
[48;5;235m[38;5;249m[49m[39m
[48;5;235m[38;5;249m[49m[39m
[48;5;235m[38;5;249m[49m[39m
[48;5;235m[38;5;249m[49m[39m



[48;5;235m[38;5;249mAwesome Python Data Science[49m[39m







[38;5;11m[1m▐[0m[38;5;12m [39m[38;5;12mProbably the best curated list of data science software in Python[39m

[38;2;255;187;0m[4mContents[0m
[38;5;12m- [39m[38;5;14m[1mContents[0m[38;5;12m (#contents)[39m
[38;5;12m- [39m[38;5;14m[1mMachine Learning[0m[38;5;12m (#machine-learning)[39m
[48;5;235m[38;5;249m- **General Purpose Machine Learning** (#general-purpose-machine-learning)[49m[39m
[38;5;12m  	- [39m[38;5;14m[1mGradient Boosting[0m[38;5;12m (#gradient-boosting)[39m
[48;5;235m[38;5;249m- **Ensemble Methods** (#ensemble-methods)[49m[39m[48;5;235m[38;5;249m      [49m[39m
[48;5;235m[38;5;249m- **Imbalanced Datasets** (#imbalanced-datasets)[49m[39m
[48;5;235m[38;5;249m- **Random Forests** (#random-forests)[49m[39m[48;5;235m[38;5;249m          [49m[39m
[48;5;235m[38;5;249m- **Kernel Methods** (#kernel-methods)[49m[39m[48;5;235m[38;5;249m          [49m[39m
[38;5;12m- [39m[38;5;14m[1mDeep Learning[0m[38;5;12m (#deep-learning)[39m
[48;5;235m[38;5;249m- **PyTorch** (#pytorch)[49m[39m[48;5;235m[38;5;249m      [49m[39m
[48;5;235m[38;5;249m- **TensorFlow** (#tensorflow)[49m[39m
[48;5;235m[38;5;249m- **MXNet** (#mxnet)[49m[39m[48;5;235m[38;5;249m          [49m[39m
[38;5;12m 	- [39m[38;5;14m[1mJAX[0m[38;5;12m (#jax)[39m
[48;5;235m[38;5;249m- **Others** (#others)[49m[39m
[38;5;12m- [39m[38;5;14m[1mAutomated Machine Learning[0m[38;5;12m (#automated-machine-learning)[39m
[38;5;12m- [39m[38;5;14m[1mNatural Language Processing[0m[38;5;12m (#natural-language-processing)[39m
[38;5;12m- [39m[38;5;14m[1mComputer Audition[0m[38;5;12m (#computer-audition)[39m
[38;5;12m- [39m[38;5;14m[1mComputer Vision[0m[38;5;12m (#computer-vision)[39m
[38;5;12m- [39m[38;5;14m[1mTime Series[0m[38;5;12m (#time-series)[39m
[38;5;12m- [39m[38;5;14m[1mReinforcement Learning[0m[38;5;12m (#reinforcement-learning)[39m
[38;5;12m- [39m[38;5;14m[1mGraph Machine Learning[0m[38;5;12m (#graph-machine-learning)[39m
[38;5;12m- [39m[38;5;14m[1mLearning-to-Rank & Recommender Systems[0m[38;5;12m (#learning-to-rank-&-recommender-systems)[39m
[38;5;12m- [39m[38;5;14m[1mProbabilistic Graphical Models[0m[38;5;12m (#probabilistic-graphical-models)[39m
[38;5;12m- [39m[38;5;14m[1mProbabilistic Methods[0m[38;5;12m (#probabilistic-methods)[39m
[38;5;12m- [39m[38;5;14m[1mModel Explanation[0m[38;5;12m (#model-explanation)[39m
[38;5;12m- [39m[38;5;14m[1mOptimization[0m[38;5;12m (#optimization)[39m
[38;5;12m- [39m[38;5;14m[1mGenetic Programming[0m[38;5;12m (#genetic-programming)[39m
[38;5;12m- [39m[38;5;14m[1mFeature Engineering[0m[38;5;12m (#feature-engineering)[39m
[48;5;235m[38;5;249m- **General** (#general)[49m[39m[48;5;235m[38;5;249m                    [49m[39m
[48;5;235m[38;5;249m- **Feature Selection** (#feature-selection)[49m[39m
[38;5;12m- [39m[38;5;14m[1mVisualization[0m[38;5;12m (#visualization)[39m
[48;5;235m[38;5;249m- **General Purposes** (#general-purposes)[49m[39m[48;5;235m[38;5;249m    [49m[39m
[48;5;235m[38;5;249m- **Interactive plots** (#interactive-plots)[49m[39m[48;5;235m[38;5;249m  [49m[39m
[48;5;235m[38;5;249m- **Map** (#map)[49m[39m[48;5;235m[38;5;249m                              [49m[39m
[48;5;235m[38;5;249m- **Automatic Plotting** (#automatic-plotting)[49m[39m
[48;5;235m[38;5;249m- **NLP** (#nlp)[49m[39m[48;5;235m[38;5;249m                              [49m[39m
[38;5;12m- [39m[38;5;14m[1mData Manipulation[0m[38;5;12m (#data-manipulation)[39m
[48;5;235m[38;5;249m- **Data Frames** (#data-frames)[49m[39m[48;5;235m[38;5;249m        [49m[39m
[48;5;235m[38;5;249m- **Pipelines** (#pipelines)[49m[39m[48;5;235m[38;5;249m            [49m[39m
[48;5;235m[38;5;249m- **Data-centric AI** (#data-centric-ai)[49m[39m
[48;5;235m[38;5;249m- **Synthetic Data** (#synthetic-data)[49m[39m[48;5;235m[38;5;249m  [49m[39m
[38;5;12m- [39m[38;5;14m[1mDeployment[0m[38;5;12m (#deployment)[39m
[38;5;12m- [39m[38;5;14m[1mStatistics[0m[38;5;12m (#statistics)[39m
[38;5;12m- [39m[38;5;14m[1mDistributed Computing[0m[38;5;12m (#distributed-computing)[39m
[38;5;12m- [39m[38;5;14m[1mExperimentation[0m[38;5;12m (#experimentation)[39m
[38;5;12m- [39m[38;5;14m[1mData Validation[0m[38;5;12m (#data-validation)[39m
[38;5;12m- [39m[38;5;14m[1mEvaluation[0m[38;5;12m (#evaluation)[39m
[38;5;12m- [39m[38;5;14m[1mComputations[0m[38;5;12m (#computations)[39m
[38;5;12m- [39m[38;5;14m[1mWeb Scraping[0m[38;5;12m (#web-scraping)[39m
[38;5;12m- [39m[38;5;14m[1mSpatial Analysis[0m[38;5;12m (#spatial-analysis)[39m
[38;5;12m- [39m[38;5;14m[1mQuantum Computing[0m[38;5;12m (#quantum-computing)[39m
[38;5;12m- [39m[38;5;14m[1mConversion[0m[38;5;12m (#conversion)[39m
[38;5;12m- [39m[38;5;14m[1mContributing[0m[38;5;12m (#contributing)[39m
[38;5;12m- [39m[38;5;14m[1mLicense[0m[38;5;12m (#license)[39m

[38;2;255;187;0m[4mMachine Learning[0m

[38;2;255;187;0m[4mGeneral Purpose Machine Learning[0m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mscikit-learn[0m[38;5;12m (http://scikit-learn.org/stable/) - Machine learning in Python. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mPyCaret[0m[38;5;12m (https://github.com/pycaret/pycaret) - An open-source, low-code machine learning library in Python.  [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mShogun[0m[38;5;12m (https://github.com/shogun-toolbox/shogun) - Machine learning toolbox.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mxLearn[0m[38;5;12m (https://github.com/aksnzhy/xlearn) - High Performance, Easy-to-use, and Scalable Machine Learning Package.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mcuML[0m[38;5;12m (https://github.com/rapidsai/cuml) - RAPIDS Machine Learning Library. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mmodAL[0m[38;5;12m (https://github.com/cosmic-cortex/modAL) - Modular active learning framework for Python3. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mSparkit-learn[0m[38;5;12m (https://github.com/lensacom/sparkit-learn) - PySpark + scikit-learn = Sparkit-learn. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mmlpack[0m[38;5;12m (https://github.com/mlpack/mlpack) - A scalable C++ machine learning library (Python bindings).[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mdlib[0m[38;5;12m (https://github.com/davisking/dlib) - Toolkit for making real-world machine learning and data analysis applications in C++ (Python bindings).[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mMLxtend[0m[38;5;12m (https://github.com/rasbt/mlxtend) - Extension and helper modules for Python's data analysis and machine learning libraries. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mhyperlearn[0m[38;5;12m (https://github.com/danielhanchen/hyperlearn) - 50%+ Faster, 50%+ less RAM usage, GPU support re-written Sklearn, Statsmodels. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mReproducible Experiment Platform (REP)[0m[38;5;12m (https://github.com/yandex/rep) - Machine Learning toolbox for Humans. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mscikit-multilearn[0m[38;5;12m (https://github.com/scikit-multilearn/scikit-multilearn) - Multi-label classification for python. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mseqlearn[0m[38;5;12m (https://github.com/larsmans/seqlearn) - Sequence classification toolkit for Python. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mpystruct[0m[38;5;12m (https://github.com/pystruct/pystruct) - Simple structured learning framework for Python. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1msklearn-expertsys[0m[38;5;12m (https://github.com/tmadl/sklearn-expertsys) - Highly interpretable classifiers for scikit learn. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mRuleFit[0m[38;5;12m (https://github.com/christophM/rulefit) - Implementation of the rulefit. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mmetric-learn[0m[38;5;12m (https://github.com/all-umass/metric-learn) - Metric learning algorithms in Python. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mpyGAM[0m[38;5;12m (https://github.com/dswah/pyGAM) - Generalized Additive Models in Python.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mcausalml[0m[38;5;12m (https://github.com/uber/causalml) - Uplift modeling and causal inference with machine learning algorithms. [39m

[38;2;255;187;0m[4mGradient Boosting[0m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mXGBoost[0m[38;5;12m (https://github.com/dmlc/xgboost) - Scalable, Portable, and Distributed Gradient Boosting. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mLightGBM[0m[38;5;12m (https://github.com/Microsoft/LightGBM) - A fast, distributed, high-performance gradient boosting. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mCatBoost[0m[38;5;12m (https://github.com/catboost/catboost) - An open-source gradient boosting on decision trees library. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mThunderGBM[0m[38;5;12m (https://github.com/Xtra-Computing/thundergbm) - Fast GBDTs and Random Forests on GPUs. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mNGBoost[0m[38;5;12m (https://github.com/stanfordmlgroup/ngboost) - Natural Gradient Boosting for Probabilistic Prediction.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mTensorFlow Decision Forests[0m[38;5;12m (https://github.com/tensorflow/decision-forests) - A collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models in Keras. [39m

[38;2;255;187;0m[4mEnsemble Methods[0m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mML-Ensemble[0m[38;5;12m (http://ml-ensemble.com/) - High performance ensemble learning. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mStacking[0m[38;5;12m (https://github.com/ikki407/stacking) - Simple and useful stacking library written in Python. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mstacked_generalization[0m[38;5;12m (https://github.com/fukatani/stacked_generalization) - Library for machine learning stacking generalization. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mvecstack[0m[38;5;12m (https://github.com/vecxoz/vecstack) - Python package for stacking (machine learning technique). [39m

[38;2;255;187;0m[4mImbalanced Datasets[0m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mimbalanced-learn[0m[38;5;12m (https://github.com/scikit-learn-contrib/imbalanced-learn) - Module to perform under-sampling and over-sampling with various techniques. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mimbalanced-algorithms[0m[38;5;12m (https://github.com/dialnd/imbalanced-algorithms) - Python-based implementations of algorithms for learning on imbalanced data. [39m

[38;2;255;187;0m[4mRandom Forests[0m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mrpforest[0m[38;5;12m (https://github.com/lyst/rpforest) - A forest of random projection trees. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1msklearn-random-bits-forest[0m[38;5;12m (https://github.com/tmadl/sklearn-random-bits-forest) - Wrapper of the Random Bits Forest program written by (Wang et al., 2016).[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mrgf_python[0m[38;5;12m (https://github.com/fukatani/rgf_python) - Python Wrapper of Regularized Greedy Forest. [39m

[38;2;255;187;0m[4mKernel Methods[0m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mpyFM[0m[38;5;12m (https://github.com/coreylynch/pyFM) - Factorization machines in python. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mfastFM[0m[38;5;12m (https://github.com/ibayer/fastFM) - A library for Factorization Machines. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mtffm[0m[38;5;12m (https://github.com/geffy/tffm) - TensorFlow implementation of an arbitrary order Factorization Machine. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mliquidSVM[0m[38;5;12m (https://github.com/liquidSVM/liquidSVM) - An implementation of SVMs.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mscikit-rvm[0m[38;5;12m (https://github.com/JamesRitchie/scikit-rvm) - Relevance Vector Machine implementation using the scikit-learn API. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mThunderSVM[0m[38;5;12m (https://github.com/Xtra-Computing/thundersvm) - A fast SVM Library on GPUs and CPUs. [39m

[38;2;255;187;0m[4mDeep Learning[0m

[38;2;255;187;0m[4mPyTorch[0m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mPyTorch[0m[38;5;12m (https://github.com/pytorch/pytorch) - Tensors and Dynamic neural networks in Python with strong GPU acceleration. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mpytorch-lightning[0m[38;5;12m (https://github.com/Lightning-AI/lightning) - PyTorch Lightning is just organized PyTorch. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mignite[0m[38;5;12m (https://github.com/pytorch/ignite) - High-level library to help with training neural networks in PyTorch. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mskorch[0m[38;5;12m (https://github.com/dnouri/skorch) - A scikit-learn compatible neural network library that wraps PyTorch. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mCatalyst[0m[38;5;12m (https://github.com/catalyst-team/catalyst) - High-level utils for PyTorch DL & RL research. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mChemicalX[0m[38;5;12m (https://github.com/AstraZeneca/chemicalx) - A PyTorch-based deep learning library for drug pair scoring. [39m

[38;2;255;187;0m[4mTensorFlow[0m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mTensorFlow[0m[38;5;12m (https://github.com/tensorflow/tensorflow) - Computation using data flow graphs for scalable machine learning by Google. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mTensorLayer[0m[38;5;12m (https://github.com/zsdonghao/tensorlayer) - Deep Learning and Reinforcement Learning Library for Researcher and Engineer. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mTFLearn[0m[38;5;12m (https://github.com/tflearn/tflearn) - Deep learning library featuring a higher-level API for TensorFlow. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mSonnet[0m[38;5;12m (https://github.com/deepmind/sonnet) - TensorFlow-based neural network library. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mtensorpack[0m[38;5;12m (https://github.com/ppwwyyxx/tensorpack) - A Neural Net Training Interface on TensorFlow. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mPolyaxon[0m[38;5;12m (https://github.com/polyaxon/polyaxon) - A platform that helps you build, manage and monitor deep learning models. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mtfdeploy[0m[38;5;12m (https://github.com/riga/tfdeploy) - Deploy TensorFlow graphs for fast evaluation and export to TensorFlow-less environments running numpy. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mtensorflow-upstream[0m[38;5;12m (https://github.com/ROCmSoftwarePlatform/tensorflow-upstream) - TensorFlow ROCm port. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mTensorFlow Fold[0m[38;5;12m (https://github.com/tensorflow/fold) - Deep learning with dynamic computation graphs in TensorFlow. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mTensorLight[0m[38;5;12m (https://github.com/bsautermeister/tensorlight) - A high-level framework for TensorFlow. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mMesh TensorFlow[0m[38;5;12m (https://github.com/tensorflow/mesh) - Model Parallelism Made Easier. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mLudwig[0m[38;5;12m (https://github.com/uber/ludwig) - A toolbox that allows one to train and test deep learning models without the need to write code. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mKeras[0m[38;5;12m (https://keras.io) - A high-level neural networks API running on top of TensorFlow.  [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mkeras-contrib[0m[38;5;12m (https://github.com/keras-team/keras-contrib) - Keras community contributions. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mHyperas[0m[38;5;12m (https://github.com/maxpumperla/hyperas) - Keras + Hyperopt: A straightforward wrapper for a convenient hyperparameter. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mElephas[0m[38;5;12m (https://github.com/maxpumperla/elephas) - Distributed Deep learning with Keras & Spark. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mqkeras[0m[38;5;12m (https://github.com/google/qkeras) - A quantization deep learning library. [39m

[38;2;255;187;0m[4mMXNet[0m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mMXNet[0m[38;5;12m (https://github.com/apache/incubator-mxnet) - Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mGluon[0m[38;5;12m (https://github.com/gluon-api/gluon-api) - A clear, concise, simple yet powerful and efficient API for deep learning (now included in MXNet). [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mXfer[0m[38;5;12m (https://github.com/amzn/xfer) - Transfer Learning library for Deep Neural Networks. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mMXNet[0m[38;5;12m (https://github.com/ROCmSoftwarePlatform/mxnet) - HIP Port of MXNet. [39m

[38;2;255;187;0m[4mJAX[0m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mJAX[0m[38;5;12m (https://github.com/google/jax) - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mFLAX[0m[38;5;12m (https://github.com/google/flax) - A neural network library for JAX that is designed for flexibility.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mOptax[0m[38;5;12m (https://github.com/google-deepmind/optax) - A gradient processing and optimization library for JAX.[39m

[38;2;255;187;0m[4mOthers[0m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mtransformers[0m[38;5;12m (https://github.com/huggingface/transformers) - State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mTangent[0m[38;5;12m (https://github.com/google/tangent) - Source-to-Source Debuggable Derivatives in Pure Python.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mautograd[0m[38;5;12m (https://github.com/HIPS/autograd) - Efficiently computes derivatives of numpy code.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mCaffe[0m[38;5;12m (https://github.com/BVLC/caffe) - A fast open framework for deep learning.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mnnabla[0m[38;5;12m (https://github.com/sony/nnabla) - Neural Network Libraries by Sony.[39m

[38;2;255;187;0m[4mAutomated Machine Learning[0m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mauto-sklearn[0m[38;5;12m (https://github.com/automl/auto-sklearn) - An AutoML toolkit and a drop-in replacement for a scikit-learn estimator. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mAuto-PyTorch[0m[38;5;12m (https://github.com/automl/Auto-PyTorch) - Automatic architecture search and hyperparameter optimization for PyTorch. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mAutoKeras[0m[38;5;12m (https://github.com/keras-team/autokeras) - AutoML library for deep learning. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mAutoGluon[0m[38;5;12m (https://github.com/awslabs/autogluon) - AutoML for Image, Text, Tabular, Time-Series, and MultiModal Data.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mTPOT[0m[38;5;12m (https://github.com/rhiever/tpot) - AutoML tool that optimizes machine learning pipelines using genetic programming. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mMLBox[0m[38;5;12m (https://github.com/AxeldeRomblay/MLBox) - A powerful Automated Machine Learning python library.[39m

[38;2;255;187;0m[4mNatural Language Processing[0m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mtorchtext[0m[38;5;12m (https://github.com/pytorch/text) - Data loaders and abstractions for text and NLP. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mgluon-nlp[0m[38;5;12m (https://github.com/dmlc/gluon-nlp) - NLP made easy. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mKerasNLP[0m[38;5;12m (https://github.com/keras-team/keras-nlp) - Modular Natural Language Processing workflows with Keras. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mspaCy[0m[38;5;12m (https://spacy.io/) - Industrial-Strength Natural Language Processing.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mNLTK[0m[38;5;12m (https://github.com/nltk/nltk) -  Modules, data sets, and tutorials supporting research and development in Natural Language Processing.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mCLTK[0m[38;5;12m (https://github.com/cltk/cltk) - The Classical Language Toolkik.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mgensim[0m[38;5;12m (https://radimrehurek.com/gensim/) - Topic Modelling for Humans.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mpyMorfologik[0m[38;5;12m (https://github.com/dmirecki/pyMorfologik) - Python binding for .[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mskift[0m[38;5;12m (https://github.com/shaypal5/skift) - Scikit-learn wrappers for Python fastText. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mPhonemizer[0m[38;5;12m (https://github.com/bootphon/phonemizer) - Simple text-to-phonemes converter for multiple languages.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mflair[0m[38;5;12m (https://github.com/zalandoresearch/flair) - Very simple framework for state-of-the-art NLP.[39m

[38;2;255;187;0m[4mComputer Audition[0m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mtorchaudio[0m[38;5;12m (https://github.com/pytorch/audio) - An audio library for PyTorch. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mlibrosa[0m[38;5;12m (https://github.com/librosa/librosa) - Python library for audio and music analysis.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mYaafe[0m[38;5;12m (https://github.com/Yaafe/Yaafe) - Audio features extraction.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1maubio[0m[38;5;12m (https://github.com/aubio/aubio) - A library for audio and music analysis.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mEssentia[0m[38;5;12m (https://github.com/MTG/essentia) - Library for audio and music analysis, description, and synthesis.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mLibXtract[0m[38;5;12m (https://github.com/jamiebullock/LibXtract) - A simple, portable, lightweight library of audio feature extraction functions.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mMarsyas[0m[38;5;12m (https://github.com/marsyas/marsyas) - Music Analysis, Retrieval, and Synthesis for Audio Signals.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mmuda[0m[38;5;12m (https://github.com/bmcfee/muda) - A library for augmenting annotated audio data.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mmadmom[0m[38;5;12m (https://github.com/CPJKU/madmom) - Python audio and music signal processing library.[39m

[38;2;255;187;0m[4mComputer Vision[0m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mtorchvision[0m[38;5;12m (https://github.com/pytorch/vision) - Datasets, Transforms, and Models specific to Computer Vision. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mPyTorch3D[0m[38;5;12m (https://github.com/facebookresearch/pytorch3d) - PyTorch3D is FAIR's library of reusable components for deep learning with 3D data. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mgluon-cv[0m[38;5;12m (https://github.com/dmlc/gluon-cv) - Provides implementations of the state-of-the-art  deep learning models in computer vision. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mKerasCV[0m[38;5;12m (https://github.com/keras-team/keras-cv) - Industry-strength Computer Vision workflows with Keras. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mOpenCV[0m[38;5;12m (https://github.com/opencv/opencv) - Open Source Computer Vision Library.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mDecord[0m[38;5;12m (https://github.com/dmlc/decord) - An efficient video loader for deep learning with smart shuffling that's super easy to digest.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mMMEngine[0m[38;5;12m (https://github.com/open-mmlab/mmengine) - OpenMMLab Foundational Library for Training Deep Learning Models. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mscikit-image[0m[38;5;12m (https://github.com/scikit-image/scikit-image) - Image Processing SciKit (Toolbox for SciPy).[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mimgaug[0m[38;5;12m (https://github.com/aleju/imgaug) - Image augmentation for machine learning experiments.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mimgaug_extension[0m[38;5;12m (https://github.com/cadenai/imgaug_extension) - Additional augmentations for imgaug.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mAugmentor[0m[38;5;12m (https://github.com/mdbloice/Augmentor) - Image augmentation library in Python for machine learning.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1malbumentations[0m[38;5;12m (https://github.com/albu/albumentations) - Fast image augmentation library and easy-to-use wrapper around other libraries.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mLAVIS[0m[38;5;12m (https://github.com/salesforce/LAVIS) - A One-stop Library for Language-Vision Intelligence.[39m

[38;2;255;187;0m[4mTime Series[0m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1msktime[0m[38;5;12m (https://github.com/alan-turing-institute/sktime) - A unified framework for machine learning with time series. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mdarts[0m[38;5;12m (https://github.com/unit8co/darts) - A python library for easy manipulation and forecasting of time series.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mstatsforecast[0m[38;5;12m (https://github.com/Nixtla/statsforecast) - Lightning fast forecasting with statistical and econometric models.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mmlforecast[0m[38;5;12m (https://github.com/Nixtla/mlforecast) - Scalable machine learning-based time series forecasting.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mneuralforecast[0m[38;5;12m (https://github.com/Nixtla/neuralforecast) - Scalable machine learning-based time series forecasting.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mtslearn[0m[38;5;12m (https://github.com/rtavenar/tslearn) - Machine learning toolkit dedicated to time-series data. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mtick[0m[38;5;12m (https://github.com/X-DataInitiative/tick) - Module for statistical learning, with a particular emphasis on time-dependent modeling.  [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mgreykite[0m[38;5;12m (https://github.com/linkedin/greykite) - A flexible, intuitive, and fast forecasting library next.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mProphet[0m[38;5;12m (https://github.com/facebook/prophet) - Automatic Forecasting Procedure.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mPyFlux[0m[38;5;12m (https://github.com/RJT1990/pyflux) - Open source time series library for Python.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mbayesloop[0m[38;5;12m (https://github.com/christophmark/bayesloop) - Probabilistic programming framework that facilitates objective model selection for time-varying parameter models.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mluminol[0m[38;5;12m (https://github.com/linkedin/luminol) - Anomaly Detection and Correlation library.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mdateutil[0m[38;5;12m (https://dateutil.readthedocs.io/en/stable/) - Powerful extensions to the standard datetime module[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mmaya[0m[38;5;12m (https://github.com/timofurrer/maya) - makes it very easy to parse a string and for changing timezones[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mChaos Genius[0m[38;5;12m (https://github.com/chaos-genius/chaos_genius) - ML powered analytics engine for outlier/anomaly detection and root cause analysis[39m

[38;2;255;187;0m[4mReinforcement Learning[0m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mGymnasium[0m[38;5;12m [39m[38;5;12m(https://github.com/Farama-Foundation/Gymnasium)[39m[38;5;12m [39m[38;5;12m-[39m[38;5;12m [39m[38;5;12mAn[39m[38;5;12m [39m[38;5;12mAPI[39m[38;5;12m [39m[38;5;12mstandard[39m[38;5;12m [39m[38;5;12mfor[39m[38;5;12m [39m[38;5;12msingle-agent[39m[38;5;12m [39m[38;5;12mreinforcement[39m[38;5;12m [39m[38;5;12mlearning[39m[38;5;12m [39m[38;5;12menvironments,[39m[38;5;12m [39m[38;5;12mwith[39m[38;5;12m [39m[38;5;12mpopular[39m[38;5;12m [39m[38;5;12mreference[39m[38;5;12m [39m[38;5;12menvironments[39m[38;5;12m [39m[38;5;12mand[39m[38;5;12m [39m[38;5;12mrelated[39m[38;5;12m [39m[38;5;12mutilities[39m[38;5;12m [39m[38;5;12m(formerly[39m[38;5;12m [39m[38;5;14m[1mGym[0m[38;5;12m [39m
[38;5;12m(https://github.com/openai/gym)).[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mPettingZoo[0m[38;5;12m (https://github.com/Farama-Foundation/PettingZoo) - An API standard for multi-agent reinforcement learning environments, with popular reference environments and related utilities.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mMAgent2[0m[38;5;12m (https://github.com/Farama-Foundation/MAgent2) - An engine for high performance multi-agent environments with very large numbers of agents, along with a set of reference environments.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mStable Baselines3[0m[38;5;12m (https://github.com/DLR-RM/stable-baselines3) - A set of improved implementations of reinforcement learning algorithms based on OpenAI Baselines.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mShimmy[0m[38;5;12m (https://github.com/Farama-Foundation/Shimmy) - An API conversion tool for popular external reinforcement learning environments.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mEnvPool[0m[38;5;12m (https://github.com/sail-sg/envpool) - C++-based high-performance parallel environment execution engine (vectorized env) for general RL environments.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mRLlib[0m[38;5;12m (https://ray.readthedocs.io/en/latest/rllib.html) - Scalable Reinforcement Learning.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mTianshou[0m[38;5;12m (https://github.com/thu-ml/tianshou/#comprehensive-functionality) - An elegant PyTorch deep reinforcement learning library. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mAcme[0m[38;5;12m (https://github.com/google-deepmind/acme) - A library of reinforcement learning components and agents.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mCatalyst-RL[0m[38;5;12m (https://github.com/catalyst-team/catalyst-rl) - PyTorch framework for RL research. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1md3rlpy[0m[38;5;12m (https://github.com/takuseno/d3rlpy) - An offline deep reinforcement learning library.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mDI-engine[0m[38;5;12m (https://github.com/opendilab/DI-engine) - OpenDILab Decision AI Engine. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mTF-Agents[0m[38;5;12m (https://github.com/tensorflow/agents) - A library for Reinforcement Learning in TensorFlow. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mTensorForce[0m[38;5;12m (https://github.com/reinforceio/tensorforce) - A TensorFlow library for applied reinforcement learning. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mTRFL[0m[38;5;12m (https://github.com/deepmind/trfl) - TensorFlow Reinforcement Learning. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mDopamine[0m[38;5;12m (https://github.com/google/dopamine) - A research framework for fast prototyping of reinforcement learning algorithms.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mkeras-rl[0m[38;5;12m (https://github.com/keras-rl/keras-rl) - Deep Reinforcement Learning for Keras. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mgarage[0m[38;5;12m (https://github.com/rlworkgroup/garage) - A toolkit for reproducible reinforcement learning research.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mHorizon[0m[38;5;12m (https://github.com/facebookresearch/Horizon) - A platform for Applied Reinforcement Learning.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mrlpyt[0m[38;5;12m (https://github.com/astooke/rlpyt) - Reinforcement Learning in PyTorch. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mcleanrl[0m[38;5;12m (https://github.com/vwxyzjn/cleanrl) - High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG).[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mMachin[0m[38;5;12m (https://github.com/iffiX/machin) -  A reinforcement library designed for pytorch. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mSKRL[0m[38;5;12m (https://github.com/Toni-SM/skrl) - Modular reinforcement learning library (on PyTorch and JAX) with support for NVIDIA Isaac Gym, Isaac Orbit and Omniverse Isaac Gym. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mImitation[0m[38;5;12m (https://github.com/HumanCompatibleAI/imitation) - Clean PyTorch implementations of imitation and reward learning algorithms. [39m

[38;2;255;187;0m[4mGraph Machine Learning[0m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mpytorch_geometric[0m[38;5;12m (https://github.com/rusty1s/pytorch_geometric) - Geometric Deep Learning Extension Library for PyTorch. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mpytorch_geometric_temporal[0m[38;5;12m (https://github.com/benedekrozemberczki/pytorch_geometric_temporal) - Temporal Extension Library for PyTorch Geometric. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mPyTorch Geometric Signed Directed[0m[38;5;12m (https://github.com/SherylHYX/pytorch_geometric_signed_directed) -  A signed/directed graph neural network extension library for PyTorch Geometric. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mdgl[0m[38;5;12m (https://github.com/dmlc/dgl) - Python package built to ease deep learning on graph, on top of existing DL frameworks. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mSpektral[0m[38;5;12m (https://github.com/danielegrattarola/spektral) - Deep learning on graphs. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mStellarGraph[0m[38;5;12m (https://github.com/stellargraph/stellargraph) - Machine Learning on Graphs. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mGraph Nets[0m[38;5;12m (https://github.com/google-deepmind/graph_nets) - Build Graph Nets in Tensorflow. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mTensorFlow GNN[0m[38;5;12m (https://github.com/tensorflow/gnn) - A library to build Graph Neural Networks on the TensorFlow platform. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mAuto Graph Learning[0m[38;5;12m (https://github.com/THUMNLab/AutoGL) -An autoML framework & toolkit for machine learning on graphs.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mPyTorch-BigGraph[0m[38;5;12m (https://github.com/facebookresearch/PyTorch-BigGraph) - Generate embeddings from large-scale graph-structured data. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mAuto Graph Learning[0m[38;5;12m (https://github.com/THUMNLab/AutoGL) - An autoML framework & toolkit for machine learning on graphs.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mKarate Club[0m[38;5;12m (https://github.com/benedekrozemberczki/karateclub) - An unsupervised machine learning library for graph-structured data.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mLittle Ball of Fur[0m[38;5;12m (https://github.com/benedekrozemberczki/littleballoffur) - A library for sampling graph structured data.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mGreatX[0m[38;5;12m (https://github.com/EdisonLeeeee/GreatX) - A graph reliability toolbox based on PyTorch and PyTorch Geometric (PyG). [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mJraph[0m[38;5;12m (https://github.com/google-deepmind/jraph) - A Graph Neural Network Library in Jax.[39m

[38;2;255;187;0m[4mLearning-to-Rank & Recommender Systems[0m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mLightFM[0m[38;5;12m (https://github.com/lyst/lightfm) - A Python implementation of LightFM, a hybrid recommendation algorithm.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mSpotlight[0m[38;5;12m (https://maciejkula.github.io/spotlight/) - Deep recommender models using PyTorch.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mSurprise[0m[38;5;12m (https://github.com/NicolasHug/Surprise) - A Python scikit for building and analyzing recommender systems.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mRecBole[0m[38;5;12m (https://github.com/RUCAIBox/RecBole) - A unified, comprehensive and efficient recommendation library. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mallRank[0m[38;5;12m (https://github.com/allegro/allRank) - allRank is a framework for training learning-to-rank neural models based on PyTorch. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mTensorFlow Recommenders[0m[38;5;12m (https://github.com/tensorflow/recommenders) - A library for building recommender system models using TensorFlow. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mTensorFlow Ranking[0m[38;5;12m (https://github.com/tensorflow/ranking) - Learning to Rank in TensorFlow.  [39m

[38;2;255;187;0m[4mProbabilistic Graphical Models[0m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mpomegranate[0m[38;5;12m (https://github.com/jmschrei/pomegranate) - Probabilistic and graphical models for Python. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mpgmpy[0m[38;5;12m (https://github.com/pgmpy/pgmpy) - A python library for working with Probabilistic Graphical Models.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mpyAgrum[0m[38;5;12m (https://agrum.gitlab.io/) - A GRaphical Universal Modeler.[39m

[38;2;255;187;0m[4mProbabilistic Methods[0m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mpyro[0m[38;5;12m (https://github.com/uber/pyro) - A flexible, scalable deep probabilistic programming library built on PyTorch. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mPyMC[0m[38;5;12m (https://github.com/pymc-devs/pymc) - Bayesian Stochastic Modelling in Python.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mZhuSuan[0m[38;5;12m (http://zhusuan.readthedocs.io/en/latest/) - Bayesian Deep Learning. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mGPflow[0m[38;5;12m (http://gpflow.readthedocs.io/en/latest/?badge=latest) - Gaussian processes in TensorFlow. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mInferPy[0m[38;5;12m (https://github.com/PGM-Lab/InferPy) - Deep Probabilistic Modelling Made Easy.  [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mPyStan[0m[38;5;12m (https://github.com/stan-dev/pystan) - Bayesian inference using the No-U-Turn sampler (Python interface).[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1msklearn-bayes[0m[38;5;12m (https://github.com/AmazaspShumik/sklearn-bayes) - Python package for Bayesian Machine Learning with scikit-learn API. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mskpro[0m[38;5;12m (https://github.com/alan-turing-institute/skpro) - Supervised domain-agnostic prediction framework for probabilistic modelling by [39m[38;5;14m[1mThe Alan Turing Institute[0m[38;5;12m (https://www.turing.ac.uk/). [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mPyVarInf[0m[38;5;12m (https://github.com/ctallec/pyvarinf) - Bayesian Deep Learning methods with Variational Inference for PyTorch. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1memcee[0m[38;5;12m (https://github.com/dfm/emcee) - The Python ensemble sampling toolkit for affine-invariant MCMC.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mhsmmlearn[0m[38;5;12m (https://github.com/jvkersch/hsmmlearn) - A library for hidden semi-Markov models with explicit durations.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mpyhsmm[0m[38;5;12m (https://github.com/mattjj/pyhsmm) - Bayesian inference in HSMMs and HMMs.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mGPyTorch[0m[38;5;12m (https://github.com/cornellius-gp/gpytorch) - A highly efficient and modular implementation of Gaussian Processes in PyTorch. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1msklearn-crfsuite[0m[38;5;12m (https://github.com/TeamHG-Memex/sklearn-crfsuite) - A scikit-learn-inspired API for CRFsuite. [39m

[38;2;255;187;0m[4mModel Explanation[0m

[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mdalex[0m[38;5;12m (https://github.com/ModelOriented/DALEX) - moDel Agnostic Language for Exploration and explanation. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mShapley[0m[38;5;12m (https://github.com/benedekrozemberczki/shapley) - A data-driven framework to quantify the value of classifiers in a machine learning ensemble.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mAlibi[0m[38;5;12m (https://github.com/SeldonIO/alibi) - Algorithms for monitoring and explaining machine learning models.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1manchor[0m[38;5;12m (https://github.com/marcotcr/anchor) - Code for "High-Precision Model-Agnostic Explanations" paper.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1maequitas[0m[38;5;12m (https://github.com/dssg/aequitas) - Bias and Fairness Audit Toolkit.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mContrastive Explanation[0m[38;5;12m (https://github.com/MarcelRobeer/ContrastiveExplanation) - Contrastive Explanation (Foil Trees). [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1myellowbrick[0m[38;5;12m (https://github.com/DistrictDataLabs/yellowbrick) - Visual analysis and diagnostic tools to facilitate machine learning model selection. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mscikit-plot[0m[38;5;12m (https://github.com/reiinakano/scikit-plot) - An intuitive library to add plotting functionality to scikit-learn objects. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mshap[0m[38;5;12m (https://github.com/slundberg/shap) - A unified approach to explain the output of any machine learning model. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mELI5[0m[38;5;12m (https://github.com/TeamHG-Memex/eli5) - A library for debugging/inspecting machine learning classifiers and explaining their predictions.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mLime[0m[38;5;12m (https://github.com/marcotcr/lime) - Explaining the predictions of any machine learning classifier. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mFairML[0m[38;5;12m (https://github.com/adebayoj/fairml) - FairML is a python toolbox auditing the machine learning models for bias. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mL2X[0m[38;5;12m (https://github.com/Jianbo-Lab/L2X) - Code for replicating the experiments in the paper [39m[48;2;30;30;40m[38;5;13m[3mLearning to Explain: An Information-Theoretic Perspective on Model Interpretation[0m[38;5;12m.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mPDPbox[0m[38;5;12m (https://github.com/SauceCat/PDPbox) - Partial dependence plot toolbox.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mPyCEbox[0m[38;5;12m (https://github.com/AustinRochford/PyCEbox) - Python Individual Conditional Expectation Plot Toolbox.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mSkater[0m[38;5;12m (https://github.com/datascienceinc/Skater) - Python Library for Model Interpretation.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mmodel-analysis[0m[38;5;12m (https://github.com/tensorflow/model-analysis) - Model analysis tools for TensorFlow. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mthemis-ml[0m[38;5;12m (https://github.com/cosmicBboy/themis-ml) - A library that implements fairness-aware machine learning algorithms. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mtreeinterpreter[0m[38;5;12m (https://github.com/andosa/treeinterpreter) - Interpreting scikit-learn's decision tree and random forest predictions. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mAI Explainability 360[0m[38;5;12m (https://github.com/IBM/AIX360) - Interpretability and explainability of data and machine learning models.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mAuralisation[0m[38;5;12m (https://github.com/keunwoochoi/Auralisation) - Auralisation of learned features in CNN (for audio).[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mCapsNet-Visualization[0m[38;5;12m (https://github.com/bourdakos1/CapsNet-Visualization) - A visualization of the CapsNet layers to better understand how it works.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mlucid[0m[38;5;12m (https://github.com/tensorflow/lucid) - A collection of infrastructure and tools for research in neural network interpretability.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mNetron[0m[38;5;12m (https://github.com/lutzroeder/Netron) - Visualizer for deep learning and machine learning models (no Python code, but visualizes models from most Python Deep Learning frameworks).[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mFlashLight[0m[38;5;12m (https://github.com/dlguys/flashlight) - Visualization Tool for your NeuralNetwork.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mtensorboard-pytorch[0m[38;5;12m (https://github.com/lanpa/tensorboard-pytorch) - Tensorboard for PyTorch (and chainer, mxnet, numpy, ...).[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mmxboard[0m[38;5;12m (https://github.com/awslabs/mxboard) - Logging MXNet data for visualization in TensorBoard. [39m

[38;2;255;187;0m[4mGenetic Programming[0m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mgplearn[0m[38;5;12m (https://github.com/trevorstephens/gplearn) - Genetic Programming in Python. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mDEAP[0m[38;5;12m (https://github.com/DEAP/deap) - Distributed Evolutionary Algorithms in Python.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mkaroo_gp[0m[38;5;12m (https://github.com/kstaats/karoo_gp) - A Genetic Programming platform for Python with GPU support. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mmonkeys[0m[38;5;12m (https://github.com/hchasestevens/monkeys) - A strongly-typed genetic programming framework for Python.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1msklearn-genetic[0m[38;5;12m (https://github.com/manuel-calzolari/sklearn-genetic) - Genetic feature selection module for scikit-learn. [39m


[38;2;255;187;0m[4mOptimization[0m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mOptuna[0m[38;5;12m (https://github.com/optuna/optuna) - A hyperparameter optimization framework.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mSpearmint[0m[38;5;12m (https://github.com/HIPS/Spearmint) - Bayesian optimization.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mBoTorch[0m[38;5;12m (https://github.com/pytorch/botorch) - Bayesian optimization in PyTorch. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mscikit-opt[0m[38;5;12m (https://github.com/guofei9987/scikit-opt) - Heuristic Algorithms for optimization.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1msklearn-genetic-opt[0m[38;5;12m (https://github.com/rodrigo-arenas/Sklearn-genetic-opt) - Hyperparameters tuning and feature selection using evolutionary algorithms. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mSMAC3[0m[38;5;12m (https://github.com/automl/SMAC3) - Sequential Model-based Algorithm Configuration.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mOptunity[0m[38;5;12m (https://github.com/claesenm/optunity) - Is a library containing various optimizers for hyperparameter tuning.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mhyperopt[0m[38;5;12m (https://github.com/hyperopt/hyperopt) - Distributed Asynchronous Hyperparameter Optimization in Python.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mhyperopt-sklearn[0m[38;5;12m (https://github.com/hyperopt/hyperopt-sklearn) - Hyper-parameter optimization for sklearn. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1msklearn-deap[0m[38;5;12m (https://github.com/rsteca/sklearn-deap) - Use evolutionary algorithms instead of gridsearch in scikit-learn. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1msigopt_sklearn[0m[38;5;12m (https://github.com/sigopt/sigopt_sklearn) - SigOpt wrappers for scikit-learn methods. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mBayesian Optimization[0m[38;5;12m (https://github.com/fmfn/BayesianOptimization) - A Python implementation of global optimization with gaussian processes.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mSafeOpt[0m[38;5;12m (https://github.com/befelix/SafeOpt) - Safe Bayesian Optimization.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mscikit-optimize[0m[38;5;12m (https://github.com/scikit-optimize/scikit-optimize) - Sequential model-based optimization with a [39m[48;5;235m[38;5;249mscipy.optimize[49m[39m[38;5;12m interface.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mSolid[0m[38;5;12m (https://github.com/100/Solid) - A comprehensive gradient-free optimization framework written in Python.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mPySwarms[0m[38;5;12m (https://github.com/ljvmiranda921/pyswarms) - A research toolkit for particle swarm optimization in Python.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mPlatypus[0m[38;5;12m (https://github.com/Project-Platypus/Platypus) - A Free and Open Source Python Library for Multiobjective Optimization.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mGPflowOpt[0m[38;5;12m (https://github.com/GPflow/GPflowOpt) - Bayesian Optimization using GPflow. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mPOT[0m[38;5;12m (https://github.com/rflamary/POT) - Python Optimal Transport library.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mTalos[0m[38;5;12m (https://github.com/autonomio/talos) - Hyperparameter Optimization for Keras Models.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mnlopt[0m[38;5;12m (https://github.com/stevengj/nlopt) - Library for nonlinear optimization (global and local, constrained or unconstrained).[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mOR-Tools[0m[38;5;12m (https://developers.google.com/optimization) - An open-source software suite for optimization by Google; provides a unified programming interface to a half dozen solvers: SCIP, GLPK, GLOP, CP-SAT, CPLEX, and Gurobi.[39m

[38;2;255;187;0m[4mFeature Engineering[0m

[38;2;255;187;0m[4mGeneral[0m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mFeaturetools[0m[38;5;12m (https://github.com/Featuretools/featuretools) - Automated feature engineering.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mFeature Engine[0m[38;5;12m (https://github.com/feature-engine/feature_engine) - Feature engineering package with sklearn-like functionality. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mOpenFE[0m[38;5;12m (https://github.com/IIIS-Li-Group/OpenFE) - Automated feature generation with expert-level performance.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mskl-groups[0m[38;5;12m (https://github.com/dougalsutherland/skl-groups) - A scikit-learn addon to operate on set/"group"-based features. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mFeature Forge[0m[38;5;12m (https://github.com/machinalis/featureforge) - A set of tools for creating and testing machine learning features. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mfew[0m[38;5;12m (https://github.com/lacava/few) - A feature engineering wrapper for sklearn. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mscikit-mdr[0m[38;5;12m (https://github.com/EpistasisLab/scikit-mdr) - A sklearn-compatible Python implementation of Multifactor Dimensionality Reduction (MDR) for feature construction. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mtsfresh[0m[38;5;12m (https://github.com/blue-yonder/tsfresh) - Automatic extraction of relevant features from time series. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mdirty_cat[0m[38;5;12m (https://github.com/dirty-cat/dirty_cat) - Machine learning on dirty tabular data (especially: string-based variables for classifcation and regression). [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mNitroFE[0m[38;5;12m (https://github.com/NITRO-AI/NitroFE) - Moving window features. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1msk-transformer[0m[38;5;12m (https://github.com/chrislemke/sk-transformers) - A collection of various pandas & scikit-learn compatible transformers for all kinds of preprocessing and feature engineering steps [39m


[38;2;255;187;0m[4mFeature Selection[0m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mscikit-feature[0m[38;5;12m (https://github.com/jundongl/scikit-feature) - Feature selection repository in Python.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mboruta_py[0m[38;5;12m (https://github.com/scikit-learn-contrib/boruta_py) - Implementations of the Boruta all-relevant feature selection method. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mBoostARoota[0m[38;5;12m (https://github.com/chasedehan/BoostARoota) - A fast xgboost feature selection algorithm. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mscikit-rebate[0m[38;5;12m (https://github.com/EpistasisLab/scikit-rebate) - A scikit-learn-compatible Python implementation of ReBATE, a suite of Relief-based feature selection algorithms for Machine Learning. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mzoofs[0m[38;5;12m (https://github.com/jaswinder9051998/zoofs) - A feature selection library based on evolutionary algorithms.[39m

[38;2;255;187;0m[4mVisualization[0m
[38;2;255;187;0m[4mGeneral Purposes[0m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mMatplotlib[0m[38;5;12m (https://github.com/matplotlib/matplotlib) - Plotting with Python.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mseaborn[0m[38;5;12m (https://github.com/mwaskom/seaborn) - Statistical data visualization using matplotlib.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mprettyplotlib[0m[38;5;12m (https://github.com/olgabot/prettyplotlib) - Painlessly create beautiful matplotlib plots.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mpython-ternary[0m[38;5;12m (https://github.com/marcharper/python-ternary) - Ternary plotting library for Python with matplotlib.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mmissingno[0m[38;5;12m (https://github.com/ResidentMario/missingno) - Missing data visualization module for Python.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mchartify[0m[38;5;12m (https://github.com/spotify/chartify/) - Python library that makes it easy for data scientists to create charts.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mphyst[0m[38;5;12m (https://github.com/janpipek/physt) - Improved histograms.[39m
[38;2;255;187;0m[4mInteractive plots[0m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1manimatplot[0m[38;5;12m (https://github.com/t-makaro/animatplot) - A python package for animating plots built on matplotlib.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mplotly[0m[38;5;12m (https://plot.ly/python/) - A Python library that makes interactive and publication-quality graphs.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mBokeh[0m[38;5;12m (https://github.com/bokeh/bokeh) - Interactive Web Plotting for Python.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mAltair[0m[38;5;12m (https://altair-viz.github.io/) - Declarative statistical visualization library for Python. Can easily do many data transformation within the code to create graph[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mbqplot[0m[38;5;12m (https://github.com/bqplot/bqplot) - Plotting library for IPython/Jupyter notebooks[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mpyecharts[0m[38;5;12m (https://github.com/pyecharts/pyecharts) - Migrated from [39m[38;5;14m[1mEcharts[0m[38;5;12m (https://github.com/apache/echarts), a charting and visualization library, to Python's interactive visual drawing library.[39m
[38;2;255;187;0m[4mMap[0m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mfolium[0m[38;5;12m (https://python-visualization.github.io/folium/quickstart.html#Getting-Started) - Makes it easy to visualize data on an interactive open street map[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mgeemap[0m[38;5;12m (https://github.com/giswqs/geemap) - Python package for interactive mapping with Google Earth Engine (GEE)[39m
[38;2;255;187;0m[4mAutomatic Plotting[0m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mHoloViews[0m[38;5;12m (https://github.com/ioam/holoviews) - Stop plotting your data - annotate your data and let it visualize itself.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mAutoViz[0m[38;5;12m (https://github.com/AutoViML/AutoViz): Visualize data automatically with 1 line of code (ideal for machine learning)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mSweetViz[0m[38;5;12m (https://github.com/fbdesignpro/sweetviz): Visualize and compare datasets, target values and associations, with one line of code.[39m

[38;2;255;187;0m[4mNLP[0m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mpyLDAvis[0m[38;5;12m (https://github.com/bmabey/pyLDAvis): Visualize interactive topic model[39m

[38;2;255;187;0m[4mDeployment[0m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mfastapi[0m[38;5;12m (https://fastapi.tiangolo.com/) - Modern, fast (high-performance), a web framework for building APIs with Python[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mstreamlit[0m[38;5;12m (https://www.streamlit.io/) - Make it easy to deploy the machine learning model[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mstreamsync[0m[38;5;12m (https://github.com/streamsync-cloud/streamsync) - No-code in the front, Python in the back. An open-source framework for creating data apps.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mgradio[0m[38;5;12m (https://github.com/gradio-app/gradio) - Create UIs for your machine learning model in Python in 3 minutes.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mVizro[0m[38;5;12m (https://github.com/mckinsey/vizro) - A toolkit for creating modular data visualization applications.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mdatapane[0m[38;5;12m (https://datapane.com/) - A collection of APIs to turn scripts and notebooks into interactive reports.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mbinder[0m[38;5;12m (https://mybinder.org/) - Enable sharing and execute Jupyter Notebooks[39m


[38;2;255;187;0m[4mStatistics[0m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mpandas_summary[0m[38;5;12m (https://github.com/mouradmourafiq/pandas-summary) - Extension to pandas dataframes describe function. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mPandas Profiling[0m[38;5;12m (https://github.com/pandas-profiling/pandas-profiling) - Create HTML profiling reports from pandas DataFrame objects. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mstatsmodels[0m[38;5;12m (https://github.com/statsmodels/statsmodels) - Statistical modeling and econometrics in Python.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mstockstats[0m[38;5;12m (https://github.com/jealous/stockstats) - Supply a wrapper [39m[38;5;12mStockDataFrame[39m[38;5;12m based on the [39m[38;5;12mpandas.DataFrame[39m[38;5;12m with inline stock statistics/indicators support.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mweightedcalcs[0m[38;5;12m (https://github.com/jsvine/weightedcalcs) - A pandas-based utility to calculate weighted means, medians, distributions, standard deviations, and more.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mscikit-posthocs[0m[38;5;12m (https://github.com/maximtrp/scikit-posthocs) - Pairwise Multiple Comparisons Post-hoc Tests.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mAlphalens[0m[38;5;12m (https://github.com/quantopian/alphalens) - Performance analysis of predictive (alpha) stock factors.[39m


[38;2;255;187;0m[4mData Manipulation[0m

[38;2;255;187;0m[4mData Frames[0m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mpandas[0m[38;5;12m (https://pandas.pydata.org/pandas-docs/stable/) - Powerful Python data analysis toolkit.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mpolars[0m[38;5;12m (https://github.com/pola-rs/polars) - A fast multi-threaded, hybrid-out-of-core DataFrame library.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mArctic[0m[38;5;12m (https://github.com/manahl/arctic) - High-performance datastore for time series and tick data.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mdatatable[0m[38;5;12m (https://github.com/h2oai/datatable) - Data.table for Python. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mpandas_profiling[0m[38;5;12m (https://github.com/pandas-profiling/pandas-profiling) - Create HTML profiling reports from pandas DataFrame objects[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mcuDF[0m[38;5;12m (https://github.com/rapidsai/cudf) - GPU DataFrame Library. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mblaze[0m[38;5;12m (https://github.com/blaze/blaze) - NumPy and pandas interface to Big Data. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mpandasql[0m[38;5;12m (https://github.com/yhat/pandasql) -  Allows you to query pandas DataFrames using SQL syntax. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mpandas-gbq[0m[38;5;12m (https://github.com/pydata/pandas-gbq) - pandas Google Big Query. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mxpandas[0m[38;5;12m (https://github.com/alan-turing-institute/xpandas) - Universal 1d/2d data containers with Transformers .functionality for data analysis by [39m[38;5;14m[1mThe Alan Turing Institute[0m[38;5;12m (https://www.turing.ac.uk/).[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mpysparkling[0m[38;5;12m (https://github.com/svenkreiss/pysparkling) - A pure Python implementation of Apache Spark's RDD and DStream interfaces. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mmodin[0m[38;5;12m (https://github.com/modin-project/modin) - Speed up your pandas workflows by changing a single line of code. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mswifter[0m[38;5;12m (https://github.com/jmcarpenter2/swifter) - A package that efficiently applies any function to a pandas dataframe or series in the fastest available manner.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mpandas-log[0m[38;5;12m (https://github.com/eyaltrabelsi/pandas-log) - A package that allows providing feedback about basic pandas operations and finds both business logic and performance issues.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mvaex[0m[38;5;12m (https://github.com/vaexio/vaex) - Out-of-Core DataFrames for Python, ML, visualize and explore big tabular data at a billion rows per second.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mxarray[0m[38;5;12m [39m[38;5;12m(https://github.com/pydata/xarray)[39m[38;5;12m [39m[38;5;12m-[39m[38;5;12m [39m[38;5;12mXarray[39m[38;5;12m [39m[38;5;12mcombines[39m[38;5;12m [39m[38;5;12mthe[39m[38;5;12m [39m[38;5;12mbest[39m[38;5;12m [39m[38;5;12mfeatures[39m[38;5;12m [39m[38;5;12mof[39m[38;5;12m [39m[38;5;12mNumPy[39m[38;5;12m [39m[38;5;12mand[39m[38;5;12m [39m[38;5;12mpandas[39m[38;5;12m [39m[38;5;12mfor[39m[38;5;12m [39m[38;5;12mmultidimensional[39m[38;5;12m [39m[38;5;12mdata[39m[38;5;12m [39m[38;5;12mselection[39m[38;5;12m [39m[38;5;12mby[39m[38;5;12m [39m[38;5;12msupplementing[39m[38;5;12m [39m[38;5;12mnumerical[39m[38;5;12m [39m[38;5;12maxis[39m[38;5;12m [39m[38;5;12mlabels[39m[38;5;12m [39m[38;5;12mwith[39m[38;5;12m [39m[38;5;12mnamed[39m[38;5;12m [39m[38;5;12mdimensions[39m[38;5;12m [39m[38;5;12mfor[39m[38;5;12m [39m[38;5;12mmore[39m[38;5;12m [39m[38;5;12mintuitive,[39m[38;5;12m [39m[38;5;12mconcise,[39m[38;5;12m [39m[38;5;12mand[39m[38;5;12m [39m[38;5;12mless[39m[38;5;12m [39m
[38;5;12merror-prone[39m[38;5;12m [39m[38;5;12mindexing[39m[38;5;12m [39m[38;5;12mroutines.[39m

[38;2;255;187;0m[4mPipelines[0m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mpdpipe[0m[38;5;12m (https://github.com/shaypal5/pdpipe) - Sasy pipelines for pandas DataFrames.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mSSPipe[0m[38;5;12m (https://sspipe.github.io/) - Python pipe (|) operator with support for DataFrames and Numpy, and Pytorch.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mpandas-ply[0m[38;5;12m (https://github.com/coursera/pandas-ply) - Functional data manipulation for pandas. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mDplython[0m[38;5;12m (https://github.com/dodger487/dplython) - Dplyr for Python. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1msklearn-pandas[0m[38;5;12m (https://github.com/scikit-learn-contrib/sklearn-pandas) - pandas integration with sklearn. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mDataset[0m[38;5;12m (https://github.com/analysiscenter/dataset) - Helps you conveniently work with random or sequential batches of your data and define data processing.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mpyjanitor[0m[38;5;12m (https://github.com/ericmjl/pyjanitor) - Clean APIs for data cleaning. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mmeza[0m[38;5;12m (https://github.com/reubano/meza) - A Python toolkit for processing tabular data.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mProdmodel[0m[38;5;12m (https://github.com/prodmodel/prodmodel) - Build system for data science pipelines.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mdopanda[0m[38;5;12m (https://github.com/dovpanda-dev/dovpanda) -  Hints and tips for using pandas in an analysis environment. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mHamilton[0m[38;5;12m (https://github.com/DAGWorks-Inc/hamilton) - A microframework for dataframe generation that applies Directed Acyclic Graphs specified by a flow of lazily evaluated Python functions.[39m

[38;2;255;187;0m[4mData-centric AI[0m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mcleanlab[0m[38;5;12m (https://github.com/cleanlab/cleanlab) - The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1msnorkel[0m[38;5;12m (https://github.com/snorkel-team/snorkel) - A system for quickly generating training data with weak supervision.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mdataprep[0m[38;5;12m (https://github.com/sfu-db/dataprep) - Collect, clean, and visualize your data in Python with a few lines of code.[39m

[38;2;255;187;0m[4mSynthetic Data[0m

[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mydata-synthetic[0m[38;5;12m (https://github.com/ydataai/ydata-synthetic) - A package to generate synthetic tabular and time-series data leveraging the state-of-the-art generative models. [39m

[38;2;255;187;0m[4mDistributed Computing[0m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mHorovod[0m[38;5;12m (https://github.com/uber/horovod) - Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mPySpark[0m[38;5;12m (https://spark.apache.org/docs/0.9.0/python-programming-guide.html) - Exposes the Spark programming model to Python. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mVeles[0m[38;5;12m (https://github.com/Samsung/veles) - Distributed machine learning platform.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mJubatus[0m[38;5;12m (https://github.com/jubatus/jubatus) - Framework and Library for Distributed Online Machine Learning.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mDMTK[0m[38;5;12m (https://github.com/Microsoft/DMTK) - Microsoft Distributed Machine Learning Toolkit.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mPaddlePaddle[0m[38;5;12m (https://github.com/PaddlePaddle/Paddle) - PArallel Distributed Deep LEarning.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mdask-ml[0m[38;5;12m (https://github.com/dask/dask-ml) - Distributed and parallel machine learning. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mDistributed[0m[38;5;12m (https://github.com/dask/distributed) - Distributed computation in Python.[39m

[38;2;255;187;0m[4mExperimentation[0m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mmlflow[0m[38;5;12m (https://github.com/mlflow/mlflow) - Open source platform for the machine learning lifecycle.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mNeptune[0m[38;5;12m (https://neptune.ai) - A lightweight ML experiment tracking, results visualization, and management tool.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mdvc[0m[38;5;12m (https://github.com/iterative/dvc) - Data Version Control | Git for Data & Models | ML Experiments Management.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1menvd[0m[38;5;12m (https://github.com/tensorchord/envd) - 🏕️ machine learning development environment for data science and AI/ML engineering teams.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mSacred[0m[38;5;12m (https://github.com/IDSIA/sacred) - A tool to help you configure, organize, log, and reproduce experiments.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mAx[0m[38;5;12m (https://github.com/facebook/Ax) - Adaptive Experimentation Platform. [39m

[38;2;255;187;0m[4mData Validation[0m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mgreat_expectations[0m[38;5;12m (https://github.com/great-expectations/great_expectations) - Always know what to expect from your data.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mpandera[0m[38;5;12m (https://github.com/unionai-oss/pandera) - A lightweight, flexible, and expressive statistical data testing library.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mdeepchecks[0m[38;5;12m (https://github.com/deepchecks/deepchecks) - Validation & testing of ML models and data during model development, deployment, and production. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mevidently[0m[38;5;12m (https://github.com/evidentlyai/evidently) - Evaluate and monitor ML models from validation to production.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mTensorFlow Data Validation[0m[38;5;12m (https://github.com/tensorflow/data-validation) - Library for exploring and validating machine learning data.[39m

[38;2;255;187;0m[4mEvaluation[0m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mrecmetrics[0m[38;5;12m (https://github.com/statisticianinstilettos/recmetrics) - Library of useful metrics and plots for evaluating recommender systems.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mMetrics[0m[38;5;12m (https://github.com/benhamner/Metrics) - Machine learning evaluation metric.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1msklearn-evaluation[0m[38;5;12m (https://github.com/edublancas/sklearn-evaluation) - Model evaluation made easy: plots, tables, and markdown reports. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mAI Fairness 360[0m[38;5;12m (https://github.com/IBM/AIF360) - Fairness metrics for datasets and ML models, explanations, and algorithms to mitigate bias in datasets and models.[39m

[38;2;255;187;0m[4mComputations[0m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mnumpy[0m[38;5;12m (http://www.numpy.org/) - The fundamental package needed for scientific computing with Python.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mDask[0m[38;5;12m (https://github.com/dask/dask) - Parallel computing with task scheduling. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mbottleneck[0m[38;5;12m (https://github.com/kwgoodman/bottleneck) - Fast NumPy array functions written in C.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mCuPy[0m[38;5;12m (https://github.com/cupy/cupy) - NumPy-like API accelerated with CUDA.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mscikit-tensor[0m[38;5;12m (https://github.com/mnick/scikit-tensor) - Python library for multilinear algebra and tensor factorizations.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mnumdifftools[0m[38;5;12m (https://github.com/pbrod/numdifftools) - Solve automatic numerical differentiation problems in one or more variables.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mquaternion[0m[38;5;12m (https://github.com/moble/quaternion) - Add built-in support for quaternions to numpy.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1madaptive[0m[38;5;12m (https://github.com/python-adaptive/adaptive) - Tools for adaptive and parallel samping of mathematical functions.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mNumExpr[0m[38;5;12m (https://github.com/pydata/numexpr) - A fast numerical expression evaluator for NumPy that comes with an integrated computing virtual machine to speed calculations up by avoiding memory allocation for intermediate results.[39m

[38;2;255;187;0m[4mWeb Scraping[0m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mBeautifulSoup[0m[38;5;12m (https://www.crummy.com/software/BeautifulSoup/bs4/doc/): The easiest library to scrape static websites for beginners[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mScrapy[0m[38;5;12m (https://scrapy.org/): Fast and extensible scraping library. Can write rules and create customized scraper without touching the core[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mSelenium[0m[38;5;12m (https://selenium-python.readthedocs.io/installation.html#introduction): Use Selenium Python API to access all functionalities of Selenium WebDriver in an intuitive way like a real user.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mPattern[0m[38;5;12m (https://github.com/clips/pattern): High level scraping for well-establish websites such as Google, Twitter, and Wikipedia. Also has NLP, machine learning algorithms, and visualization[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mtwitterscraper[0m[38;5;12m (https://github.com/taspinar/twitterscraper): Efficient library to scrape Twitter[39m

[38;2;255;187;0m[4mSpatial Analysis[0m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mGeoPandas[0m[38;5;12m (https://github.com/geopandas/geopandas) - Python tools for geographic data. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mPySal[0m[38;5;12m (https://github.com/pysal/pysal) - Python Spatial Analysis Library.[39m

[38;2;255;187;0m[4mQuantum Computing[0m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mqiskit[0m[38;5;12m (https://github.com/Qiskit/qiskit) - Qiskit is an open-source SDK for working with quantum computers at the level of circuits, algorithms, and application modules.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mcirq[0m[38;5;12m (https://github.com/quantumlib/Cirq) - A python framework for creating, editing, and invoking Noisy Intermediate Scale Quantum (NISQ) circuits.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mPennyLane[0m[38;5;12m (https://github.com/XanaduAI/pennylane) - Quantum machine learning, automatic differentiation, and optimization of hybrid quantum-classical computations.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mQML[0m[38;5;12m (https://github.com/qmlcode/qml) - A Python Toolkit for Quantum Machine Learning.[39m

[38;2;255;187;0m[4mConversion[0m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1msklearn-porter[0m[38;5;12m (https://github.com/nok/sklearn-porter) - Transpile trained scikit-learn estimators to C, Java, JavaScript, and others.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mONNX[0m[38;5;12m (https://github.com/onnx/onnx) - Open Neural Network Exchange.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mMMdnn[0m[38;5;12m (https://github.com/Microsoft/MMdnn) -  A set of tools to help users inter-operate among different deep learning frameworks.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mtreelite[0m[38;5;12m (https://github.com/dmlc/treelite) - Universal model exchange and serialization format for decision tree forests.[39m

[38;2;255;187;0m[4mContributing[0m
[38;5;12mContributions are welcome! :sunglasses: [39m
[38;5;12mRead the .[39m

[38;2;255;187;0m[4mLicense[0m
[38;5;12mThis work is licensed under the Creative Commons Attribution 4.0 International License - [39m[38;5;14m[1mCC BY 4.0[0m[38;5;12m (https://creativecommons.org/licenses/by/4.0/)[39m
