Gorgonia is a library that helps facilitate machine learning in Go.

Self-contained Machine Learning and Natural Language Processing library in Go

Assignment 1: automatic differentiation

Implementing Multiple Layer Neural Network from Scratch

A visual Deep Learning Framework for the Web - Built with WebGPU, Next.js and ReactFlow.

(Spring 2017) Assignment 2: GPU Executor

Structural Equation Modeling using Torch

Deep Learning framework in C++/CUDA that supports symbolic/automatic differentiation, dynamic computation graphs, tensor/matrix operations accelerated by GPU and implementations of various state-of-the-art graph neural networks and other Machine Learning models including Covariant Compositional Networks For Learning Graphs [Risi et al]

Build, distribute, and execute task graphs

Computational graph library for machine learning

Model-based Policy Gradients

A computation graph micro-framework providing seamless lazy and concurrent evaluation

Jaxpr Visualisation Tool

GenCoG: A DSL-Based Approach to Generating Computation Graphs for TVM Testing (ISSTA‘23)

A short collection of Jupyter notebooks explaining some basic computational math

artifax is a Python package to evaluate nodes in a computation graph where the dependencies associated with each node are extracted directly from their function signatures.

C computation graph, AutoGrad with OpenCL support [WIP]

Real-time execution and remote monitoring and tuning of BDSim Block-Diagrams for modeling and control of Dynamical Systems