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Jonas Zeunert
2025-07-18 22:22:32 +02:00
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- Deep Learning (#deep-learning)
- **PyTorch** (#pytorch) 
- **TensorFlow** (#tensorflow)
- **MXNet** (#mxnet) 
 - JAX (#jax)
- **Others** (#others)
- Automated Machine Learning (#automated-machine-learning)
@@ -156,12 +155,6 @@
⟡ Elephas (https://github.com/maxpumperla/elephas) - Distributed Deep learning with Keras & Spark. 
⟡ qkeras (https://github.com/google/qkeras) - A quantization deep learning library. 
MXNet
⟡ MXNet (https://github.com/apache/incubator-mxnet) - Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler. 
⟡ Gluon (https://github.com/gluon-api/gluon-api) - A clear, concise, simple yet powerful and efficient API for deep learning (now included in MXNet). 
⟡ Xfer (https://github.com/amzn/xfer) - Transfer Learning library for Deep Neural Networks. 
⟡ MXNet (https://github.com/ROCmSoftwarePlatform/mxnet) - HIP Port of MXNet. 
JAX
⟡ JAX (https://github.com/google/jax) - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more.
⟡ FLAX (https://github.com/google/flax) - A neural network library for JAX that is designed for flexibility.
@@ -184,7 +177,6 @@
Natural Language Processing
⟡ torchtext (https://github.com/pytorch/text) - Data loaders and abstractions for text and NLP. 
⟡ gluon-nlp (https://github.com/dmlc/gluon-nlp) - NLP made easy. 
⟡ KerasNLP (https://github.com/keras-team/keras-nlp) - Modular Natural Language Processing workflows with Keras. 
⟡ spaCy (https://spacy.io/) - Industrial-Strength Natural Language Processing.
⟡ NLTK (https://github.com/nltk/nltk) - Modules, data sets, and tutorials supporting research and development in Natural Language Processing.
@@ -209,7 +201,6 @@
Computer Vision
⟡ torchvision (https://github.com/pytorch/vision) - Datasets, Transforms, and Models specific to Computer Vision. 
⟡ PyTorch3D (https://github.com/facebookresearch/pytorch3d) - PyTorch3D is FAIR's library of reusable components for deep learning with 3D data. 
⟡ gluon-cv (https://github.com/dmlc/gluon-cv) - Provides implementations of the state-of-the-art deep learning models in computer vision. 
⟡ KerasCV (https://github.com/keras-team/keras-cv) - Industry-strength Computer Vision workflows with Keras. 
⟡ OpenCV (https://github.com/opencv/opencv) - Open Source Computer Vision Library.
⟡ Decord (https://github.com/dmlc/decord) - An efficient video loader for deep learning with smart shuffling that's super easy to digest.
@@ -223,6 +214,7 @@
Time Series
⟡ sktime (https://github.com/alan-turing-institute/sktime) - A unified framework for machine learning with time series. 
⟡ skforecast (https://github.com/JoaquinAmatRodrigo/skforecast) - Time series forecasting with machine learning models
⟡ darts (https://github.com/unit8co/darts) - A python library for easy manipulation and forecasting of time series.
⟡ statsforecast (https://github.com/Nixtla/statsforecast) - Lightning fast forecasting with statistical and econometric models.
⟡ mlforecast (https://github.com/Nixtla/mlforecast) - Scalable machine learning-based time series forecasting.
@@ -239,8 +231,7 @@
⟡ Chaos Genius (https://github.com/chaos-genius/chaos_genius) - ML powered analytics engine for outlier/anomaly detection and root cause analysis
Reinforcement Learning
⟡ Gymnasium (https://github.com/Farama-Foundation/Gymnasium) - An API standard for single-agent reinforcement learning environments, with popular reference environments and related utilities (formerly Gym 
(https://github.com/openai/gym)).
⟡ Gymnasium (https://github.com/Farama-Foundation/Gymnasium) - An API standard for single-agent reinforcement learning environments, with popular reference environments and related utilities (formerly Gym (https://github.com/openai/gym)).
⟡ PettingZoo (https://github.com/Farama-Foundation/PettingZoo) - An API standard for multi-agent reinforcement learning environments, with popular reference environments and related utilities.
⟡ MAgent2 (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.
⟡ Stable Baselines3 (https://github.com/DLR-RM/stable-baselines3) - A set of improved implementations of reinforcement learning algorithms based on OpenAI Baselines.
@@ -340,10 +331,10 @@
⟡ Netron (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).
⟡ FlashLight (https://github.com/dlguys/flashlight) - Visualization Tool for your NeuralNetwork.
⟡ tensorboard-pytorch (https://github.com/lanpa/tensorboard-pytorch) - Tensorboard for PyTorch (and chainer, mxnet, numpy, ...).
⟡ mxboard (https://github.com/awslabs/mxboard) - Logging MXNet data for visualization in TensorBoard. 
Genetic Programming
⟡ gplearn (https://github.com/trevorstephens/gplearn) - Genetic Programming in Python. 
⟡ PyGAD (https://github.com/ahmedfgad/GeneticAlgorithmPython) - Genetic Algorithm in Python. 
⟡ DEAP (https://github.com/DEAP/deap) - Distributed Evolutionary Algorithms in Python.
⟡ karoo_gp (https://github.com/kstaats/karoo_gp) - A Genetic Programming platform for Python with GPU support. 
⟡ monkeys (https://github.com/hchasestevens/monkeys) - A strongly-typed genetic programming framework for Python.
@@ -352,6 +343,8 @@
Optimization
⟡ Optuna (https://github.com/optuna/optuna) - A hyperparameter optimization framework.
⟡ pymoo (https://github.com/anyoptimization/pymoo) - Multi-objective Optimization in Python.
⟡ pycma (https://github.com/CMA-ES/pycma?tab=readme-ov-file) - Python implementation of CMA-ES.
⟡ Spearmint (https://github.com/HIPS/Spearmint) - Bayesian optimization.
⟡ BoTorch (https://github.com/pytorch/botorch) - Bayesian optimization in PyTorch. 
⟡ scikit-opt (https://github.com/guofei9987/scikit-opt) - Heuristic Algorithms for optimization.
@@ -462,8 +455,8 @@
⟡ swifter (https://github.com/jmcarpenter2/swifter) - A package that efficiently applies any function to a pandas dataframe or series in the fastest available manner.
⟡ pandas-log (https://github.com/eyaltrabelsi/pandas-log) - A package that allows providing feedback about basic pandas operations and finds both business logic and performance issues.
⟡ vaex (https://github.com/vaexio/vaex) - Out-of-Core DataFrames for Python, ML, visualize and explore big tabular data at a billion rows per second.
⟡ xarray (https://github.com/pydata/xarray) - Xarray combines the best features of NumPy and pandas for multidimensional data selection by supplementing numerical axis labels with named dimensions for more intuitive, concise, and less 
error-prone indexing routines.
⟡ xarray (https://github.com/pydata/xarray) - Xarray combines the best features of NumPy and pandas for multidimensional data selection by supplementing numerical axis labels with named dimensions for more intuitive, concise, and less error-prone
indexing routines.
Pipelines
⟡ pdpipe (https://github.com/shaypal5/pdpipe) - Sasy pipelines for pandas DataFrames.
@@ -511,6 +504,7 @@
⟡ deepchecks (https://github.com/deepchecks/deepchecks) - Validation & testing of ML models and data during model development, deployment, and production. 
⟡ evidently (https://github.com/evidentlyai/evidently) - Evaluate and monitor ML models from validation to production.
⟡ TensorFlow Data Validation (https://github.com/tensorflow/data-validation) - Library for exploring and validating machine learning data.
⟡ DataComPy (https://github.com/capitalone/datacompy)- A library to compare Pandas, Polars, and Spark data frames. It provides stats and lets users adjust for match accuracy.
Evaluation
⟡ recmetrics (https://github.com/statisticianinstilettos/recmetrics) - Library of useful metrics and plots for evaluating recommender systems.
@@ -558,3 +552,5 @@
License
This work is licensed under the Creative Commons Attribution 4.0 International License - CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/)
pythondatascience Github: https://github.com/krzjoa/awesome-python-data-science