diff --git a/INTERESTING.md b/INTERESTING.md deleted file mode 100644 index 5b4e302..0000000 --- a/INTERESTING.md +++ /dev/null @@ -1,6 +0,0 @@ -#### Paradoxes -Inspection paradox | [link](https://allendowney.blogspot.com/2015/08/the-inspection-paradox-is-everywhere.html), [link](https://jakevdp.github.io/blog/2018/09/13/waiting-time-paradox/) -Simpsons paradox | [link](https://en.wikipedia.org/wiki/Simpson%27s_paradox) -Berksons paradox | [link](https://en.wikipedia.org/wiki/Berkson%27s_paradox) -Base rate fallacy | [link](https://en.wikipedia.org/wiki/Base_rate_fallacy) -Sampling bias | [link](https://en.wikipedia.org/wiki/Sampling_bias) diff --git a/README.md b/README.md index b1095b0..2898169 100644 --- a/README.md +++ b/README.md @@ -4,107 +4,320 @@ #### Core [pandas](https://pandas.pydata.org/) - Data structures built on top of [numpy](https://www.numpy.org/). -[scikit-learn](https://scikit-learn.org/stable/) - Core ML library. +[scikit-learn](https://scikit-learn.org/stable/) - Core ML library, [intelex](https://github.com/intel/scikit-learn-intelex). [matplotlib](https://matplotlib.org/) - Plotting library. -[animatplot](https://github.com/t-makaro/animatplot) - Animate plots build on matplotlib. -[seaborn](https://seaborn.pydata.org/) - Python data visualization library based on matplotlib. -[pandas_summary](https://github.com/mouradmourafiq/pandas-summary) - Basic statistics using `DataFrameSummary(df).summary()`. -[pandas_profiling](https://github.com/pandas-profiling/pandas-profiling) - Descriptive statistics using `ProfileReport`. +[seaborn](https://seaborn.pydata.org/) - Data visualization library based on matplotlib. +[ydata-profiling](https://github.com/ydataai/ydata-profiling) - Descriptive statistics using `ProfileReport`. [sklearn_pandas](https://github.com/scikit-learn-contrib/sklearn-pandas) - Helpful `DataFrameMapper` class. -[janitor](https://pyjanitor.readthedocs.io/) - Clean messy column names. [missingno](https://github.com/ResidentMario/missingno) - Missing data visualization. +[rainbow-csv](https://marketplace.visualstudio.com/items?itemName=mechatroner.rainbow-csv) - VSCode plugin to display .csv files with nice colors. -#### Pandas and Jupyter -General ticks: [link](https://www.dataquest.io/blog/jupyter-notebook-tips-tricks-shortcuts/) -[cookiecutter-data-science](https://github.com/drivendata/cookiecutter-data-science) - Project template for data science projects. -[nteract](https://nteract.io/) - Open Jupyter Notebooks with doubleclick. -[modin](https://github.com/modin-project/modin) - Parallelization library for faster pandas `DataFrame`. -[swifter](https://github.com/jmcarpenter2/swifter) - Apply any function to a pandas dataframe faster. +#### General Python Programming +[uv](https://github.com/astral-sh/uv) - Dependency management. +[just](https://github.com/casey/just) - Command runner. Replacement for make. +[python-dotenv](https://github.com/theskumar/python-dotenv) - Manage environment variables. +[structlog](https://github.com/hynek/structlog) - Python logging. +[more_itertools](https://more-itertools.readthedocs.io/en/latest/) - Extension of itertools. +[tqdm](https://github.com/tqdm/tqdm) - Progress bars for for-loops. Also supports [pandas apply()](https://stackoverflow.com/a/34365537/1820480). +[hydra](https://github.com/facebookresearch/hydra) - Configuration management. + +#### Pandas Tricks, Alternatives and Additions +[pandasvault](https://github.com/firmai/pandasvault) - Large collection of pandas tricks. +[polars](https://github.com/pola-rs/polars) - Multi-threaded alternative to pandas. [xarray](https://github.com/pydata/xarray/) - Extends pandas to n-dimensional arrays. -[blackcellmagic](https://github.com/csurfer/blackcellmagic) - Code formatting for jupyter notebooks. -[pivottablejs](https://github.com/nicolaskruchten/jupyter_pivottablejs) - Drag n drop Pivot Tables and Charts for jupyter notebooks. -[qgrid](https://github.com/quantopian/qgrid) - Pandas `DataFrame` sorting. +[mlx](https://github.com/ml-explore/mlx) - An array framework for Apple silicon. +[pandas_flavor](https://github.com/Zsailer/pandas_flavor) - Write custom accessors like `.str` and `.dt`. +[duckdb](https://github.com/duckdb/duckdb) - Efficiently run SQL queries on pandas DataFrame. +[daft](https://github.com/Eventual-Inc/Daft) - Distributed DataFrame. +[quak](https://github.com/manzt/quak) - Scalable, interactive data table, [twitter](https://x.com/trevmanz/status/1816760923949809982). + +#### Pandas Parallelization +[modin](https://github.com/modin-project/modin) - Parallelization library for faster pandas `DataFrame`. +[vaex](https://github.com/vaexio/vaex) - Out-of-Core DataFrames. +[pandarallel](https://github.com/nalepae/pandarallel) - Parallelize pandas operations. +[swifter](https://github.com/jmcarpenter2/swifter) - Apply any function to a pandas DataFrame faster. + +#### Environment and Jupyter +[Jupyter Tricks](https://www.dataquest.io/blog/jupyter-notebook-tips-tricks-shortcuts/) +[ipyflow](https://github.com/ipyflow/ipyflow) - IPython kernel for Jupyter with additional features. +[nteract](https://nteract.io/) - Open Jupyter Notebooks with doubleclick. +[papermill](https://github.com/nteract/papermill) - Parameterize and execute Jupyter notebooks, [tutorial](https://pbpython.com/papermil-rclone-report-1.html). [nbdime](https://github.com/jupyter/nbdime) - Diff two notebook files, Alternative GitHub App: [ReviewNB](https://www.reviewnb.com/). +[RISE](https://github.com/damianavila/RISE) - Turn Jupyter notebooks into presentations. +[qgrid](https://github.com/quantopian/qgrid) - Pandas `DataFrame` sorting. +[lux](https://github.com/lux-org/lux) - DataFrame visualization within Jupyter. +[pandasgui](https://github.com/adamerose/pandasgui) - GUI for viewing, plotting and analyzing Pandas DataFrames. +[dtale](https://github.com/man-group/dtale) - View and analyze Pandas data structures, integrating with Jupyter. +[itables](https://github.com/mwouts/itables) - Interactive tables in Jupyter. +[handcalcs](https://github.com/connorferster/handcalcs) - More convenient way of writing mathematical equations in Jupyter. +[notebooker](https://github.com/man-group/notebooker) - Productionize and schedule Jupyter Notebooks. +[bamboolib](https://github.com/tkrabel/bamboolib) - Intuitive GUI for tables. +[voila](https://github.com/QuantStack/voila) - Turn Jupyter notebooks into standalone web applications. +[voila-gridstack](https://github.com/voila-dashboards/voila-gridstack) - Voila grid layout. #### Extraction [textract](https://github.com/deanmalmgren/textract) - Extract text from any document. -[camelot](https://github.com/socialcopsdev/camelot) - Extract text from PDF. #### Big Data [spark](https://docs.databricks.com/spark/latest/dataframes-datasets/introduction-to-dataframes-python.html#work-with-dataframes) - `DataFrame` for big data, [cheatsheet](https://gist.github.com/crawles/b47e23da8218af0b9bd9d47f5242d189), [tutorial](https://github.com/ericxiao251/spark-syntax). -[sparkit-learn](https://github.com/lensacom/sparkit-learn) - PySpark + Scikit-learn. [dask](https://github.com/dask/dask), [dask-ml](http://ml.dask.org/) - Pandas `DataFrame` for big data and machine learning library, [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). -[turicreate](https://github.com/apple/turicreate) - Helpful `SFrame` class for out-of-memory dataframes. [h2o](https://github.com/h2oai/h2o-3) - Helpful `H2OFrame` class for out-of-memory dataframes. -[datatable](https://github.com/h2oai/datatable) - Data Table for big data support. -[cuDF](https://github.com/rapidsai/cudf) - GPU DataFrame Library. +[cuDF](https://github.com/rapidsai/cudf) - GPU DataFrame Library, [Intro](https://www.youtube.com/watch?v=6XzS5XcpicM&t=2m50s). +[cupy](https://github.com/cupy/cupy) - NumPy-like API accelerated with CUDA. [ray](https://github.com/ray-project/ray/) - Flexible, high-performance distributed execution framework. -[mars](https://github.com/mars-project/mars) - Tensor-based unified framework for large-scale data computation. [bottleneck](https://github.com/kwgoodman/bottleneck) - Fast NumPy array functions written in C. -[bolz](https://github.com/Blosc/bcolz) - A columnar data container that can be compressed. -[cupy](https://github.com/cupy/cupy) - NumPy-like API accelerated with CUDA. +[petastorm](https://github.com/uber/petastorm) - Data access library for parquet files by Uber. +[zarr](https://github.com/zarr-developers/zarr-python) - Distributed NumPy arrays. +[NVTabular](https://github.com/NVIDIA/NVTabular) - Feature engineering and preprocessing library for tabular data by Nvidia. +[tensorstore](https://github.com/google/tensorstore) - Reading and writing large multi-dimensional arrays (Google). -##### Command line tools -[ni](https://github.com/spencertipping/ni) - Command line tool for big data. -[xsv](https://github.com/BurntSushi/xsv) - Command line tool for indexing, slicing, analyzing, splitting and joining CSV files. -[csvkit](https://csvkit.readthedocs.io/en/1.0.3/) - Another command line tool for CSV files. +#### Command line tools, CSV +[csvkit](https://github.com/wireservice/csvkit) - Command line tool for CSV files. [csvsort](https://pypi.org/project/csvsort/) - Sort large csv files. -#### Statistics -Visualizations - [Null Hypothesis Significance Testing (NHST)](https://rpsychologist.com/d3/NHST/), [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/) -[Common statistical tests explained](https://lindeloev.github.io/tests-as-linear/) -[Bland-Altman Plot](http://www.statsmodels.org/dev/generated/statsmodels.graphics.agreement.mean_diff_plot.html) - Plot for agreement between two methods of measurement. -[scikit-posthocs](https://github.com/maximtrp/scikit-posthocs) - Statistical post-hoc tests for pairwise multiple comparisons. +#### Classical Statistics + +##### Datasets +[Rdatasets](https://vincentarelbundock.github.io/Rdatasets/articles/data.html) - Collection of more than 2000 datasets, stored as csv files (R package). +[crimedatasets](https://lightbluetitan.github.io/crimedatasets/) - Datasets focused on crimes, criminal activities (R package). +[educationr](https://lightbluetitan.github.io/educationr/) - Datasets related to education (performance, learning methods, test scores, absenteeism) (R package). +[MedDataSets](https://lightbluetitan.github.io/meddatasets/index.html) - Datasets related to medicine, diseases, treatments, drugs, and public health (R package). +[oncodatasets](https://lightbluetitan.github.io/oncodatasets/) - Datasets focused on cancer research, survival rates, genetic studies, biomarkers, epidemiology (R package). +[timeseriesdatasets_R](https://lightbluetitan.github.io/timeseriesdatasets_R/) - Time series datasets (R package). +[usdatasets](https://lightbluetitan.github.io/usdatasets/) - US-exclusive datasets (crime, economics, education, finance, energy, healthcare) (R package). + + +##### 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) + +##### Correlation +[Guess the Correlation](https://www.guessthecorrelation.com/) - Correlation guessing game. +[phik](https://github.com/kaveio/phik) - Correlation between categorical, ordinal and interval variables. +[hoeffd](https://search.r-project.org/CRAN/refmans/Hmisc/html/hoeffd.html) - Hoeffding's D Statistics, measure of dependence (R package). + +##### Packages +[statsmodels](https://www.statsmodels.org/stable/index.html) - Statistical tests. +[linearmodels](https://github.com/bashtage/linearmodels) - Instrumental variable and panel data models. +[pingouin](https://github.com/raphaelvallat/pingouin) - Statistical tests. [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) - Statistical tests. +[scikit-posthocs](https://github.com/maximtrp/scikit-posthocs) - Statistical post-hoc tests for pairwise multiple comparisons. +Bland-Altman Plot [1](https://pingouin-stats.org/generated/pingouin.plot_blandaltman.html), [2](http://www.statsmodels.org/dev/generated/statsmodels.graphics.agreement.mean_diff_plot.html) - Plot for agreement between two methods of measurement. +[ANOVA](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.f_oneway.html) +[StatCheck](https://statcheck.steveharoz.com/) - Extract statistics from articles and recompute p-values (R package). +[TOSTER](https://github.com/Lakens/TOSTER) - TOST equivalence test and power functions (R package). + +##### Effect Size +[Estimating Effect Sizes From Pretest-Posttest-Control Group Designs](https://journals.sagepub.com/doi/epdf/10.1177/1094428106291059) - Scott B. Morris, [Twitter](https://twitter.com/MatthewBJane/status/1742588609025200557) + +##### Statistical Tests +[test_proportions_2indep](https://www.statsmodels.org/dev/generated/statsmodels.stats.proportion.test_proportions_2indep.html) - Proportion test. +[G-Test](https://en.wikipedia.org/wiki/G-test) - Alternative to chi-square test, [power_divergence](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.power_divergence.html). + +##### Comparing Two Populations +[torch-two-sample](https://github.com/josipd/torch-two-sample) - Friedman-Rafsky Test: Compare two population based on a multivariate generalization of the Runstest. [Explanation](https://www.real-statistics.com/multivariate-statistics/multivariate-normal-distribution/friedman-rafsky-test/), [Application](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5014134/) + +##### Power and Sample Size Calculations +[pwrss](https://cran.r-project.org/web/packages/pwrss/index.html) - Statistical Power and Sample Size Calculation Tools (R package), [Tutorial with t-test](https://rpubs.com/metinbulus/welch) + +##### Interim Analyses / Sequential Analysis / Stopping +[Sequential Analysis](https://en.wikipedia.org/wiki/Sequential_analysis) - Wikipedia. +[sequential](https://cran.r-project.org/web/packages/Sequential/Sequential.pdf) - Exact Sequential Analysis for Poisson and Binomial Data (R package). +[confseq](https://github.com/gostevehoward/confseq) - Uniform boundaries, confidence sequences, and always-valid p-values. + +##### 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/) +[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/) + +##### 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) + +##### 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) + +#### 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) + +#### 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/) - Large tool suite for working with epidemiological data (R packages). [Github](https://github.com/reconhub) +[incidence2](https://github.com/reconhub/incidence2) - Computation, handling, visualisation and simple modelling of incidence (R package). +[EpiEstim](https://github.com/mrc-ide/EpiEstim) - Estimate time varying instantaneous reproduction number R during epidemics (R package) [paper](https://academic.oup.com/aje/article/178/9/1505/89262). +[researchpy](https://github.com/researchpy/researchpy) - Helpful `summary_cont()` function for summary statistics (Table 1). +[zEpid](https://github.com/pzivich/zEpid) - Epidemiology analysis package, [Tutorial](https://github.com/pzivich/Python-for-Epidemiologists). +[tipr](https://github.com/LucyMcGowan/tipr) - Sensitivity analyses for unmeasured confounders (R package). +[quartets](https://github.com/r-causal/quartets) - Anscombe’s Quartet, Causal Quartet, [Datasaurus Dozen](https://github.com/jumpingrivers/datasauRus) and others (R package). +[episensr](https://cran.r-project.org/web/packages/episensr/vignettes/episensr.html) - Quantitative Bias Analysis for Epidemiologic Data (=simulation of possible effects of different sources of bias) (R package). + +#### 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/) - Stanford CS class. #### Exploration and Cleaning +[Checklist](https://github.com/r0f1/ml_checklist). +[pyjanitor](https://github.com/pyjanitor-devs/pyjanitor) - Clean messy column names. +[skimpy](https://github.com/aeturrell/skimpy) - Create summary statistics of dataframes. Helpful `clean_columns()` function. +[pandera](https://github.com/unionai-oss/pandera) - Data / Schema validation. [impyute](https://github.com/eltonlaw/impyute) - Imputations. [fancyimpute](https://github.com/iskandr/fancyimpute) - Matrix completion and imputation algorithms. [imbalanced-learn](https://github.com/scikit-learn-contrib/imbalanced-learn) - Resampling for imbalanced datasets. [tspreprocess](https://github.com/MaxBenChrist/tspreprocess) - Time series preprocessing: Denoising, Compression, Resampling. [Kaggler](https://github.com/jeongyoonlee/Kaggler) - Utility functions (`OneHotEncoder(min_obs=100)`) -[pyupset](https://github.com/ImSoErgodic/py-upset) - Visualizing intersecting sets. -[pyemd](https://github.com/wmayner/pyemd) - Earth Mover's Distance, similarity between histograms. + +#### Noisy Labels +[cleanlab](https://github.com/cleanlab/cleanlab) - Machine learning with noisy labels, finding mislabelled data, and uncertainty quantification. Also see awesome list below. +[doubtlab](https://github.com/koaning/doubtlab) - Find bad or noisy labels. + +#### Train / Test Split +[iterative-stratification](https://github.com/trent-b/iterative-stratification) - Stratification of multilabel data. #### Feature Engineering +[Vincent Warmerdam: Untitled12.ipynb](https://www.youtube.com/watch?v=yXGCKqo5cEY) - Using df.pipe() +[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) - Pipeline, [examples](https://github.com/jem1031/pandas-pipelines-custom-transformers). [pdpipe](https://github.com/shaypal5/pdpipe) - Pipelines for DataFrames. -[few](https://github.com/lacava/few) - Feature engineering wrapper for sklearn. -[skoot](https://github.com/tgsmith61591/skoot) - Pipeline helper functions. +[scikit-lego](https://github.com/koaning/scikit-lego) - Custom transformers for pipelines. [categorical-encoding](https://github.com/scikit-learn-contrib/categorical-encoding) - Categorical encoding of variables, [vtreat (R package)](https://cran.r-project.org/web/packages/vtreat/vignettes/vtreat.html). [dirty_cat](https://github.com/dirty-cat/dirty_cat) - Encoding dirty categorical variables. [patsy](https://github.com/pydata/patsy/) - R-like syntax for statistical models. [mlxtend](https://rasbt.github.io/mlxtend/user_guide/feature_extraction/LinearDiscriminantAnalysis/) - LDA. [featuretools](https://github.com/Featuretools/featuretools) - Automated feature engineering, [example](https://github.com/WillKoehrsen/automated-feature-engineering/blob/master/walk_through/Automated_Feature_Engineering.ipynb). [tsfresh](https://github.com/blue-yonder/tsfresh) - Time series feature engineering. +[temporian](https://github.com/google/temporian) - Time series feature engineering by Google. [pypeln](https://github.com/cgarciae/pypeln) - Concurrent data pipelines. +[feature-engine](https://github.com/feature-engine/feature_engine) - Encoders, transformers, etc. #### Feature Selection -[Tutorial](https://machinelearningmastery.com/feature-selection-machine-learning-python/), [Talk](https://www.youtube.com/watch?v=JsArBz46_3s) +[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) +Blog post series - [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/) +Tutorials - [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) - Feature selection. +[eli5](https://eli5.readthedocs.io/en/latest/blackbox/permutation_importance.html#feature-selection) - Feature selection using permutation importance. [scikit-feature](https://github.com/jundongl/scikit-feature) - Feature selection algorithms. [stability-selection](https://github.com/scikit-learn-contrib/stability-selection) - Stability selection. [scikit-rebate](https://github.com/EpistasisLab/scikit-rebate) - Relief-based feature selection algorithms. [scikit-genetic](https://github.com/manuel-calzolari/sklearn-genetic) - Genetic feature selection. [boruta_py](https://github.com/scikit-learn-contrib/boruta_py) - Feature selection, [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) - Boruta feature selection algorithm + shapley values. [linselect](https://github.com/efavdb/linselect) - Feature selection package. +[mlxtend](https://rasbt.github.io/mlxtend/user_guide/feature_selection/ExhaustiveFeatureSelector/) - Exhaustive feature selection. +[BoostARoota](https://github.com/chasedehan/BoostARoota) - Xgboost feature selection algorithm. +[INVASE](https://github.com/jsyoon0823/INVASE) - Instance-wise Variable Selection using Neural Networks. +[SubTab](https://github.com/AstraZeneca/SubTab) - Subsetting Features of Tabular Data for Self-Supervised Representation Learning, AstraZeneca. +[mrmr](https://github.com/smazzanti/mrmr) - Maximum Relevance and Minimum Redundancy Feature Selection, [Website](http://home.penglab.com/proj/mRMR/). +[arfs](https://github.com/ThomasBury/arfs) - All Relevant Feature Selection. +[VSURF](https://github.com/robingenuer/VSURF) - Variable Selection Using Random Forests (R package) [doc](https://www.rdocumentation.org/packages/VSURF/versions/1.1.0/topics/VSURF). +[FeatureSelectionGA](https://github.com/kaushalshetty/FeatureSelectionGA) - Feature Selection using Genetic Algorithm. -#### Dimensionality Reduction +#### Subset Selection +[apricot](https://github.com/jmschrei/apricot) - Selecting subsets of data sets to train machine learning models quickly. +[ducks](https://github.com/manimino/ducks) - Index data for fast lookup by any combination of fields. + +#### Dimensionality Reduction / Representation Learning + +##### Selection +Check also the Clustering section and self-supervised learning section for ideas! +[Review](https://members.loria.fr/moberger/Enseignement/AVR/Exposes/TR_Dimensiereductie.pdf) + +PCA - [link](https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html) +Autoencoder - [link](https://blog.keras.io/building-autoencoders-in-keras.html) +Isomaps - [link](https://scikit-learn.org/stable/modules/generated/sklearn.manifold.Isomap.html#sklearn.manifold.Isomap) +LLE - [link](https://scikit-learn.org/stable/modules/generated/sklearn.manifold.LocallyLinearEmbedding.html) +Force-directed graph drawing - [link](https://scanpy.readthedocs.io/en/stable/api/scanpy.tl.draw_graph.html#scanpy.tl.draw_graph) +MDS - [link](https://scikit-learn.org/stable/modules/generated/sklearn.manifold.MDS.html) +Diffusion Maps - [link](https://scanpy.readthedocs.io/en/stable/api/scanpy.tl.diffmap.html) +t-SNE - [link](https://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html#sklearn.manifold.TSNE) +NeRV - [link](https://github.com/ziyuang/pynerv), [paper](https://www.jmlr.org/papers/volume11/venna10a/venna10a.pdf) +MDR - [link](https://github.com/EpistasisLab/scikit-mdr) +UMAP - [link](https://github.com/lmcinnes/umap) +Random Projection - [link](https://scikit-learn.org/stable/modules/random_projection.html) +Ivis - [link](https://github.com/beringresearch/ivis) +SimCLR - [link](https://github.com/lightly-ai/lightly) + +##### Neural-network based +[esvit](https://github.com/microsoft/esvit) - Vision Transformers for Representation Learning (Microsoft). +[MCML](https://github.com/pachterlab/MCML) - Semi-supervised dimensionality reduction of Multi-Class, Multi-Label data (sequencing data) [paper](https://www.biorxiv.org/content/10.1101/2021.08.25.457696v1). + +##### Packages +[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) and [sklearn.decomposition](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.decomposition) - PCA, t-SNE, MDS, Isomaps and others. +Additional plots for PCA - Factor Loadings, Cumulative Variance Explained, [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) - Johnson-Lindenstrauss lemma, Gaussian random projection, Sparse random projection. +[sklearn.cross_decomposition](https://scikit-learn.org/stable/modules/cross_decomposition.html#cross-decomposition) - Partial least squares, supervised estimators for dimensionality reduction and regression. [prince](https://github.com/MaxHalford/prince) - Dimensionality reduction, factor analysis (PCA, MCA, CA, FAMD). -[sklearn](https://scikit-learn.org/stable/modules/generated/sklearn.manifold.MDS.html) - Multidimensional scaling (MDS). -[sklearn](https://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html) - t-distributed Stochastic Neighbor Embedding (t-SNE), [intro](https://distill.pub/2016/misread-tsne/). Faster implementations: [lvdmaaten](https://lvdmaaten.github.io/tsne/), [MulticoreTSNE](https://github.com/DmitryUlyanov/Multicore-TSNE). -[sklearn](http://scikit-learn.org/stable/modules/generated/sklearn.decomposition.TruncatedSVD.html) - Truncated SVD (aka LSA). -[mdr](https://github.com/EpistasisLab/scikit-mdr) - Dimensionality reduction, multifactor dimensionality reduction (MDR). -[umap](https://github.com/lmcinnes/umap) - Uniform Manifold Approximation and Projection. -[FIt-SNE](https://github.com/KlugerLab/FIt-SNE) - Fast Fourier Transform-accelerated Interpolation-based t-SNE. +Faster t-SNE implementations: [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) - Uniform Manifold Approximation and Projection, [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) - Hierarchical UMAP. +[sleepwalk](https://github.com/anders-biostat/sleepwalk/) - Explore embeddings, interactive visualization (R package). +[somoclu](https://github.com/peterwittek/somoclu) - Self-organizing map. +[scikit-tda](https://github.com/scikit-tda/scikit-tda) - Topological Data Analysis, [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) - Topological Data Analysis. +[ivis](https://github.com/beringresearch/ivis) - Dimensionality reduction using Siamese Networks. +[trimap](https://github.com/eamid/trimap) - Dimensionality reduction using triplets. +[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) - Projection pursuit, Sufficient dimension reduction, Robust M-estimators. +[DBS](https://cran.r-project.org/web/packages/DatabionicSwarm/vignettes/DatabionicSwarm.html) - DatabionicSwarm (R package). +[contrastive](https://github.com/abidlabs/contrastive) - Contrastive PCA. +[scPCA](https://github.com/PhilBoileau/scPCA) - Sparse contrastive PCA (R package). +[tmap](https://github.com/reymond-group/tmap) - Visualization library for large, high-dimensional data sets. +[lollipop](https://github.com/neurodata/lollipop) - Linear Optimal Low Rank Projection. +[linearsdr](https://github.com/HarrisQ/linearsdr) - Linear Sufficient Dimension Reduction (R package). +[PHATE](https://github.com/KrishnaswamyLab/PHATE) - Tool for visualizing high dimensional data. #### Visualization [All charts](https://datavizproject.com/), [Austrian monuments](https://github.com/njanakiev/austrian-monuments-visualization). +[Better heatmaps and correlation plots](https://towardsdatascience.com/better-heatmaps-and-correlation-matrix-plots-in-python-41445d0f2bec). +[Example notebooks for interactive visualizations](https://github.com/nicolaskruchten/pydata_global_2021/tree/main)(Plotly,Seaborn, Holoviz, Altair) [cufflinks](https://github.com/santosjorge/cufflinks) - Dynamic visualization library, wrapper for [plotly](https://plot.ly/), [medium](https://towardsdatascience.com/the-next-level-of-data-visualization-in-python-dd6e99039d5e), [example](https://github.com/WillKoehrsen/Data-Analysis/blob/master/plotly/Plotly%20Whirlwind%20Introduction.ipynb). -[physt](https://github.com/janpipek/physt) - Better histograms, [talk](https://www.youtube.com/watch?v=ZG-wH3-Up9Y). -[matplotlib_venn](https://github.com/konstantint/matplotlib-venn) - Venn diagrams. -[joypy](https://github.com/sbebo/joypy) - Draw stacked density plots. +[physt](https://github.com/janpipek/physt) - Better histograms, [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) - Fast histograms. +[matplotlib_venn](https://github.com/konstantint/matplotlib-venn) - Venn diagrams, [alternative](https://github.com/penrose/penrose). +[ridgeplot](https://github.com/tpvasconcelos/ridgeplot) - Ridge plots. +[joypy](https://github.com/sbebo/joypy) - Draw stacked density plots (=ridge plots), [Ridge plots in seaborn](https://seaborn.pydata.org/examples/kde_ridgeplot.html). [mosaic plots](https://www.statsmodels.org/dev/generated/statsmodels.graphics.mosaicplot.mosaic.html) - Categorical variable visualization, [example](https://sukhbinder.wordpress.com/2018/09/18/mosaic-plot-in-python/). -[yellowbrick](https://github.com/DistrictDataLabs/yellowbrick) - Wrapper for matplotlib for diagnosic ML plots. +[scikit-plot](https://github.com/reiinakano/scikit-plot) - ROC curves and other visualizations for ML models. +[yellowbrick](https://github.com/DistrictDataLabs/yellowbrick) - Visualizations for ML models (similar to scikit-plot). [bokeh](https://bokeh.pydata.org/en/latest/) - Interactive visualization library, [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) - Plotting library. +[animatplot](https://github.com/t-makaro/animatplot) - Animate plots build on matplotlib. [plotnine](https://github.com/has2k1/plotnine) - ggplot for Python. [altair](https://altair-viz.github.io/) - Declarative statistical visualization library. [bqplot](https://github.com/bloomberg/bqplot) - Plotting library for IPython/Jupyter Notebooks. @@ -112,23 +325,48 @@ Visualizations - [Null Hypothesis Significance Testing (NHST)](https://rpsycholo [dtreeviz](https://github.com/parrt/dtreeviz) - Decision tree visualization and model interpretation. [chartify](https://github.com/spotify/chartify/) - Generate charts. [VivaGraphJS](https://github.com/anvaka/VivaGraphJS) - Graph visualization (JS package). -[pm](https://github.com/anvaka/pm) - Navigatable 3D graph visualization (JS package), [example](https://w2v-vis-dot-hcg-team-di.appspot.com/#/galaxy/word2vec?cx=5698&cy=-5135&cz=5923&lx=0.1127&ly=0.3238&lz=-0.1680&lw=0.9242&ml=150&s=1.75&l=1&v=hc). +[pm](https://github.com/anvaka/pm) - Navigatable 3D graph visualization (JS package). [python-ternary](https://github.com/marcharper/python-ternary) - Triangle plots. [falcon](https://github.com/uwdata/falcon) - Interactive visualizations for big data. +[hiplot](https://github.com/facebookresearch/hiplot) - High dimensional Interactive Plotting. +[visdom](https://github.com/fossasia/visdom) - Live Visualizations. +[mpl-scatter-density](https://github.com/astrofrog/mpl-scatter-density) - Scatter density plots. Alternative to 2d-histograms. +[ComplexHeatmap](https://github.com/jokergoo/ComplexHeatmap) - Complex heatmaps for multidimensional genomic data (R package). +[largeVis](https://github.com/elbamos/largeVis) - Visualize embeddings (t-SNE etc.) (R package). +[proplot](https://github.com/proplot-dev/proplot) - Matplotlib wrapper. +[morpheus](https://software.broadinstitute.org/morpheus/) - Broad Institute tool matrix visualization and analysis software. [Source](https://github.com/cmap/morpheus.js), Tutorial: [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) - Interactive 2D scatter plot widget for Jupyter. +[fastplotlib](https://github.com/fastplotlib/fastplotlib) - Fast plotting library using pygfx. + +#### Colors +[palettable](https://github.com/jiffyclub/palettable) - Color palettes from [colorbrewer2](https://colorbrewer2.org/#type=sequential&scheme=BuGn&n=3). +[colorcet](https://github.com/holoviz/colorcet) - Collection of perceptually uniform colormaps. +[Named Colors Wheel](https://arantius.github.io/web-color-wheel/) - Color wheel for all named HTML colors. #### Dashboards -[dash](https://dash.plot.ly/gallery) - Dashboarding solution by plot.ly. Tutorial: [1](https://www.youtube.com/watch?v=J_Cy_QjG6NE), [2](https://www.youtube.com/watch?v=hRH01ZzT2NI), [3](https://www.youtube.com/watch?v=wv2MXJIdKRY), [4](https://www.youtube.com/watch?v=37Zj955LFT0), [5](https://www.youtube.com/watch?v=luixWRpp6Jo) -[bokeh](https://github.com/bokeh/bokeh) - Dashboarding solution. -[visdom](https://github.com/facebookresearch/visdom) - Dashboarding library by facebook. -[bowtie](https://github.com/jwkvam/bowtie/) - Dashboarding solution. +[py-shiny](https://github.com/rstudio/py-shiny) - Shiny for Python, [talk](https://www.youtube.com/watch?v=ijRBbtT2tgc). +[superset](https://github.com/apache/superset) - Dashboarding solution by Apache. +[streamlit](https://github.com/streamlit/streamlit) - Dashboarding solution. [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) - Convert Python notebook to web app, [Example](https://github.com/pplonski/dashboard-python-jupyter-notebook). +[dash](https://dash.plot.ly/gallery) - Dashboarding solution by plot.ly. [Resources](https://github.com/ucg8j/awesome-dash). +[visdom](https://github.com/facebookresearch/visdom) - Dashboarding library by Facebook. [panel](https://panel.pyviz.org/index.html) - Dashboarding solution. -[altair example](https://github.com/xhochy/altair-vue-vega-example) - [Video](https://www.youtube.com/watch?v=4L568emKOvs) +[altair example](https://github.com/xhochy/altair-vue-vega-example) - [Video](https://www.youtube.com/watch?v=4L568emKOvs). +[voila](https://github.com/QuantStack/voila) - Turn Jupyter notebooks into standalone web applications. +[voila-gridstack](https://github.com/voila-dashboards/voila-gridstack) - Voila grid layout. + +#### UI +[gradio](https://github.com/gradio-app/gradio) - Create UIs for your machine learning model. -#### Geopraphical Tools -[folium](https://github.com/python-visualization/folium) - Plot geographical maps using the Leaflet.js library. +#### Survey Tools +[samplics](https://github.com/samplics-org/samplics) - Sampling techniques for complex survey designs. + +#### Geographical Tools +[folium](https://github.com/python-visualization/folium) - Plot geographical maps using the Leaflet.js library, [jupyter plugin](https://github.com/jupyter-widgets/ipyleaflet). +[gmaps](https://github.com/pbugnion/gmaps) - Google Maps for Jupyter notebooks. [stadiamaps](https://stadiamaps.com/) - Plot geographical maps. [datashader](https://github.com/bokeh/datashader) - Draw millions of points on a map. -[sklearn](https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.BallTree.html) - BallTree, [Example](https://tech.minodes.com/experiments-with-in-memory-spatial-radius-queries-in-python-e40c9e66cf63). +[sklearn](https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.BallTree.html) - BallTree. [pynndescent](https://github.com/lmcinnes/pynndescent) - Nearest neighbor descent for approximate nearest neighbors. [geocoder](https://github.com/DenisCarriere/geocoder) - Geocoding of addresses, IP addresses. Conversion of different geo formats: [talk](https://www.youtube.com/watch?v=eHRggqAvczE), [repo](https://github.com/dillongardner/PyDataSpatialAnalysis) @@ -137,40 +375,44 @@ Low Level Geospatial Tools (GEOS, GDAL/OGR, PROJ.4) Vector Data (Shapely, Fiona, Pyproj) Raster Data (Rasterio) Plotting (Descartes, Catropy) -Predict economic indicators from Open Street Map [ipynb](https://github.com/njanakiev/osm-predict-economic-measurements/blob/master/osm-predict-economic-indicators.ipynb). +[Predict economic indicators from Open Street Map](https://janakiev.com/blog/osm-predict-economic-indicators/). +[PySal](https://github.com/pysal/pysal) - Python Spatial Analysis Library. +[geography](https://github.com/ushahidi/geograpy) - Extract countries, regions and cities from a URL or text. +[cartogram](https://go-cart.io/cartogram) - Distorted maps based on population. #### Recommender Systems Examples: [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) - Recommender, [talk](https://www.youtube.com/watch?v=d7iIb_XVkZs). -[turicreate](https://github.com/apple/turicreate) - Recommender. -[implicit](https://github.com/benfred/implicit) - Fast Python Collaborative Filtering for Implicit Feedback Datasets. +[implicit](https://github.com/benfred/implicit) - Fast Collaborative Filtering for Implicit Feedback Datasets. [spotlight](https://github.com/maciejkula/spotlight) - Deep recommender models using PyTorch. [lightfm](https://github.com/lyst/lightfm) - Recommendation algorithms for both implicit and explicit feedback. [funk-svd](https://github.com/gbolmier/funk-svd) - Fast SVD. -[pywFM](https://github.com/jfloff/pywFM) - Factorization. #### Decision Tree Models +[Intro to Decision Trees and Random Forests](https://victorzhou.com/blog/intro-to-random-forests/), Intro to Gradient Boosting [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) - Gradient boosting (GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, [doc](https://sites.google.com/view/lauraepp/parameters). [xgboost](https://github.com/dmlc/xgboost) - Gradient boosting (GBDT, GBRT or GBM) library, [doc](https://sites.google.com/view/lauraepp/parameters), Methods for CIs: [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) - Gradient boosting. -[thundergbm](https://github.com/Xtra-Computing/thundergbm) - GBDTs and Random Forest. -[h2o](https://github.com/h2oai/h2o-3) - Gradient boosting. +[h2o](https://github.com/h2oai/h2o-3) - Gradient boosting and general machine learning framework. +[pycaret](https://github.com/pycaret/pycaret) - Wrapper for xgboost, lightgbm, catboost etc. [forestci](https://github.com/scikit-learn-contrib/forest-confidence-interval) - Confidence intervals for random forests. -[scikit-garden](https://github.com/scikit-garden/scikit-garden) - Quantile Regression. [grf](https://github.com/grf-labs/grf) - Generalized random forest. [dtreeviz](https://github.com/parrt/dtreeviz) - Decision tree visualization and model interpretation. +[Nuance](https://github.com/SauceCat/Nuance) - Decision tree visualization. [rfpimp](https://github.com/parrt/random-forest-importances) - Feature Importance for RandomForests using Permuation Importance. Why the default feature importance for random forests is wrong: [link](http://explained.ai/rf-importance/index.html) -[treeinterpreter](https://github.com/andosa/treeinterpreter) - Interpreting scikit-learn's decision tree and random forest predictions. [bartpy](https://github.com/JakeColtman/bartpy) - Bayesian Additive Regression Trees. -[infiniteboost](https://github.com/arogozhnikov/infiniteboost) - Combination of RFs and GBDTs. [merf](https://github.com/manifoldai/merf) - Mixed Effects Random Forest for Clustering, [video](https://www.youtube.com/watch?v=gWj4ZwB7f3o) +[groot](https://github.com/tudelft-cda-lab/GROOT) - Robust decision trees. +[linear-tree](https://github.com/cerlymarco/linear-tree) - Trees with linear models at the leaves. +[supertree](https://github.com/mljar/supertree) - Decision tree visualization. #### 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/) - NLP, doc2vec, word2vec, text processing, topic modelling (LSA, LDA), [Example](https://markroxor.github.io/gensim/static/notebooks/gensim_news_classification.html), [Coherence Model](https://radimrehurek.com/gensim/models/coherencemodel.html) for evaluation. -Embeddings - [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/). +Embeddings - [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) - Vector embedding utility package. [pyldavis](https://github.com/bmabey/pyLDAvis) - Visualization for topic modelling. [spaCy](https://spacy.io/) - NLP. [NTLK](https://www.nltk.org/) - NLP, helpful `KMeansClusterer` with `cosine_distance`. @@ -178,75 +420,346 @@ Embeddings - [GloVe](https://nlp.stanford.edu/projects/glove/) ([[1](https://www [fastText](https://github.com/facebookresearch/fastText) - Efficient text classification and representation learning. [annoy](https://github.com/spotify/annoy) - Approximate nearest neighbor search. [faiss](https://github.com/facebookresearch/faiss) - Approximate nearest neighbor search. -[pysparnn](https://github.com/facebookresearch/pysparnn) - Approximate nearest neighbor search. -[infomap](https://github.com/mapequation/infomap) - Cluster (word-)vectors to find topics, [example](https://github.com/mapequation/infomap/blob/master/examples/python/infomap-examples.ipynb). +[infomap](https://github.com/mapequation/infomap) - Cluster (word-)vectors to find topics. [datasketch](https://github.com/ekzhu/datasketch) - Probabilistic data structures for large data (MinHash, HyperLogLog). [flair](https://github.com/zalandoresearch/flair) - NLP Framework by Zalando. -[stanfordnlp](https://github.com/stanfordnlp/stanfordnlp) - NLP Library. +[stanza](https://github.com/stanfordnlp/stanza) - NLP Library. +[Chatistics](https://github.com/MasterScrat/Chatistics) - Turn Messenger, Hangouts, WhatsApp and Telegram chat logs into DataFrames. +[textdistance](https://github.com/life4/textdistance) - Collection for comparing distances between two or more sequences. -##### Papers -[Search Engine Correlation](https://arxiv.org/pdf/1107.2691.pdf) +#### 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) + +##### Tutorials +[MIT 7.016 Introductory Biology, Fall 2018](https://www.youtube.com/playlist?list=PLUl4u3cNGP63LmSVIVzy584-ZbjbJ-Y63) - Videos 27, 28, and 29 talk about staining and imaging. +[Bio-image Analysis Notebooks](https://haesleinhuepf.github.io/BioImageAnalysisNotebooks/intro.html) - Large collection of image processing workflows, including [point-spread-function estimation](https://haesleinhuepf.github.io/BioImageAnalysisNotebooks/18a_deconvolution/extract_psf.html) and [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) using [pyclesperanto](https://github.com/clEsperanto/pyclesperanto_prototype) and others. +[python_for_microscopists](https://github.com/bnsreenu/python_for_microscopists) - Notebooks and associated [youtube channel](https://www.youtube.com/channel/UC34rW-HtPJulxr5wp2Xa04w/videos) for a variety of image processing tasks. + +##### Datasets +[jump-cellpainting](https://github.com/jump-cellpainting/datasets) - Cellpainting dataset. +[MedMNIST](https://github.com/MedMNIST/MedMNIST) - Datasets for 2D and 3D Biomedical Image Classification. +[CytoImageNet](https://github.com/stan-hua/CytoImageNet) - Huge diverse dataset like ImageNet but for cell images. +[Haghighi](https://github.com/carpenterlab/2021_Haghighi_NatureMethods) - Gene Expression and Morphology Profiles. +[broadinstitute/lincs-profiling-complementarity](https://github.com/broadinstitute/lincs-profiling-complementarity) - Cellpainting vs. L1000 assay. + +#### Biostatistics / Robust statistics +[MinCovDet](https://scikit-learn.org/stable/modules/generated/sklearn.covariance.MinCovDet.html) - Robust estimator of covariance, RMPV, [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) - Weighted average of z-scores based on Spearman correlation. +[winsorize](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.mstats.winsorize.html#scipy.stats.mstats.winsorize) - Simple adjustment of outliers. + +#### 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) - Measure of statistical effect size. +[Z'-factor](https://link.springer.com/referenceworkentry/10.1007/978-3-540-47648-1_6298) - Measure of statistical effect size. +[CV](https://en.wikipedia.org/wiki/Coefficient_of_variation) - Coefficient of variation. +[SSMD](https://en.wikipedia.org/wiki/Strictly_standardized_mean_difference) - Strictly standardized mean difference. +[Signal Window](https://www.intechopen.com/chapters/48130) - Assay quality measurement. -#### Image Processing -[cv2](https://github.com/skvark/opencv-python) - OpenCV, classical algorithms: [Gaussian Filter](https://docs.opencv.org/3.1.0/d4/d13/tutorial_py_filtering.html), [Morphological Transformations](https://docs.opencv.org/3.0-beta/doc/py_tutorials/py_imgproc/py_morphological_ops/py_morphological_ops.html). +#### Microscopy + Assay +[BD Spectrum Viewer](https://www.bdbiosciences.com/en-us/resources/bd-spectrum-viewer) - Calculate spectral overlap, bleed through for fluorescence microscopy dyes. +[SpectraViewer](https://www.perkinelmer.com/lab-products-and-services/spectraviewer) - Visualize the spectral compatibility of fluorophores (PerkinElmer). +[Thermofisher Spectrum Viewer](https://www.thermofisher.com/order/stain-it) - Thermofisher Spectrum Viewer. +[Microscopy Resolution Calculator](https://www.microscope.healthcare.nikon.com/microtools/resolution-calculator) - Calculate resolution of images (Nikon). +[PlateEditor](https://github.com/vindelorme/PlateEditor) - Drug Layout for plates, [app](https://plateeditor.sourceforge.io/), [zip](https://sourceforge.net/projects/plateeditor/), [paper](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0252488). + +##### Image Formats and Converters +OME-Zarr - [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) - Various formats to zarr. +[raw2ometiff](https://github.com/glencoesoftware/raw2ometiff) - Zarr to tiff. +[BatchConvert](https://github.com/Euro-BioImaging/BatchConvert) - Wrapper for bioformats2raw to parallelize conversions with nextflow, [video](https://www.youtube.com/watch?v=DeCWV274l0c). +REMBI model - Recommended Metadata for Biological Images, BioImage Archive: [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) + +##### Matrix Formats +[anndata](https://github.com/scverse/anndata) - annotated data matrices in memory and on disk, [Docs](https://anndata.readthedocs.io/en/latest/index.html). +[muon](https://github.com/scverse/muon) - Multimodal omics framework. +[mudata](https://github.com/scverse/mudata) - Multimodal Data (.h5mu) implementation. +[bdz](https://github.com/openssbd/bdz) - Zarr-based format for storing quantitative biological dynamics data. + +#### Image Viewers +[napari](https://github.com/napari/napari) - Image viewer and image processing tool. +[Fiji](https://fiji.sc/) - General purpose tool. Image viewer and image processing tool. +[vizarr](https://github.com/hms-dbmi/vizarr) - Browser-based image viewer for zarr format. +[avivator](https://github.com/hms-dbmi/viv) - Browser-based image viewer for tiff files. +[OMERO](https://www.openmicroscopy.org/omero/) - Image viewer for high-content screening. [IDR](https://idr.openmicroscopy.org/) uses OMERO. [Intro](https://www.youtube.com/watch?v=nSCrMO_c-5s) +[fiftyone](https://github.com/voxel51/fiftyone) - Viewer and tool for building high-quality datasets and computer vision models. +Image Data Explorer - Microscopy Image Viewer, [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) - Microscopy Image Viewer, [Doc](https://imswitch.readthedocs.io/en/stable/gui.html), [Video](https://www.youtube.com/watch?v=XsbnMkGSPQQ). +[pixmi](https://github.com/piximi/piximi) - Web-based image annotation and classification tool, [App](https://www.piximi.app/). +[DeepCell Label](https://label.deepcell.org/) - Data labeling tool to segment images, [Video](https://www.youtube.com/watch?v=zfsvUBkEeow). + +#### Napari Plugins +[napari-sam](https://github.com/MIC-DKFZ/napari-sam) - Segment Anything Plugin. +[napari-chatgpt](https://github.com/royerlab/napari-chatgpt) - ChatGPT Plugin. + +##### Image Restoration and Denoising +[aydin](https://github.com/royerlab/aydin) - Image denoising. +[DivNoising](https://github.com/juglab/DivNoising) - Unsupervised denoising method. +[CSBDeep](https://github.com/CSBDeep/CSBDeep) - Content-aware image restoration, [Project page](https://csbdeep.bioimagecomputing.com/tools/). +[gibbs-diffusion](https://github.com/rubenohana/gibbs-diffusion) - Image denoising. + +##### Illumination correction +[skimage](https://scikit-image.org/docs/dev/api/skimage.exposure.html#skimage.exposure.equalize_adapthist) - Illumination correction (CLAHE). +[cidre](https://github.com/smithk/cidre) - Illumination correction method for optical microscopy. +[BaSiCPy](https://github.com/peng-lab/BaSiCPy) - Background and Shading Correction of Optical Microscopy Images, [BaSiC](https://github.com/marrlab/BaSiC). + +##### Bleedthrough correction / Spectral Unmixing +[PICASSO](https://github.com/nygctech/PICASSO) - Blind unmixing without reference spectra measurement, [Paper](https://www.biorxiv.org/content/10.1101/2021.01.27.428247v1.full) +[cytoflow](https://github.com/cytoflow/cytoflow) - Flow cytometry. Includes Bleedthrough correction methods. +Linear unmixing in Fiji for Bleedthrough Correction - [Youtube](https://www.youtube.com/watch?v=W90qs0J29v8). +Bleedthrough Correction using Lumos and Fiji - [Link](https://imagej.net/plugins/lumos-spectral-unmixing). +AutoUnmix - [Link](https://www.biorxiv.org/content/10.1101/2023.05.30.542836v1.full). + +##### Platforms and Pipelines +[CellProfiler](https://github.com/CellProfiler/CellProfiler), [CellProfilerAnalyst](https://github.com/CellProfiler/CellProfiler-Analyst) - Create image analysis pipelines. +[fractal](https://fractal-analytics-platform.github.io/) - Framework to process high-content imaging data from UZH, [Github](https://github.com/fractal-analytics-platform). +[atomai](https://github.com/pycroscopy/atomai) - Deep and Machine Learning for Microscopy. +[py-clesperanto](https://github.com/clesperanto/pyclesperanto_prototype/) - Tools for 3D microscopy analysis, [deskewing](https://github.com/clEsperanto/pyclesperanto_prototype/blob/master/demo/transforms/deskew.ipynb) and lots of other tutorials, interacts with napari. +[qupath](https://github.com/qupath/qupath) - Image analysis. + +##### Microscopy Pipelines +Labsyspharm Stack see below. +[BiaPy](https://github.com/danifranco/BiaPy) - Bioimage analysis pipelines, [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) - Image processing pipeline on top of Dask. +[DeepCell Kiosk](https://github.com/vanvalenlab/kiosk-console/tree/master) - Image analysis platform. +[IMCWorkflow](https://github.com/BodenmillerGroup/IMCWorkflow/) - Image analysis pipeline using [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/). + +##### Labsyspharm +[mcmicro](https://github.com/labsyspharm/mcmicro) - Multiple-choice microscopy pipeline, [Website](https://mcmicro.org/overview/), [Paper](https://www.nature.com/articles/s41592-021-01308-y). +[MCQuant](https://github.com/labsyspharm/quantification) - Quantification of cell features. +[cylinter](https://github.com/labsyspharm/cylinter) - Quality assurance for microscopy images, [Website](https://labsyspharm.github.io/cylinter/). +[ashlar](https://github.com/labsyspharm/ashlar) - Whole-slide microscopy image stitching and registration. +[scimap](https://github.com/labsyspharm/scimap) - Spatial Single-Cell Analysis Toolkit. + +##### Cell Segmentation +[microscopy-tree](https://biomag-lab.github.io/microscopy-tree/) - Review of cell segmentation algorithms, [Paper](https://www.sciencedirect.com/science/article/abs/pii/S0962892421002518). +Review of organoid pipelines - [Paper](https://arxiv.org/ftp/arxiv/papers/2301/2301.02341.pdf). +[BioImage.IO](https://bioimage.io/#/) - BioImage Model Zoo. +[MEDIAR](https://github.com/Lee-Gihun/MEDIAR) - Cell segmentation. +[cellpose](https://github.com/mouseland/cellpose) - Cell segmentation. [Paper](https://www.biorxiv.org/content/10.1101/2020.02.02.931238v1), [Dataset](https://www.cellpose.org/dataset). +[stardist](https://github.com/stardist/stardist) - Cell segmentation with Star-convex Shapes. +[instanseg](https://github.com/instanseg/instanseg) - Cell segmentation. +[UnMicst](https://github.com/HMS-IDAC/UnMicst) - Identifying Cells and Segmenting Tissue. +[ilastik](https://github.com/ilastik/ilastik) - Segment, classify, track and count cells. [ImageJ Plugin](https://github.com/ilastik/ilastik4ij). +[nnUnet](https://github.com/MIC-DKFZ/nnUNet) - 3D biomedical image segmentation. +[allencell](https://www.allencell.org/segmenter.html) - Tools for 3D segmentation, classical and deep learning methods. +[Cell-ACDC](https://github.com/SchmollerLab/Cell_ACDC) - Python GUI for cell segmentation and tracking. +[ZeroCostDL4Mic](https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki) - Deep-Learning in Microscopy. +[DL4MicEverywhere](https://github.com/HenriquesLab/DL4MicEverywhere) - Bringing the ZeroCostDL4Mic experience using Docker. +[EmbedSeg](https://github.com/juglab/EmbedSeg) - Embedding-based Instance Segmentation. +[segment-anything](https://github.com/facebookresearch/segment-anything) - Segment Anything (SAM) from Facebook. +[micro-sam](https://github.com/computational-cell-analytics/micro-sam) - Segment Anything for Microscopy. +[Segment-Everything-Everywhere-All-At-Once](https://github.com/UX-Decoder/Segment-Everything-Everywhere-All-At-Once) - Segment Everything Everywhere All at Once from Microsoft. +[deepcell-tf](https://github.com/vanvalenlab/deepcell-tf/tree/master) - Cell segmentation, [DeepCell](https://deepcell.org/). +[labkit](https://github.com/juglab/labkit-ui) - Fiji plugin for image segmentation. +[MedImageInsight](https://arxiv.org/abs/2410.06542) - Embedding Model for General Domain Medical Imaging. +[CHIEF](https://github.com/hms-dbmi/CHIEF) - Clinical Histopathology Imaging Evaluation Foundation Model. + +##### Cell Segmentation Datasets +[cellpose](https://www.cellpose.org/dataset) - Cell images. +[omnipose](http://www.cellpose.org/dataset_omnipose) - Cell images. +[LIVECell](https://github.com/sartorius-research/LIVECell) - Cell images. +[Sartorius](https://www.kaggle.com/competitions/sartorius-cell-instance-segmentation/overview) - Neurons. +[EmbedSeg](https://github.com/juglab/EmbedSeg/releases/tag/v0.1.0) - 2D + 3D images. +[connectomics](https://sites.google.com/view/connectomics/) - Annotation of the EPFL Hippocampus dataset. +[ZeroCostDL4Mic](https://www.ebi.ac.uk/biostudies/BioImages/studies/S-BIAD895) - Stardist example training and test dataset. + +##### Evaluation +[seg-eval](https://github.com/lstrgar/seg-eval) - Cell segmentation performance evaluation without Ground Truth labels, [Paper](https://www.biorxiv.org/content/10.1101/2023.02.23.529809v1.full.pdf). + +##### Feature Engineering Images +[Computer vision challenges in drug discovery - Maciej Hermanowicz](https://www.youtube.com/watch?v=Y5GJmnIhvFk) +[CellProfiler](https://github.com/CellProfiler/CellProfiler) - Biological image analysis. [scikit-image](https://github.com/scikit-image/scikit-image) - Image processing. -[mahotas](http://luispedro.org/software/mahotas/) - Image processing (Bioinformatics), [example](https://github.com/luispedro/python-image-tutorial/blob/master/Segmenting%20cell%20images%20(fluorescent%20microscopy).ipynb). +[scikit-image regionprops](https://scikit-image.org/docs/dev/api/skimage.measure.html#skimage.measure.regionprops) - Regionprops: area, eccentricity, extent. +[mahotas](https://github.com/luispedro/mahotas) - Zernike, Haralick, LBP, and TAS features, [example](https://github.com/luispedro/python-image-tutorial/blob/master/Segmenting%20cell%20images%20(fluorescent%20microscopy).ipynb). +[pyradiomics](https://github.com/AIM-Harvard/pyradiomics) - Radiomics features from medical imaging. +[pyefd](https://github.com/hbldh/pyefd) - Elliptical feature descriptor, approximating a contour with a Fourier series. +[pyvips](https://github.com/libvips/pyvips/tree/master) - Faster image processing operations. + +#### 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) - Fuzzy k-means and locally linear adjustments. +[pyliger](https://github.com/welch-lab/pyliger) - Batch-effect correction, [R package](https://github.com/welch-lab/liger). +[nimfa](https://github.com/mims-harvard/nimfa) - Nonnegative matrix factorization. +[scgen](https://github.com/theislab/scgen) - Batch removal. [Doc](https://scgen.readthedocs.io/en/stable/). +[CORAL](https://github.com/google-research/google-research/tree/30e54523f08d963ced3fbb37c00e9225579d2e1d/correct_batch_effects_wdn) - Correcting for Batch Effects Using Wasserstein Distance, [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) - Awesome Domain Adaptation Python Toolbox. +[pytorch-adapt](https://github.com/KevinMusgrave/pytorch-adapt) - Various neural network models for domain adaptation. -#### Neural Networks +##### Sequencing +[Single cell tutorial](https://github.com/theislab/single-cell-tutorial). +[PyDESeq2](https://github.com/owkin/PyDESeq2) - Analyzing RNA-seq data. +[cellxgene](https://github.com/chanzuckerberg/cellxgene) - Interactive explorer for single-cell transcriptomics data. +[scanpy](https://github.com/theislab/scanpy) - Analyze single-cell gene expression data, [tutorial](https://github.com/theislab/single-cell-tutorial). +[besca](https://github.com/bedapub/besca) - Beyond single-cell analysis. +[janggu](https://github.com/BIMSBbioinfo/janggu) - Deep Learning for Genomics. +[gdsctools](https://github.com/CancerRxGene/gdsctools) - Drug responses in the context of the Genomics of Drug Sensitivity in Cancer project, ANOVA, IC50, MoBEM, [doc](https://gdsctools.readthedocs.io/en/master/). +[monkeybread](https://github.com/immunitastx/monkeybread) - Analysis of single-cell spatial transcriptomics data. -##### Reading -[Convolutional Neural Networks for Visual Recognition](https://cs231n.github.io/) -[Cell Segmentation Talk](https://www.youtube.com/watch?v=dVFZpodqJiI) -[Cell Segmentation Blog Post](https://www.thomasjpfan.com/2018/07/nuclei-image-segmentation-tutorial/) [2](https://www.thomasjpfan.com/2017/08/hassle-free-unets/) -[Deep Learning Book](http://www.deeplearningbook.org/) -[Tutorial](https://github.com/lexfridman/mit-deep-learning) +##### Drug discovery +[TDC](https://github.com/mims-harvard/TDC/tree/main) - Drug Discovery and Development. +[DeepPurpose](https://github.com/kexinhuang12345/DeepPurpose) - Deep Learning Based Molecular Modelling and Prediction Toolkit. + +#### Neural Networks +[mit6874](https://mit6874.github.io/) - Computational Systems Biology: Deep Learning in the Life Sciences. +[ConvNet Shape Calculator](https://madebyollin.github.io/convnet-calculator/) - Calculate output dimensions of Conv2D layer. +[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). + +##### Tutorials & Viewer +[Google Tuning Playbook](https://github.com/google-research/tuning_playbook) - A playbook for systematically maximizing the performance of deep learning models by Google. +[fast.ai course](https://course.fast.ai/) - Practical Deep Learning for Coders. +[Tensorflow without a PhD](https://github.com/GoogleCloudPlatform/tensorflow-without-a-phd) - Neural Network course by Google. Feature Visualization: [Blog](https://distill.pub/2017/feature-visualization/), [PPT](http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture12.pdf) -[Talk](https://www.youtube.com/watch?v=EK61htlw8hY): Extracting knowledge of big NNs to smaller NNs -[Visualization of optimization algorithms](https://vis.ensmallen.org/) +[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) - Image Viewer. ##### Image Related -[keras preprocessing](https://keras.io/preprocessing/image/) - Preprocess images. [imgaug](https://github.com/aleju/imgaug) - More sophisticated image preprocessing. -[imgaug_extension](https://github.com/cadenai/imgaug_extension) - Extension for imgaug. -[albumentations](https://github.com/albu/albumentations) - Wrapper around imgaug and other libraries. [Augmentor](https://github.com/mdbloice/Augmentor) - Image augmentation library. -[tcav](https://github.com/tensorflow/tcav) - Interpretability method. -[cutouts-explorer](https://github.com/mgckind/cutouts-explorer) - Image Viewer. +[keras preprocessing](https://keras.io/preprocessing/image/) - Preprocess images. +[albumentations](https://github.com/albu/albumentations) - Wrapper around imgaug and other libraries. +[augmix](https://github.com/google-research/augmix) - Image augmentation from Google. +[kornia](https://github.com/kornia/kornia) - Image augmentation, feature extraction and loss functions. +[augly](https://github.com/facebookresearch/AugLy) - Image, audio, text, video augmentation from Facebook. +[pyvips](https://github.com/libvips/pyvips/tree/master) - Faster image processing operations. + +##### Lossfunction Related +[SegLoss](https://github.com/JunMa11/SegLoss) - List of loss functions for medical image segmentation. -#### Text Related -[ktext](https://github.com/hamelsmu/ktext) - Utilities for pre-processing text for deep learning in Keras. +##### Activation Functions +[rational_activations](https://github.com/ml-research/rational_activations) - Rational activation functions. + +##### Text Related +[ktext](https://github.com/hamelsmu/ktext) - Utilities for pre-processing text for deep learning in Keras. [textgenrnn](https://github.com/minimaxir/textgenrnn) - Ready-to-use LSTM for text generation. +[ctrl](https://github.com/salesforce/ctrl) - Text generation. + +##### Neural network and deep learning frameworks +[OpenMMLab](https://github.com/open-mmlab) - Framework for segmentation, classification and lots of other computer vision tasks. +[caffe](https://github.com/BVLC/caffe) - Deep learning framework, [pretrained models](https://github.com/BVLC/caffe/wiki/Model-Zoo). +[mxnet](https://github.com/apache/incubator-mxnet) - Deep learning framework, [book](https://d2l.ai/index.html). -##### Libs -[keras](https://keras.io/) - Neural Networks on top of [tensorflow](https://www.tensorflow.org/). +##### Libs General +[keras](https://keras.io/) - Neural Networks on top of [tensorflow](https://www.tensorflow.org/), [examples](https://gist.github.com/candlewill/552fa102352ccce42fd829ae26277d24). [keras-contrib](https://github.com/keras-team/keras-contrib) - Keras community contributions. +[keras-tuner](https://github.com/keras-team/keras-tuner) - Hyperparameter tuning for Keras. [hyperas](https://github.com/maxpumperla/hyperas) - Keras + Hyperopt: Convenient hyperparameter optimization wrapper. [elephas](https://github.com/maxpumperla/elephas) - Distributed Deep learning with Keras & Spark. -[tflearn](https://github.com/tflearn/tflearn) - Neural Networks on top of tensorflow. -[tensorlayer](https://github.com/tensorlayer/tensorlayer) - Neural Networks on top of tensorflow, [tricks](https://github.com/wagamamaz/tensorlayer-tricks). -[tensorforce](https://github.com/reinforceio/tensorforce) - Tensorflow for applied reinforcement learning. -[fastai](https://github.com/fastai/fastai) - Neural Networks in pytorch. -[ignite](https://github.com/pytorch/ignite) - Highlevel library for pytorch. -[skorch](https://github.com/dnouri/skorch) - Scikit-learn compatible neural network library that wraps pytorch. -[Detectron](https://github.com/facebookresearch/Detectron) - Object Detection by Facebook. +[tflearn](https://github.com/tflearn/tflearn) - Neural Networks on top of TensorFlow. +[tensorlayer](https://github.com/tensorlayer/tensorlayer) - Neural Networks on top of TensorFlow, [tricks](https://github.com/wagamamaz/tensorlayer-tricks). +[tensorforce](https://github.com/reinforceio/tensorforce) - TensorFlow for applied reinforcement learning. [autokeras](https://github.com/jhfjhfj1/autokeras) - AutoML for deep learning. -[simpledet](https://github.com/TuSimple/simpledet) - Object Detection and Instance Recognition. [PlotNeuralNet](https://github.com/HarisIqbal88/PlotNeuralNet) - Plot neural networks. [lucid](https://github.com/tensorflow/lucid) - Neural network interpretability, [Activation Maps](https://openai.com/blog/introducing-activation-atlases/). -[AdaBound](https://github.com/Luolc/AdaBound) - Optimizer that trains as fast as Adam and as good as SGD. -[caffe](https://github.com/BVLC/caffe) - Deep learning framework, [pretrained models](https://github.com/BVLC/caffe/wiki/Model-Zoo). +[tcav](https://github.com/tensorflow/tcav) - Interpretability method. +[AdaBound](https://github.com/Luolc/AdaBound) - Optimizer that trains as fast as Adam and as good as SGD, [alt](https://github.com/titu1994/keras-adabound). [foolbox](https://github.com/bethgelab/foolbox) - Adversarial examples that fool neural networks. [hiddenlayer](https://github.com/waleedka/hiddenlayer) - Training metrics. [imgclsmob](https://github.com/osmr/imgclsmob) - Pretrained models. +[netron](https://github.com/lutzroeder/netron) - Visualizer for deep learning and machine learning models. +[ffcv](https://github.com/libffcv/ffcv) - Fast dataloader. + +##### Libs PyTorch +[Good PyTorch Introduction](https://cs230.stanford.edu/blog/pytorch/) +[skorch](https://github.com/dnouri/skorch) - Scikit-learn compatible neural network library that wraps PyTorch, [talk](https://www.youtube.com/watch?v=0J7FaLk0bmQ), [slides](https://github.com/thomasjpfan/skorch_talk). +[fastai](https://github.com/fastai/fastai) - Neural Networks in PyTorch. +[timm](https://github.com/rwightman/pytorch-image-models) - PyTorch image models. +[ignite](https://github.com/pytorch/ignite) - Highlevel library for PyTorch. +[torchcv](https://github.com/donnyyou/torchcv) - Deep Learning in Computer Vision. +[pytorch-optimizer](https://github.com/jettify/pytorch-optimizer) - Collection of optimizers for PyTorch. +[pytorch-lightning](https://github.com/PyTorchLightning/PyTorch-lightning) - Wrapper around PyTorch. +[litserve](https://github.com/Lightning-AI/LitServe) - Serve models. +[lightly](https://github.com/lightly-ai/lightly) - MoCo, SimCLR, SimSiam, Barlow Twins, BYOL, NNCLR. +[MONAI](https://github.com/project-monai/monai) - Deep learning in healthcare imaging. +[kornia](https://github.com/kornia/kornia) - Image transformations, epipolar geometry, depth estimation. +[torchinfo](https://github.com/Tylep/torchinfo) - Nice model summary. +[lovely-tensors](https://github.com/xl0/lovely-tensors/) - Inspect tensors, mean, std, inf values. + +##### Distributed Libs +[flexflow](https://github.com/flexflow/FlexFlow) - Distributed TensorFlow Keras and PyTorch. +[horovod](https://github.com/horovod/horovod) - Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet. -##### Snippets -[Simple Keras models](https://gist.github.com/candlewill/552fa102352ccce42fd829ae26277d24) -[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) +##### Architecture Visualization +[Awesome List](https://github.com/ashishpatel26/Tools-to-Design-or-Visualize-Architecture-of-Neural-Network). +[netron](https://github.com/lutzroeder/netron) - Viewer for neural networks. +[visualkeras](https://github.com/paulgavrikov/visualkeras) - Visualize Keras networks. + +##### Object detection / Instance Segmentation +[Metrics reloaded: Recommendations for image analysis validation](https://arxiv.org/abs/2206.01653) - Guide for choosing correct image analysis metrics, [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) - Easily accessible Yolo and SAM models. +[yolact](https://github.com/dbolya/yolact) - Fully convolutional model for real-time instance segmentation. +[EfficientDet Pytorch](https://github.com/toandaominh1997/EfficientDet.Pytorch), [EfficientDet Keras](https://github.com/xuannianz/EfficientDet) - Scalable and Efficient Object Detection. +[detectron2](https://github.com/facebookresearch/detectron2) - Object Detection (Mask R-CNN) by Facebook. +[simpledet](https://github.com/TuSimple/simpledet) - Object Detection and Instance Recognition. +[CenterNet](https://github.com/xingyizhou/CenterNet) - Object detection. +[FCOS](https://github.com/tianzhi0549/FCOS) - Fully Convolutional One-Stage Object Detection. +[norfair](https://github.com/tryolabs/norfair) - Real-time 2D object tracking. +[Detic](https://github.com/facebookresearch/Detic) - Detector with image classes that can use image-level labels (facebookresearch). +[EasyCV](https://github.com/alibaba/EasyCV) - Image segmentation, classification, metric-learning, object detection, pose estimation. + +##### Image Classification +[nfnets](https://github.com/ypeleg/nfnets-keras) - Neural network. +[efficientnet](https://github.com/lukemelas/EfficientNet-PyTorch) - Neural network. +[pycls](https://github.com/facebookresearch/pycls) - PyTorch image classification networks: ResNet, ResNeXt, EfficientNet, and RegNet (by Facebook). + +##### Applications and Snippets +[SPADE](https://github.com/nvlabs/spade) - Semantic Image Synthesis. +[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) - Super-scaling using a Residual Dense Network. +Cell Segmentation - [Talk](https://www.youtube.com/watch?v=dVFZpodqJiI), Blog Posts: [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) - Deep learning models. + +##### Variational Autoencoders (VAEs) +[Variational Autoencoder Explanation Video](https://www.youtube.com/watch?v=9zKuYvjFFS8) +[disentanglement_lib](https://github.com/google-research/disentanglement_lib) - BetaVAE, FactorVAE, BetaTCVAE, DIP-VAE. +[ladder-vae-pytorch](https://github.com/addtt/ladder-vae-pytorch) - Ladder Variational Autoencoders (LVAE). +[benchmark_VAE](https://github.com/clementchadebec/benchmark_VAE) - Unifying Generative Autoencoder implementations. + +##### Generative Adversarial Networks (GANs) +[Awesome GAN Applications](https://github.com/nashory/gans-awesome-applications) +[The GAN Zoo](https://github.com/hindupuravinash/the-gan-zoo) - List of Generative Adversarial Networks. +[CycleGAN and Pix2pix](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix) - Various image-to-image tasks. +[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) - PyTorch GAN implementations. + +##### Transformers +[The Annotated Transformer](https://nlp.seas.harvard.edu/annotated-transformer/) - Intro to transformers. +[Transformers from Scratch](https://e2eml.school/transformers.html) - Intro. +[Neural Networks: Zero to Hero](https://karpathy.ai/zero-to-hero.html) - Video series on building neural networks. +[SegFormer](https://github.com/NVlabs/SegFormer) - Simple and Efficient Design for Semantic Segmentation with Transformers. +[esvit](https://github.com/microsoft/esvit) - Efficient self-supervised Vision Transformers. +[nystromformer](https://github.com/Rishit-dagli/Nystromformer) - More efficient transformer because of approximate self-attention. + +##### Deep learning on structured data +[Great overview for deep learning for tabular data](https://sebastianraschka.com/blog/2022/deep-learning-for-tabular-data.html) + +##### 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/) - Open Graph Benchmark, Benchmark datasets. +[networkx](https://github.com/networkx/networkx) - Graph library. +[cugraph](https://github.com/rapidsai/cugraph) - RAPIDS, Graph library on the GPU. +[pytorch-geometric](https://github.com/rusty1s/pytorch_geometric) - Various methods for deep learning on graphs. +[dgl](https://github.com/dmlc/dgl) - Deep Graph Library. +[graph_nets](https://github.com/deepmind/graph_nets) - Build graph networks in TensorFlow, by DeepMind. + +#### Model conversion +[hummingbird](https://github.com/microsoft/hummingbird) - Compile trained ML models into tensor computations (by Microsoft). #### GPU -[cuML](https://github.com/rapidsai/cuml) - Run traditional tabular ML tasks on GPUs. +[cuML](https://github.com/rapidsai/cuml) - RAPIDS, Run traditional tabular ML tasks on GPUs, [Intro](https://www.youtube.com/watch?v=6XzS5XcpicM&t=2m50s). [thundergbm](https://github.com/Xtra-Computing/thundergbm) - GBDTs and Random Forest. [thundersvm](https://github.com/Xtra-Computing/thundersvm) - Support Vector Machines. +Legate Numpy - Distributed Numpy array multiple using GPUs by Nvidia (not released yet) [video](https://www.youtube.com/watch?v=Jxxs_moibog). #### Regression Understanding SVM Regression: [slides](https://cs.adelaide.edu.au/~chhshen/teaching/ML_SVR.pdf), [forum](https://www.quora.com/How-does-support-vector-regression-work), [paper](http://alex.smola.org/papers/2003/SmoSch03b.pdf) @@ -254,70 +767,196 @@ Understanding SVM Regression: [slides](https://cs.adelaide.edu.au/~chhshen/teach [pyearth](https://github.com/scikit-learn-contrib/py-earth) - Multivariate Adaptive Regression Splines (MARS), [tutorial](https://uc-r.github.io/mars). [pygam](https://github.com/dswah/pyGAM) - Generalized Additive Models (GAMs), [Explanation](https://multithreaded.stitchfix.com/blog/2015/07/30/gam/). [GLRM](https://github.com/madeleineudell/LowRankModels.jl) - Generalized Low Rank Models. +[tweedie](https://xgboost.readthedocs.io/en/latest/parameter.html#parameters-for-tweedie-regression-objective-reg-tweedie) - Specialized distribution for zero inflated targets, [Talk](https://www.youtube.com/watch?v=-o0lpHBq85I). +[MAPIE](https://github.com/scikit-learn-contrib/MAPIE) - Estimating prediction intervals. + +#### Polynomials +[orthopy](https://github.com/nschloe/orthopy) - Orthogonal polynomials in all shapes and sizes. #### 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) - Dynamic classifier and ensemble selection +[DESlib](https://github.com/scikit-learn-contrib/DESlib) - Dynamic classifier and ensemble selection. +[human-learn](https://github.com/koaning/human-learn) - Create and tune classifier based on your rule set. + +#### 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) - Supervised and weakly-supervised metric learning algorithms. +[pytorch-metric-learning](https://github.com/KevinMusgrave/pytorch-metric-learning) - PyTorch metric learning. +[deep_metric_learning](https://github.com/ronekko/deep_metric_learning) - Methods for deep metric learning. +[ivis](https://bering-ivis.readthedocs.io/en/latest/supervised.html) - Metric learning using siamese neural networks. +[TensorFlow similarity](https://github.com/tensorflow/similarity) - Metric learning. + +#### 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) - All kinds of distance metrics. +[vegdist](https://rdrr.io/cran/vegan/man/vegdist.html) - Distance metrics (R package). +[pyemd](https://github.com/wmayner/pyemd) - Earth Mover's Distance / Wasserstein distance, similarity between histograms. [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) - Distance correlation and related Energy statistics. +[GeomLoss](https://www.kernel-operations.io/geomloss/) - Kernel norms, Hausdorff divergences, Debiased Sinkhorn divergences (=approximation of Wasserstein distance). + +#### Self-supervised Learning +[lightly](https://github.com/lightly-ai/lightly) - MoCo, SimCLR, SimSiam, Barlow Twins, BYOL, NNCLR. +[vissl](https://github.com/facebookresearch/vissl) - Self-Supervised Learning with PyTorch: RotNet, Jigsaw, NPID, ClusterFit, PIRL, SimCLR, MoCo, DeepCluster, SwAV. #### 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) - Dendrogram, Tanglegram +[hdbscan](https://github.com/scikit-learn-contrib/hdbscan) - Clustering algorithm, [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) - All sorts of clustering algorithms. -[somoclu](https://github.com/peterwittek/somoclu) - Self-organizing map. -[hdbscan](https://github.com/scikit-learn-contrib/hdbscan) - Clustering algorithm. +[FCPS](https://github.com/Mthrun/FCPS) - Fundamental Clustering Problems Suite (R package). +[GaussianMixture](https://scikit-learn.org/stable/modules/generated/sklearn.mixture.GaussianMixture.html) - Generalized k-means clustering using a mixture of Gaussian distributions, [video](https://www.youtube.com/watch?v=aICqoAG5BXQ). [nmslib](https://github.com/nmslib/nmslib) - Similarity search library and toolkit for evaluation of k-NN methods. -[buckshotpp](https://github.com/zjohn77/buckshotpp) - Outlier-resistant and scalable clustering algorithm. [merf](https://github.com/manifoldai/merf) - Mixed Effects Random Forest for Clustering, [video](https://www.youtube.com/watch?v=gWj4ZwB7f3o) +[tree-SNE](https://github.com/isaacrob/treesne) - Hierarchical clustering algorithm based on t-SNE. +[MiniSom](https://github.com/JustGlowing/minisom) - Pure Python implementation of the Self Organizing Maps. +[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) - Clustering by community detection. +[FastPG](https://github.com/sararselitsky/FastPG) - Clustering of single cell data (RNA). Improvement of phenograph, [Paper](https://www.researchgate.net/publication/342339899_FastPG_Fast_clustering_of_millions_of_single_cells). +[HypHC](https://github.com/HazyResearch/HypHC) - Hyperbolic Hierarchical Clustering. +[BanditPAM](https://github.com/ThrunGroup/BanditPAM) - Improved k-Medoids Clustering. +[dendextend](https://github.com/talgalili/dendextend) - Comparing dendrograms (R package). +[DeepDPM](https://github.com/BGU-CS-VIL/DeepDPM) - Deep Clustering With An Unknown Number of Clusters. -#### Interpretable Classifiers and Regressors -[skope-rules](https://github.com/scikit-learn-contrib/skope-rules) - Interpretable classifier, IF-THEN rules. -[sklearn-expertsys](https://github.com/tmadl/sklearn-expertsys) - Interpretable classifiers, Bayesian Rule List classifier. +##### 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) - The similarity of two sets of biclusters. + +[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) - Various methods for clustering and cluster validation (R package). +* Minimum distance between any two clusters +* Distance between centroids +* p-separation index: Like minimum distance. Look at the average distance to nearest point in different cluster for p=10% "border" points in any cluster. Measuring density, measuring mountains vs valleys +* Estimate density by weighted count of close points + +Other measures: +* Within-cluster average distance +* Mean of within-cluster average distance over nearest-cluster average distance (silhouette score) +* Within-cluster similarity measure to normal/uniform +* Within-cluster (squared) distance to centroid (this is the k-Means loss function) +* Correlation coefficient between distance we originally had to the distance the are induced by the clustering (Huberts Gamma) +* Entropy of cluster sizes +* Average largest within-cluster gap +* Variation of clusterings on bootstrapped data #### Multi-label classification [scikit-multilearn](https://github.com/scikit-multilearn/scikit-multilearn) - Multi-label classification, [talk](https://www.youtube.com/watch?v=m-tAASQA7XQ&t=18m57s). +#### 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) + +#### 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) - Chapter 3 has good introduction to Bessel, Butterworth and Chebyshev filters. +[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) - Focuses on intuition using Jupyter Notebooks. Includes Bayesian and various Kalman filters. +[Interactive Tool](https://fiiir.com/) for FIR and IIR filters, [Examples](https://plot.ly/python/fft-filters/). +[filterpy](https://github.com/rlabbe/filterpy) - Kalman filtering and optimal estimation library. + +#### 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) - Choose appropriate `win_type`. + +#### Geometry +[geomstats](https://github.com/geomstats/geomstats) - Computations and statistics on manifolds with geometric structures. + #### Time Series -[Signal Processing Book](https://www.analog.com/en/education/education-library/scientist_engineers_guide.html) -Filter Design: [Article](https://tomroelandts.com/articles/how-to-create-a-simple-high-pass-filter), [Interactive Tool](https://fiiir.com/), [Filter examples](https://plot.ly/python/fft-filters/) -[Talk](https://www.youtube.com/watch?v=0zpg9ODE6Ww) +[Time Series Anomaly Detection Review Paper](https://arxiv.org/abs/2412.20512) [statsmodels](https://www.statsmodels.org/dev/tsa.html) - Time series analysis, [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) - Time Series library (LightGBM, Neural Networks). +[kats](https://github.com/facebookresearch/kats) - Time series prediction library by Facebook. +[prophet](https://github.com/facebook/prophet) - Time series prediction library by Facebook. +[neural_prophet](https://github.com/ourownstory/neural_prophet) - Time series prediction built on PyTorch. [pyramid](https://github.com/tgsmith61591/pyramid), [pmdarima](https://github.com/tgsmith61591/pmdarima) - Wrapper for (Auto-) ARIMA. +[modeltime](https://cran.r-project.org/web/packages/modeltime/index.html) - Time series forecasting framework (R package). [pyflux](https://github.com/RJT1990/pyflux) - Time series prediction algorithms (ARIMA, GARCH, GAS, Bayesian). -[prophet](https://github.com/facebook/prophet) - Time series prediction library. +[atspy](https://github.com/firmai/atspy) - Automated Time Series Models. +[pm-prophet](https://github.com/luke14free/pm-prophet) - Time series prediction and decomposition library. [htsprophet](https://github.com/CollinRooney12/htsprophet) - Hierarchical Time Series Forecasting using Prophet. +[nupic](https://github.com/numenta/nupic) - Hierarchical Temporal Memory (HTM) for Time Series Prediction and Anomaly Detection. [tensorflow](https://github.com/tensorflow/tensorflow/) - LSTM and others, examples: [link]( https://machinelearningmastery.com/time-series-forecasting-long-short-term-memory-network-python/ -), [link](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/timeseries), [link](https://github.com/hzy46/TensorFlow-Time-Series-Examples), [Explain LSTM](https://github.com/slundberg/shap/blob/master/notebooks/deep_explainer/Keras%20LSTM%20for%20IMDB%20Sentiment%20Classification.ipynb), seq2seq: [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) +), [link](https://github.com/hzy46/TensorFlow-Time-Series-Examples), seq2seq: [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) - Preprocessing: Denoising, Compression, Resampling. [tsfresh](https://github.com/blue-yonder/tsfresh) - Time series feature engineering. +[tsfel](https://github.com/fraunhoferportugal/tsfel) - Time series feature extraction. [thunder](https://github.com/thunder-project/thunder) - Data structures and algorithms for loading, processing, and analyzing time series data. [gatspy](https://www.astroml.org/gatspy/) - General tools for Astronomical Time Series, [talk](https://www.youtube.com/watch?v=E4NMZyfao2c). [gendis](https://github.com/IBCNServices/GENDIS) - shapelets, [example](https://github.com/IBCNServices/GENDIS/blob/master/gendis/example.ipynb). [tslearn](https://github.com/rtavenar/tslearn) - Time series clustering and classification, `TimeSeriesKMeans`, `TimeSeriesKMeans`. -[pastas](https://pastas.readthedocs.io/en/latest/examples.html) - Simulation of time series. +[pastas](https://github.com/pastas/pastas) - Analysis of Groundwater Time Series. [fastdtw](https://github.com/slaypni/fastdtw) - Dynamic Time Warp Distance. [fable](https://www.rdocumentation.org/packages/fable/versions/0.0.0.9000) - Time Series Forecasting (R package). -[CausalImpact](https://github.com/tcassou/causal_impact) - Causal Impact Analysis ([R package](https://google.github.io/CausalImpact/CausalImpact.html)). -[pydlm](https://github.com/wwrechard/pydlm) - Bayesian time series modeling ([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)) +[pydlm](https://github.com/wwrechard/pydlm) - Bayesian time series modelling ([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) - Automatic Time Series Forecasting. [luminol](https://github.com/linkedin/luminol) - Anomaly Detection and Correlation library from Linkedin. -[matrixprofile-ts](https://github.com/target/matrixprofile-ts) - Detecting patterns and anomalies, [website](https://www.cs.ucr.edu/~eamonn/MatrixProfile.html), [ppt](https://www.cs.ucr.edu/~eamonn/Matrix_Profile_Tutorial_Part1.pdf). +[matrixprofile-ts](https://github.com/target/matrixprofile-ts) - Detecting patterns and anomalies, [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) - Another matrix profile library. [obspy](https://github.com/obspy/obspy) - Seismology package. Useful `classic_sta_lta` function. [RobustSTL](https://github.com/LeeDoYup/RobustSTL) - Robust Seasonal-Trend Decomposition. [seglearn](https://github.com/dmbee/seglearn) - Time Series library. -[pyts](https://github.com/johannfaouzi/pyts) - Time series transformation and classification. +[pyts](https://github.com/johannfaouzi/pyts) - Time series transformation and classification, [Imaging time series](https://pyts.readthedocs.io/en/latest/auto_examples/index.html#imaging-time-series). +Turn time series into images and use Neural Nets: [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) - Toolbox for (deep) learning with time series. +[adtk](https://github.com/arundo/adtk) - Time Series Anomaly Detection. +[rocket](https://github.com/angus924/rocket) - Time Series classification using random convolutional kernels. +[luminaire](https://github.com/zillow/luminaire) - Anomaly Detection for time series. +[etna](https://github.com/tinkoff-ai/etna) - Time Series library. +[Chaos Genius](https://github.com/chaos-genius/chaos_genius) - ML powered analytics engine for outlier/anomaly detection and root cause analysis. + +##### Time Series Evaluation +[TimeSeriesSplit](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.TimeSeriesSplit.html) - Sklearn time series split. +[tscv](https://github.com/WenjieZ/TSCV) - Evaluation with gap. -#### Financial Data +#### Financial Data and Trading +Tutorial on using cvxpy: [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) - Read stock data. +[yfinance](https://github.com/ranaroussi/yfinance) - Read stock data from Yahoo Finance. +[findatapy](https://github.com/cuemacro/findatapy) - Read stock data from various sources. +[ta](https://github.com/bukosabino/ta) - Technical analysis library. +[backtrader](https://github.com/mementum/backtrader) - Backtesting for trading strategies. +[surpriver](https://github.com/tradytics/surpriver) - Find high moving stocks before they move using anomaly detection and machine learning. +[ffn](https://github.com/pmorissette/ffn) - Financial functions. +[bt](https://github.com/pmorissette/bt) - Backtesting algorithms. +[alpaca-trade-api-python](https://github.com/alpacahq/alpaca-trade-api-python) - Commission-free trading through API. +[eiten](https://github.com/tradytics/eiten) - Eigen portfolios, minimum variance portfolios and other algorithmic investing strategies. +[tf-quant-finance](https://github.com/google/tf-quant-finance) - Quantitative finance tools in TensorFlow, by Google. +[quantstats](https://github.com/ranaroussi/quantstats) - Portfolio management. +[Riskfolio-Lib](https://github.com/dcajasn/Riskfolio-Lib) - Portfolio optimization and strategic asset allocation. +[OpenBBTerminal](https://github.com/OpenBB-finance/OpenBBTerminal) - Terminal. +[mplfinance](https://github.com/matplotlib/mplfinance) - Financial markets data visualization. + +##### Quantopian Stack [pyfolio](https://github.com/quantopian/pyfolio) - Portfolio and risk analytics. [zipline](https://github.com/quantopian/zipline) - Algorithmic trading. [alphalens](https://github.com/quantopian/alphalens) - Performance analysis of predictive stock factors. +[empyrical](https://github.com/quantopian/empyrical) - Financial risk metrics. +[trading_calendars](https://github.com/quantopian/trading_calendars) - Calendars for various securities exchanges. #### 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/) - Survival analysis, Cox PH Regression, [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) - Survival analysis. -[xgboost](https://github.com/dmlc/xgboost) - `"objective": "survival:cox"` [NHANES example](https://slundberg.github.io/shap/notebooks/NHANES%20I%20Survival%20Model.html) +[xgboost](https://github.com/dmlc/xgboost) - `"objective": "survival:cox"` [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) - Survival analysis, [intro](http://www.hammerlab.org/2017/06/26/introducing-survivalstan/). [convoys](https://github.com/better/convoys) - Analyze time lagged conversions. RandomSurvivalForests (R packages: randomForestSRC, ggRandomForests). +[pysurvival](https://github.com/square/pysurvival) - Survival analysis. +[DeepSurvivalMachines](https://github.com/autonlab/DeepSurvivalMachines) - Fully Parametric Survival Regression. +[auton-survival](https://github.com/autonlab/auton-survival) - Regression, Counterfactual Estimation, Evaluation and Phenotyping with Censored Time-to-Events. #### Outlier Detection & Anomaly Detection [sklearn](https://scikit-learn.org/stable/modules/outlier_detection.html) - Isolation Forest and others. @@ -325,23 +964,62 @@ RandomSurvivalForests (R packages: randomForestSRC, ggRandomForests). [eif](https://github.com/sahandha/eif) - Extended Isolation Forest. [AnomalyDetection](https://github.com/twitter/AnomalyDetection) - Anomaly detection (R package). [luminol](https://github.com/linkedin/luminol) - Anomaly Detection and Correlation library from Linkedin. +Distances for comparing histograms and detecting outliers - [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) - Anomaly detection library based on singular spectrum transformation. +[telemanom](https://github.com/khundman/telemanom) - Detect anomalies in multivariate time series data using LSTMs. +[luminaire](https://github.com/zillow/luminaire) - Anomaly Detection for time series. +[rrcf](https://github.com/kLabUM/rrcf) - Robust Random Cut Forest algorithm for anomaly detection on streams. + +#### Concept Drift & Domain Shift +[TorchDrift](https://github.com/TorchDrift/TorchDrift) - Drift Detection for PyTorch Models. +[alibi-detect](https://github.com/SeldonIO/alibi-detect) - Algorithms for outlier, adversarial and drift detection. +[evidently](https://github.com/evidentlyai/evidently) - Evaluate and monitor ML models from validation to production. +[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). #### Ranking [lightning](https://github.com/scikit-learn-contrib/lightning) - Large-scale linear classification, regression and ranking. -#### Scoring -[SLIM](https://github.com/ustunb/slim-python) - Scoring systems for classification, Supersparse linear integer models. +#### Causal Inference +[Chatton et al. - The Causal Cookbook: Recipes for Propensity Scores, G-Computation, and Doubly Robust Standardization](https://journals.sagepub.com/doi/10.1177/25152459241236149) +[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) - Marginal Effects, g-computation and more. +[Statistical Rethinking](https://github.com/rmcelreath/stat_rethinking_2022) - Video Lecture Series, Bayesian Statistics, Causal Models, [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). +[Python Causality Handbook](https://github.com/matheusfacure/python-causality-handbook) +[dowhy](https://github.com/py-why/dowhy) - Estimate causal effects. +[CausalImpact](https://github.com/tcassou/causal_impact) - Causal Impact Analysis ([R package](https://google.github.io/CausalImpact/CausalImpact.html)). +[causallib](https://github.com/IBM/causallib) - Modular causal inference analysis and model evaluations by IBM, [examples](https://github.com/IBM/causallib/tree/master/examples). +[causalml](https://github.com/uber/causalml) - Causal inference by Uber. +[upliftml](https://github.com/bookingcom/upliftml) - Causal inference by Booking.com. +[causality](https://github.com/akelleh/causality) - Causal analysis using observational datasets. +[DoubleML](https://github.com/DoubleML/doubleml-for-py) - Machine Learning + Causal inference, [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) - Heterogeneous Treatment Effects Estimation by Microsoft. + +##### 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) -#### Probabilistic Modeling and Bayes +#### 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://docs.pymc.io/) - Baysian modelling, [intro](https://docs.pymc.io/notebooks/getting_started) +[PyMC3](https://www.pymc.io/projects/docs/en/stable/learn.html) - Bayesian modelling. +[numpyro](https://github.com/pyro-ppl/numpyro) - Probabilistic programming with numpy, built on [pyro](https://github.com/pyro-ppl/pyro). [pomegranate](https://github.com/jmschrei/pomegranate) - Probabilistic modelling, [talk](https://www.youtube.com/watch?v=dE5j6NW-Kzg). [pmlearn](https://github.com/pymc-learn/pymc-learn) - Probabilistic machine learning. [arviz](https://github.com/arviz-devs/arviz) - Exploratory analysis of Bayesian models. [zhusuan](https://github.com/thu-ml/zhusuan) - Bayesian deep learning, generative models. -[dowhy](https://github.com/Microsoft/dowhy) - Estimate causal effects. -[edward](https://github.com/blei-lab/edward) - Probabilistic modeling, inference, and criticism, [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](http://pyro.ai/) - Deep Universal Probabilistic Programming +[edward](https://github.com/blei-lab/edward) - Probabilistic modelling, inference, and criticism, [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) - Deep Universal Probabilistic Programming. +[TensorFlow probability](https://github.com/tensorflow/probability) - Deep learning and probabilistic modelling, [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) - High-level Bayesian model-building interface on top of PyMC3. +[neural-tangents](https://github.com/google/neural-tangents) - Infinite Neural Networks. +[bnlearn](https://github.com/erdogant/bnlearn) - Bayesian networks, parameter learning, inference and sampling methods. + +#### Gaussian Processes +[Visualization](http://www.infinitecuriosity.org/vizgp/), [Article](https://distill.pub/2019/visual-exploration-gaussian-processes/) +[GPyOpt](https://github.com/SheffieldML/GPyOpt) - Gaussian process optimization. +[GPflow](https://github.com/GPflow/GPflow) - Gaussian processes (TensorFlow). +[gpytorch](https://gpytorch.ai/) - Gaussian processes (PyTorch). #### 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/) @@ -349,65 +1027,92 @@ RandomSurvivalForests (R packages: randomForestSRC, ggRandomForests). [vecstack](https://github.com/vecxoz/vecstack) - Stacking ML models. [StackNet](https://github.com/kaz-Anova/StackNet) - Stacking ML models. [mlens](https://github.com/flennerhag/mlens) - Ensemble learning. +[combo](https://github.com/yzhao062/combo) - Combining ML models (stacking, ensembling). #### Model Evaluation +[evaluate](https://github.com/huggingface/evaluate) - Evaluate machine learning models (huggingface). [pycm](https://github.com/sepandhaghighi/pycm) - Multi-class confusion matrix. [pandas_ml](https://github.com/pandas-ml/pandas-ml) - Confusion matrix. Plotting learning curve: [link](http://www.ritchieng.com/machinelearning-learning-curve/). [yellowbrick](http://www.scikit-yb.org/en/latest/api/model_selection/learning_curve.html) - Learning curve. +[pyroc](https://github.com/noudald/pyroc) - Receiver Operating Characteristic (ROC) curves. + +#### Model Uncertainty +[awesome-conformal-prediction](https://github.com/valeman/awesome-conformal-prediction) - Uncertainty quantification. +[uncertainty-toolbox](https://github.com/uncertainty-toolbox/uncertainty-toolbox) - Predictive uncertainty quantification, calibration, metrics, and visualization. #### 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) -[shap](https://github.com/slundberg/shap) - Explain predictions of machine learning models, [talk](https://www.youtube.com/watch?v=C80SQe16Rao). +scikit-learn - [Permutation Importance](https://scikit-learn.org/stable/modules/generated/sklearn.inspection.permutation_importance.html) (can be used on any trained classifier) and [Partial Dependence](https://scikit-learn.org/stable/modules/generated/sklearn.inspection.partial_dependence.html) +[shap](https://github.com/slundberg/shap) - Explain predictions of machine learning models, [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) - Shapley interaction quantification. [treeinterpreter](https://github.com/andosa/treeinterpreter) - Interpreting scikit-learn's decision tree and random forest predictions. [lime](https://github.com/marcotcr/lime) - Explaining the predictions of any machine learning classifier, [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) - Create LIMEs for XGBoost. [eli5](https://github.com/TeamHG-Memex/eli5) - Inspecting machine learning classifiers and explaining their predictions. [lofo-importance](https://github.com/aerdem4/lofo-importance) - Leave One Feature Out Importance, [talk](https://www.youtube.com/watch?v=zqsQ2ojj7sE). [pybreakdown](https://github.com/MI2DataLab/pyBreakDown) - Generate feature contribution plots. -[FairML](https://github.com/adebayoj/fairml) - Model explanation, feature importance. [pycebox](https://github.com/AustinRochford/PyCEbox) - Individual Conditional Expectation Plot Toolbox. [pdpbox](https://github.com/SauceCat/PDPbox) - Partial dependence plot toolbox, [example](https://www.kaggle.com/dansbecker/partial-plots). [partial_dependence](https://github.com/nyuvis/partial_dependence) - Visualize and cluster partial dependence. -[skater](https://github.com/datascienceinc/Skater) - Unified framework to enable model interpretation. -[anchor](https://github.com/marcotcr/anchor) - High-Precision Model-Agnostic Explanations for classifiers. -[l2x](https://github.com/Jianbo-Lab/L2X) - Instancewise feature selection as methodology for model interpretation. [contrastive_explanation](https://github.com/MarcelRobeer/ContrastiveExplanation) - Contrastive explanations. [DrWhy](https://github.com/ModelOriented/DrWhy) - Collection of tools for explainable AI. [lucid](https://github.com/tensorflow/lucid) - Neural network interpretability. [xai](https://github.com/EthicalML/XAI) - An eXplainability toolbox for machine learning. +[innvestigate](https://github.com/albermax/innvestigate) - A toolbox to investigate neural network predictions. +[dalex](https://github.com/pbiecek/DALEX) - Explanations for ML models (R package). +[interpretml](https://github.com/interpretml/interpret) - Fit interpretable models, explain models. +[shapash](https://github.com/MAIF/shapash) - Model interpretability. +[imodels](https://github.com/csinva/imodels) - Interpretable ML package. +[captum](https://github.com/pytorch/captum) - Model interpretability and understanding for PyTorch. #### Automated Machine Learning -[AdaNet](https://github.com/tensorflow/adanet) - Automated machine learning based on tensorflow. +[AdaNet](https://github.com/tensorflow/adanet) - Automated machine learning based on TensorFlow. [tpot](https://github.com/EpistasisLab/tpot) - Automated machine learning tool, optimizes machine learning pipelines. -[auto_ml](https://github.com/ClimbsRocks/auto_ml) - Automated machine learning for analytics & production. [autokeras](https://github.com/jhfjhfj1/autokeras) - AutoML for deep learning. [nni](https://github.com/Microsoft/nni) - Toolkit for neural architecture search and hyper-parameter tuning by Microsoft. -[automl-gs](https://github.com/minimaxir/automl-gs) - Automated machine learning. +[mljar](https://github.com/mljar/mljar-supervised) - Automated machine learning. +[automl_zero](https://github.com/google-research/google-research/tree/master/automl_zero) - Automatically discover computer programs that can solve machine learning tasks from Google. +[AlphaPy](https://github.com/ScottfreeLLC/AlphaPy) - Automated Machine Learning using scikit-learn xgboost, LightGBM and others. + +#### Graph Representation Learning +[Karate Club](https://github.com/benedekrozemberczki/karateclub) - Unsupervised learning on graphs. +[PyTorch Geometric](https://github.com/rusty1s/pytorch_geometric) - Graph representation learning with PyTorch. +[DLG](https://github.com/dmlc/dgl) - Graph representation learning with TensorFlow. + +#### Convex optimization +[cvxpy](https://github.com/cvxgrp/cvxpy) - Modelling language for convex optimization problems. Tutorial: [1](https://calmcode.io/cvxpy-one/the-stigler-diet.html), [2](https://calmcode.io/cvxpy-two/introduction.html) #### Evolutionary Algorithms & Optimization [deap](https://github.com/DEAP/deap) - Evolutionary computation framework (Genetic Algorithm, Evolution strategies). [evol](https://github.com/godatadriven/evol) - DSL for composable evolutionary algorithms, [talk](https://www.youtube.com/watch?v=68ABAU_V8qI&t=11m49s). [platypus](https://github.com/Project-Platypus/Platypus) - Multiobjective optimization. +[autograd](https://github.com/HIPS/autograd) - Efficiently computes derivatives of numpy code. [nevergrad](https://github.com/facebookresearch/nevergrad) - Derivation-free optimization. [gplearn](https://gplearn.readthedocs.io/en/stable/) - Sklearn-like interface for genetic programming. [blackbox](https://github.com/paulknysh/blackbox) - Optimization of expensive black-box functions. Optometrist algorithm - [paper](https://www.nature.com/articles/s41598-017-06645-7). +[DeepSwarm](https://github.com/Pattio/DeepSwarm) - Neural architecture search. +[evotorch](https://github.com/nnaisense/evotorch) - Evolutionary computation library built on Pytorch. #### 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) - Hyperparameter search using genetic algorithms. [hyperopt](https://github.com/hyperopt/hyperopt) - Hyperparameter optimization. [hyperopt-sklearn](https://github.com/hyperopt/hyperopt-sklearn) - Hyperopt + sklearn. +[optuna](https://github.com/pfnet/optuna) - Hyperparamter optimization, [Talk](https://www.youtube.com/watch?v=tcrcLRopTX0). [skopt](https://scikit-optimize.github.io/) - `BayesSearchCV` for Hyperparameter search. [tune](https://ray.readthedocs.io/en/latest/tune.html) - Hyperparameter search with a focus on deep learning and deep reinforcement learning. -[optuna](https://github.com/pfnet/optuna) - Hyperparamter optimization. -[hypergraph](https://github.com/aljabr0/hypergraph) - Global optimization methods and hyperparameter optimization. [bbopt](https://github.com/evhub/bbopt) - Black box hyperparameter optimization. [dragonfly](https://github.com/dragonfly/dragonfly) - Scalable Bayesian optimisation. +[botorch](https://github.com/pytorch/botorch) - Bayesian optimization in PyTorch. +[ax](https://github.com/facebook/Ax) - Adaptive Experimentation Platform by Facebook. +[lightning-hpo](https://github.com/Lightning-AI/lightning-hpo) - Hyperparameter optimization based on optuna. #### Incremental Learning, Online Learning sklearn - [PassiveAggressiveClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.PassiveAggressiveClassifier.html), [PassiveAggressiveRegressor](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.PassiveAggressiveRegressor.html). -[creme-ml](https://github.com/creme-ml/creme) - Incremental learning framework. +[river](https://github.com/online-ml/river) - Online machine learning. [Kaggler](https://github.com/jeongyoonlee/Kaggler) - Online Learning algorithms. #### Active Learning @@ -421,68 +1126,133 @@ AlphaZero methodology - [1](https://github.com/AppliedDataSciencePartners/DeepRe [RLLib](https://ray.readthedocs.io/en/latest/rllib.html) - Library for reinforcement learning. [Horizon](https://github.com/facebookresearch/Horizon/) - Facebook RL framework. -#### Frameworks -[h2o](https://github.com/h2oai/h2o-3) - Scalable machine learning. -[turicreate](https://github.com/apple/turicreate) - Apple Machine Learning Toolkit. -[astroml](https://github.com/astroML/astroML) - ML for astronomical data. - #### Deployment and Lifecycle Management + +##### Workflow Scheduling and Orchestration +[nextflow](https://github.com/goodwright/nextflow.py) - Run scripts and workflow graphs in Docker image using Google Life Sciences, AWS Batch, [Website](https://github.com/nextflow-io/nextflow). +[airflow](https://github.com/apache/airflow) - Schedule and monitor workflows. +[prefect](https://github.com/PrefectHQ/prefect) - Python specific workflow scheduling. +[dagster](https://github.com/dagster-io/dagster) - Development, production and observation of data assets. +[ploomber](https://github.com/ploomber/ploomber) - Workflow orchestration. +[kestra](https://github.com/kestra-io/kestra) - Workflow orchestration. +[cml](https://github.com/iterative/cml) - CI/CD for Machine Learning Projects. +[rocketry](https://github.com/Miksus/rocketry) - Task scheduling. +[huey](https://github.com/coleifer/huey) - Task queue. + +##### 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) - Facilitates building Docker images. + +##### Data Versioning, Databases, Pipelines and Model Serving +[dvc](https://github.com/iterative/dvc) - Version control for large files. +[kedro](https://github.com/quantumblacklabs/kedro) - Build data pipelines. +[feast](https://github.com/feast-dev/feast) - Feature store. [Video](https://www.youtube.com/watch?v=_omcXenypmo). +[pgvector](https://github.com/pgvector/pgvector) - Vector similarity search for Postgres. +[pinecone](https://www.pinecone.io/) - Database for vector search applications. +[truss](https://github.com/basetenlabs/truss) - Serve ML models. +[milvus](https://github.com/milvus-io/milvus) - Vector database for similarity search. +[mlem](https://github.com/iterative/mlem) - Version and deploy your ML models following GitOps principles. + +##### Data Science Related [m2cgen](https://github.com/BayesWitnesses/m2cgen) - Transpile trained ML models into other languages. [sklearn-porter](https://github.com/nok/sklearn-porter) - Transpile trained scikit-learn estimators to C, Java, JavaScript and others. [mlflow](https://mlflow.org/) - Manage the machine learning lifecycle, including experimentation, reproducibility and deployment. -[modelchimp](https://github.com/ModelChimp/modelchimp) - Experiment Tracking. [skll](https://github.com/EducationalTestingService/skll) - Command-line utilities to make it easier to run machine learning experiments. +[BentoML](https://github.com/bentoml/BentoML) - Package and deploy machine learning models for serving in production. +[dagster](https://github.com/dagster-io/dagster) - Tool with focus on dependency graphs. +[knockknock](https://github.com/huggingface/knockknock) - Be notified when your training ends. +[metaflow](https://github.com/Netflix/metaflow) - Lifecycle Management Tool by Netflix. +[cortex](https://github.com/cortexlabs/cortex) - Deploy machine learning models. +[Neptune](https://neptune.ai) - Experiment tracking and model registry. +[clearml](https://github.com/allegroai/clearml) - Experiment Manager, MLOps and Data-Management. +[polyaxon](https://github.com/polyaxon/polyaxon) - MLOps. +[sematic](https://github.com/sematic-ai/sematic) - Deploy machine learning models. +[zenml](https://github.com/zenml-io/zenml) - MLOPs. -#### Other -[dvc](https://github.com/iterative/dvc) - Versioning for ML projects. -[daft](https://github.com/dfm/daft) - Render probabilistic graphical models using matplotlib. -[unyt](https://github.com/yt-project/unyt) - Working with units. -[scrapy](https://github.com/scrapy/scrapy) - Web scraping library. -[VowpalWabbit](https://github.com/VowpalWabbit/vowpal_wabbit) - ML Toolkit from Microsoft. -[metric-learn](https://github.com/metric-learn/metric-learn) - Metric learning. +#### Math and Background +[All kinds of math and statistics resources](https://realnotcomplex.com/) +Gilbert Strang - [Linear Algebra](https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/index.htm) +Gilbert Strang - [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/) -#### General Python Programming -[funcy](https://github.com/Suor/funcy) - Fancy and practical functional tools. -[more_itertools](https://more-itertools.readthedocs.io/en/latest/) - Extension of itertools. -[dill](https://pypi.org/project/dill/) - Serialization, alternative to pickle. -[attrs](https://github.com/python-attrs/attrs) - Python classes without boilerplate. -[dateparser](https://dateparser.readthedocs.io/en/latest/) - A better date parser. -[jellyfish](https://github.com/jamesturk/jellyfish) - Approximate string matching. - -#### Blogs -[PocketCluster](https://blog.pocketcluster.io/) - Blog. -[Distill.pub](https://distill.pub/) - Blog. - -#### 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 Business Machine Learning](https://github.com/firmai/business-machine-learning) -[Awesome Data Science with Ruby](https://github.com/arbox/data-science-with-ruby) -[Awesome Deep Learning](https://github.com/ChristosChristofidis/awesome-deep-learning) -[Awesome Financial Machine Learning](https://github.com/firmai/financial-machine-learning) -[Awesome Machine Learning](https://github.com/josephmisiti/awesome-machine-learning#python) -[Awesome Machine Learning Interpretability](https://github.com/jphall663/awesome-machine-learning-interpretability) -[Awesome Machine Learning Operations](https://github.com/EthicalML/awesome-machine-learning-operations) -[Awesome Network Embedding](https://github.com/chihming/awesome-network-embedding) +#### Resources +[Distill.pub](https://distill.pub/) - Blog. +[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) + +##### Guidelines +[datasharing](https://github.com/jtleek/datasharing) - Guide to data sharing. + +##### Books +[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/) (R Language) +[Rafael Irizarry - Advanced Data Science](https://rafalab.dfci.harvard.edu/dsbook-part-2/) (R Language) + +##### 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 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 Time Series](https://github.com/MaxBenChrist/awesome_time_series_in_python) [Awesome Time Series Anomaly Detection](https://github.com/rob-med/awesome-TS-anomaly-detection) -[Recommender Systems (Microsoft)](https://github.com/Microsoft/Recommenders) +[Awesome Visual Attentions](https://github.com/MenghaoGuo/Awesome-Vision-Attentions) +[Awesome Visual Transformer](https://github.com/dk-liang/Awesome-Visual-Transformer) + +#### Lectures +[NYU Deep Learning SP21](https://www.youtube.com/playlist?list=PLLHTzKZzVU9e6xUfG10TkTWApKSZCzuBI) - YouTube Playlist. #### Things I google a lot +[Color Codes](https://github.com/d3/d3-3.x-api-reference/blob/master/Ordinal-Scales.md#categorical-colors) [Frequency codes for time series](https://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases) [Date parsing codes](https://docs.python.org/3/library/datetime.html#strftime-and-strptime-behavior) -[Feature Calculators tsfresh](https://github.com/blue-yonder/tsfresh/blob/master/tsfresh/feature_extraction/feature_calculators.py) ## Contributing Do you know a package that should be on this list? Did you spot a package that is no longer maintained and should be removed from this list? Then feel free to read the [contribution guidelines](CONTRIBUTING.md) and submit your pull request or create a new issue. ## License - [](https://creativecommons.org/publicdomain/zero/1.0/)