Matrix and tensor operations
Core tensor operations for TypedTensor
Core tensor operations & FFI bindings for ts-torch
tensor operations modeled off numpy and tensorflow
0D/1D/2D/3D/4D tensors with extensible polymorphic operations and customizable storage
Common utility methods used by Tensor.
Topological indexing for simplicial complexes
PyTorch-like JavaScript tensor/autograd library with CPU training support. Provides Tensor, autograd, nn.Module, Linear, Conv2d, Adam optimizer, and MNIST dataset utilities.
Tiny TypeScript-native tensor library with autograd, compiling to WebGPU. Train small models in the browser without hand-writing kernels, or run a pretrained one frozen for transfer learning.
Common TEST utility methods used by Tensor.
PyTorch like deep learning inferrence library
A trainable Hidden Markov Model with Gaussian emissions using TensorFlow.js
CLI for Tuned Tensor — fine-tune and evaluate LLMs from the command line
LiteRT.js package
Anchor/JS SDK for interacting with TensorSwap, TensorWhitelist and TensorBid.
Read and write files atomically and reliably.
Computational framework for exploring unified physics through tensor formalism
Azure AI Projects client library.
AWS SDK for JavaScript Cognito Identity Provider Client for Node.js, Browser and React Native
Your one-stop-shop for your NFT needs.
An exchange for operation retry support in urql
walk paths fast and efficiently
stockshark-tensor
A collection of implementation for ECMAScript abstract operations
A native Ruby C extension providing parallelized matrix and neural-network style tensor operations.
dAImond is a PyTorch-inspired deep learning framework for Ruby featuring automatic differentiation, neural networks, and a high-performance Rust backend for tensor operations. Achieves 89%+ accuracy on MNIST.
GRX brings PyTorch-style tensor operations to Ruby. Every arithmetic op, activation, and optimizer step runs through a native C library compiled with AVX2+FMA SIMD. Ruby is the interface — C does the work. Features: autograd, SGD/Adam optimizers, Linear/Sequential/Dropout/BatchNorm layers, MSE/BCE/CrossEntropy loss functions, Xavier and He weight init. Cross-platform: .so on Linux, .dylib on macOS, .dll on Windows.
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