Tensors and Dynamic neural networks in Python with strong GPU acceleration
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Burn is a next generation tensor library and Deep Learning Framework that doesn't compromise on flexibility, efficiency and portability.
Open Machine Learning Compiler Framework
PyTorch tutorials and fun projects including neural talk, neural style, poem writing, anime generation (《深度学习框架PyTorch：入门与实战》)
NumPy & SciPy for GPU
Multi-dimensional arrays (tensors) and numerical definitions for Elixir
2-2000x faster ML algos, 50% less memory usage, works on all hardware - new and old.
SIMD-accelerated distances, dot products, matrix ops, geospatial & geometric kernels for 16 numeric types — from 6-bit floats to 64-bit complex — across x86, Arm, RISC-V, and WASM, with bindings for Python, Rust, C, C++, Swift, JS, and Go 📐
DiffSharp: Differentiable Functional Programming
OCaml bindings for PyTorch
Read and write Neuroglancer datasets programmatically.
The V Tensor Library
Born is a modern ML framework for Go — train and deploy models as single binaries. Pure Go, zero CGO, GPU accelerated.
Accelerated tensor operations and dynamic neural networks based on reverse mode automatic differentiation for every device that can run Swift - from watchOS to Linux
Machine learning compiler in Go. A from-scratch tinygrad port: graph-rewrite IR, autodiff as a compiler pass, zero-CGO WebGPU backend.