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mni-ml/framework: A machine learning library with a TypeScript API and Rust backend. CUDA and WebGPU compatibility. Built to understand how ML frameworks and models work internally. ·

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A machine learning library with a TypeScript API and Rust backend. CUDA and WebGPU compatibility. Built to understand how ML frameworks and models work internally. A TypeScript ML framework with Rust native backends (CPU, CUDA, WebGPU) providing autograd, tensor operations, and neural network training at GPU speed. All three backends share the same autograd tape and tensor store. Feature flags are mutually exclusive at compile time: Only needed if you are contributing or want a custom build. Requires Rust. MIT A machine learning library with a TypeScript API and Rust backend. CUDA and WebGPU compatibility. Built to understand how ML frameworks and models work internally.

A TypeScript ML framework with Rust native backends (CPU, CUDA, WebGPU) providing autograd, tensor operations, and neural network training at GPU speed. Features Automatic differentiation -- full backward pass through an autograd tape GPU acceleration -- CUDA (NVIDIA) and WebGPU (Metal/Vulkan/DX12) backends PyTorch-like API -- familiar Tensor, Module, Parameter, optimizer classes Comprehensive ops -- elementwise, matmul, conv1d/conv2d, pooling, reductions, activations Built-in modules -- Linear, Conv1d, Conv2d, Embedding, ReLU, Sigmoid, Tanh Optimizers -- SGD and Adam (AdamW) with…

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