#deep-learning

1 Line of code data quality profiling & exploratory data analysis for Pandas and Spark DataFrames.

Graph Neural Networks in Julia

AgML is a centralized framework for agricultural machine learning. AgML provides access to public agricultural datasets for common agricultural deep learning tasks, with standard benchmarks and pretrained models, as well the ability to generate synthetic data and annotations.

Deep universal probabilistic programming with Python and PyTorch

Probabilistic reasoning and statistical analysis in TensorFlow

View model summaries in PyTorch!

🤗 image matching webui

WildlifeDatasets: An open-source toolkit for animal re-identification

A research-focused modular generative modeling library built on JAX/Flax NNX

Effector - a Python package for global and regional effect methods

Tools for computational pathology

Rust bindings for the C++ api of PyTorch.

The RWTH extensible training framework for universal recurrent neural networks

A multi-backend graph learning library.

Search engine

🔮 A refreshing functional take on deep learning, compatible with your favorite libraries

Training/Fine-tuning at the speed of light

The fastest .NET tensor library. Beats MathNet (6x), NumSharp (3200x), matches TorchSharp CPU - pure managed C# with hand-tuned AVX2/FMA SIMD kernels. Optional CUDA/OpenCL GPU acceleration.

A high-level machine learning and deep learning library for the PHP language.

Hardware accelerated, batchable and differentiable optimizers in JAX.

The easiest way to use deep metric learning in your application. Modular, flexible, and extensible. Written in PyTorch.

Nx-powered Neural Networks

Machine learning framework written in C.

A Python library for audio data augmentation. Useful for making audio ML models work well in the real world, not just in the lab.

PyTorch Implementation of Google Research's MetNet and MetNet-2