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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. <img height="20" src="img/keras_big.png" alt="Keras compatible">
* [qkeras](https://github.com/google/qkeras) - A quantization deep learning library. <img height="20" src="img/keras_big.png" alt="Keras compatible">
### MXNet
* [MXNet](https://github.com/apache/incubator-mxnet) - Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler. <img height="20" src="img/mxnet_big.png" alt="MXNet based">
* [Gluon](https://github.com/gluon-api/gluon-api) - A clear, concise, simple yet powerful and efficient API for deep learning (now included in MXNet). <img height="20" src="img/mxnet_big.png" alt="MXNet based">
* [Xfer](https://github.com/amzn/xfer) - Transfer Learning library for Deep Neural Networks. <img height="20" src="img/mxnet_big.png" alt="MXNet based">
* [MXNet](https://github.com/ROCmSoftwarePlatform/mxnet) - HIP Port of MXNet. <img height="20" src="img/mxnet_big.png" alt="MXNet based"> <img height="20" src="img/amd_big.png" alt="Possible to run on AMD GPU">
### 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. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
* [gluon-nlp](https://github.com/dmlc/gluon-nlp) - NLP made easy. <img height="20" src="img/mxnet_big.png" alt="MXNet based">
* [KerasNLP](https://github.com/keras-team/keras-nlp) - Modular Natural Language Processing workflows with Keras. <img height="20" src="img/keras_big.png" alt="Keras based/compatible">
* [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. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
* [PyTorch3D](https://github.com/facebookresearch/pytorch3d) - PyTorch3D is FAIR's library of reusable components for deep learning with 3D data. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
* [gluon-cv](https://github.com/dmlc/gluon-cv) - Provides implementations of the state-of-the-art deep learning models in computer vision. <img height="20" src="img/mxnet_big.png" alt="MXNet based">
* [KerasCV](https://github.com/keras-team/keras-cv) - Industry-strength Computer Vision workflows with Keras. <img height="20" src="img/keras_big.png" alt="MXNet based">
* [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. <img height="20" src="img/sklearn_big.png" alt="sklearn">
* [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.
@@ -339,10 +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. <img height="20" src="img/mxnet_big.png" alt="MXNet based">
## Genetic Programming
* [gplearn](https://github.com/trevorstephens/gplearn) - Genetic Programming in Python. <img height="20" src="img/sklearn_big.png" alt="sklearn">
* [PyGAD](https://github.com/ahmedfgad/GeneticAlgorithmPython) - Genetic Algorithm in Python. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible"> <img height="20" src="img/keras_big.png" alt="keras">
* [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. <img height="20" src="img/tf_big2.png" alt="sklearn">
* [monkeys](https://github.com/hchasestevens/monkeys) - A strongly-typed genetic programming framework for Python.
@@ -351,6 +343,8 @@
<a name="opt"></a>
## 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. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
* [scikit-opt](https://github.com/guofei9987/scikit-opt) - Heuristic Algorithms for optimization.
@@ -509,6 +503,7 @@
* [deepchecks](https://github.com/deepchecks/deepchecks) - Validation & testing of ML models and data during model development, deployment, and production. <img height="20" src="img/sklearn_big.png" alt="sklearn">
* [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.
@@ -556,3 +551,6 @@ Read the <a href=https://github.com/krzjoa/awesome-python-datascience/blob/maste
## 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.md Github](https://github.com/krzjoa/awesome-python-data-science
)