
| evaluate | https://github.com/huggingface/evaluate |
| 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 |
| pyroc | https://github.com/noudald/pyroc |
| https://github.com/r0f1/datascience#model-uncertainty |
| awesome-conformal-prediction | https://github.com/valeman/awesome-conformal-prediction |
| uncertainty-toolbox | https://github.com/uncertainty-toolbox/uncertainty-toolbox |
| https://github.com/r0f1/datascience#model-explanation-interpretability-feature-importance |
| Princeton - Reproducibility Crisis in ML‑based Science | https://sites.google.com/princeton.edu/rep-workshop |
| Book | https://christophm.github.io/interpretable-ml-book/agnostic.html |
| Examples | https://github.com/jphall663/interpretable_machine_learning_with_python |
| Permutation Importance | https://scikit-learn.org/stable/modules/generated/sklearn.inspection.permutation_importance.html |
| Partial Dependence | https://scikit-learn.org/stable/modules/generated/sklearn.inspection.partial_dependence.html |
| shap | https://github.com/slundberg/shap |
| talk | https://www.youtube.com/watch?v=C80SQe16Rao |
| Good Shap intro | https://www.aidancooper.co.uk/a-non-technical-guide-to-interpreting-shap-analyses/ |
| shapiq | https://github.com/mmschlk/shapiq |
| 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 |
| pybreakdown | https://github.com/MI2DataLab/pyBreakDown |
| 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 |
| 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 |
| interpretml | https://github.com/interpretml/interpret |
| shapash | https://github.com/MAIF/shapash |
| imodels | https://github.com/csinva/imodels |
| captum | https://github.com/pytorch/captum |
| https://github.com/r0f1/datascience#automated-machine-learning |
| AdaNet | https://github.com/tensorflow/adanet |
| tpot | https://github.com/EpistasisLab/tpot |
| autokeras | https://github.com/jhfjhfj1/autokeras |
| nni | https://github.com/Microsoft/nni |
| mljar | https://github.com/mljar/mljar-supervised |
| automl_zero | https://github.com/google-research/google-research/tree/master/automl_zero |
| AlphaPy | https://github.com/ScottfreeLLC/AlphaPy |
| https://github.com/r0f1/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/r0f1/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/r0f1/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 |
| evotorch | https://github.com/nnaisense/evotorch |
| https://github.com/r0f1/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 |
| bbopt | https://github.com/evhub/bbopt |
| dragonfly | https://github.com/dragonfly/dragonfly |
| botorch | https://github.com/pytorch/botorch |
| ax | https://github.com/facebook/Ax |
| lightning-hpo | https://github.com/Lightning-AI/lightning-hpo |
| https://github.com/r0f1/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 |
| river | https://github.com/online-ml/river |
| Kaggler | https://github.com/jeongyoonlee/Kaggler |
| https://github.com/r0f1/datascience#active-learning |
| Talk | https://www.youtube.com/watch?v=0efyjq5rWS4 |
| modAL | https://github.com/modAL-python/modAL |
| https://github.com/r0f1/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/r0f1/datascience#deployment-and-lifecycle-management |
| https://github.com/r0f1/datascience#workflow-scheduling-and-orchestration |
| nextflow | https://github.com/goodwright/nextflow.py |
| Website | https://github.com/nextflow-io/nextflow |
| airflow | https://github.com/apache/airflow |
| prefect | https://github.com/PrefectHQ/prefect |
| dagster | https://github.com/dagster-io/dagster |
| ploomber | https://github.com/ploomber/ploomber |
| kestra | https://github.com/kestra-io/kestra |
| cml | https://github.com/iterative/cml |
| rocketry | https://github.com/Miksus/rocketry |
| huey | https://github.com/coleifer/huey |
| https://github.com/r0f1/datascience#containerization-and-docker |
| Reduce size of docker images (video) | https://www.youtube.com/watch?v=Z1Al4I4Os_A |
| Optimize Docker Image Size | https://www.augmentedmind.de/2022/02/06/optimize-docker-image-size/ |
| cog | https://github.com/replicate/cog |
| https://github.com/r0f1/datascience#data-versioning-databases-pipelines-and-model-serving |
| dvc | https://github.com/iterative/dvc |
| kedro | https://github.com/quantumblacklabs/kedro |
| feast | https://github.com/feast-dev/feast |
| Video | https://www.youtube.com/watch?v=_omcXenypmo |
| pgvector | https://github.com/pgvector/pgvector |
| pinecone | https://www.pinecone.io/ |
| truss | https://github.com/basetenlabs/truss |
| milvus | https://github.com/milvus-io/milvus |
| mlem | https://github.com/iterative/mlem |
| https://github.com/r0f1/datascience#data-science-related |
| m2cgen | https://github.com/BayesWitnesses/m2cgen |
| sklearn-porter | https://github.com/nok/sklearn-porter |
| mlflow | https://mlflow.org/ |
| 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 |
| Neptune | https://neptune.ai |
| clearml | https://github.com/allegroai/clearml |
| polyaxon | https://github.com/polyaxon/polyaxon |
| sematic | https://github.com/sematic-ai/sematic |
| zenml | https://github.com/zenml-io/zenml |
| https://github.com/r0f1/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/r0f1/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 |
| Datascience Cheatsheets | https://github.com/FavioVazquez/ds-cheatsheets |
| https://github.com/r0f1/datascience#guidelines |
| datasharing | https://github.com/jtleek/datasharing |
| https://github.com/r0f1/datascience#books-1 |
| Blum - Foundations of Data Science | https://www.cs.cornell.edu/jeh/book.pdf?file=book.pdf |
| Chan - Introduction to Probability for Data Science | https://probability4datascience.com/index.html |
| Colonescu - Principles of Econometrics with R | https://bookdown.org/ccolonescu/RPoE4/ |
| Rafael Irizarry - Introduction to Data Science | https://rafalab.dfci.harvard.edu/dsbook-part-1/ |
| Rafael Irizarry - Advanced Data Science | https://rafalab.dfci.harvard.edu/dsbook-part-2/ |
| https://github.com/r0f1/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 Biological Image Analysis | https://github.com/hallvaaw/awesome-biological-image-analysis |
| 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 Cytodata | https://github.com/cytodata/awesome-cytodata |
| Awesome Data Science | https://github.com/academic/awesome-datascience |
| 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 Industry Machine Learning | https://github.com/firmai/industry-machine-learning |
| Awesome Gradient Boosting | https://github.com/benedekrozemberczki/awesome-gradient-boosting-papers |
| Awesome Learning with Label Noise | https://github.com/subeeshvasu/Awesome-Learning-with-Label-Noise |
| Awesome Machine Learning | https://github.com/josephmisiti/awesome-machine-learning#python |
| Awesome Machine Learning Books | http://matpalm.com/blog/cool_machine_learning_books/ |
| 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 Monte Carlo Tree Search | https://github.com/benedekrozemberczki/awesome-monte-carlo-tree-search-papers |
| Awesome MLOps | https://github.com/kelvins/awesome-mlops |
| Awesome Neural Network Visualization | https://github.com/ashishpatel26/Tools-to-Design-or-Visualize-Architecture-of-Neural-Network |
| 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 Public Datasets | https://github.com/awesomedata/awesome-public-datasets |
| 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 Pytorch | https://github.com/bharathgs/Awesome-pytorch-list |
| Awesome Quantitative Finance | https://github.com/wilsonfreitas/awesome-quant |
| Awesome Recommender Systems | https://github.com/grahamjenson/list_of_recommender_systems |
| Awesome Satellite Benchmark Datasets | https://github.com/Seyed-Ali-Ahmadi/Awesome_Satellite_Benchmark_Datasets |
| Awesome Satellite Image for Deep Learning | https://github.com/satellite-image-deep-learning/techniques |
| Awesome Single Cell | https://github.com/seandavi/awesome-single-cell |
| Awesome Semantic Segmentation | https://github.com/mrgloom/awesome-semantic-segmentation |
| Awesome Sentence Embedding | https://github.com/Separius/awesome-sentence-embedding |
| Awesome Visual Attentions | https://github.com/MenghaoGuo/Awesome-Vision-Attentions |
| Awesome Visual Transformer | https://github.com/dk-liang/Awesome-Visual-Transformer |
| https://github.com/r0f1/datascience#lectures |
| NYU Deep Learning SP21 | https://www.youtube.com/playlist?list=PLLHTzKZzVU9e6xUfG10TkTWApKSZCzuBI |
| https://github.com/r0f1/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 |
| https://github.com/r0f1/datascience#contributing |
| contribution guidelines | https://github.com/r0f1/datascience/blob/master/CONTRIBUTING.md |
| https://github.com/r0f1/datascience#license |
| https://creativecommons.org/publicdomain/zero/1.0/ |
|
python
| https://github.com/topics/python |
|
data-science
| https://github.com/topics/data-science |
|
machine-learning
| https://github.com/topics/machine-learning |
|
data-mining
| https://github.com/topics/data-mining |
|
awesome
| https://github.com/topics/awesome |
|
statistics
| https://github.com/topics/statistics |
|
deep-learning
| https://github.com/topics/deep-learning |
|
data-visualization
| https://github.com/topics/data-visualization |
|
artificial-intelligence
| https://github.com/topics/artificial-intelligence |
|
datascience
| https://github.com/topics/datascience |
|
data-analysis
| https://github.com/topics/data-analysis |
|
awesome-list
| https://github.com/topics/awesome-list |
|
deeplearning
| https://github.com/topics/deeplearning |
|
bayes
| https://github.com/topics/bayes |
|
Readme
| https://github.com/r0f1/datascience#readme-ov-file |
|
CC0-1.0 license
| https://github.com/r0f1/datascience#CC0-1.0-1-ov-file |
|
Contributing
| https://github.com/r0f1/datascience#contributing-ov-file |
| Please reload this page | https://github.com/r0f1/datascience |
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