Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Core deep-learning frameworks and libraries for pretraining and distributed training.
Signals: deep-learning, neural-network, pytorch, tensorflow, jax, distributed-training, training, deepspeed
2,697 results
Live training loss plot in Jupyter Notebook for Keras, PyTorch and others
Training PyTorch models with differential privacy
Data manipulation and transformation for audio signal processing, powered by PyTorch
A highly efficient implementation of Gaussian Processes in PyTorch
Vector (and Scalar) Quantization, in Pytorch
tensorboard for pytorch (and chainer, mxnet, numpy, ...)
RL implementations
Collection of eclectic utils for python.
Model Compression Toolkit (MCT) is an open source project for neural network model optimization under efficient, constrained hardware. This project provides researchers, developers, and engineers advanced quantization and compression tools for deploying state-of-the-art neural networks.
A zero-dependency ML framework in C with a modern Python API for full control over execution and memory.
Enhance Images with Javascript and AI. Increase resolution, retouch, denoise, and more. Open Source, Browser & Node Compatible, MIT License.
Convert TensorFlow, Keras, Tensorflow.js and Tflite models to ONNX
TensorFlow-based neural network library
A high-performance ML model serving framework, offers dynamic batching and CPU/GPU pipelines to fully exploit your compute machine
TorchGeo: datasets, samplers, transforms, and pre-trained models for geospatial data
An open source implementation of CLIP.
A toolkit for making real world machine learning and data analysis applications in C++
Recipes are a standard, well supported set of blueprints for machine learning engineers to rapidly train models using the latest research techniques without significant engineering overhead.Specifically, recipes aims to provide- Consistent access to pre-trained SOTA models ready for production- Reference implementations for SOTA research reproducibility, and infrastructure to guarantee correctness, efficiency, and interoperability.
Traditional machine learning on top of Nx
MOA is an open source framework for Big Data stream mining. It includes a collection of machine learning algorithms (classification, regression, clustering, outlier detection, concept drift detection and recommender systems) and tools for evaluation.
Deep learning framework for MRI reconstruction
Master the fundamentals of machine learning, deep learning, and mathematical optimization by building key concepts and models from scratch using Python.
Open source guides/codes for mastering deep learning to deploying deep learning in production in PyTorch, Python, Apptainer, and more.
Machine learning platform for Web developers
24,523 repositories in the index in total.
