A Light-weight Deep Learning Library with automatic differentiation based on dynamic computation graphs.
-
Updated
May 25, 2023 - C++
A Light-weight Deep Learning Library with automatic differentiation based on dynamic computation graphs.
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]
These are my computer graphics projects.
DAG-based computation graph for streaming data
a single c++ file for learing how data flow graphs work.
Add a description, image, and links to the computation-graph topic page so that developers can more easily learn about it.
To associate your repository with the computation-graph topic, visit your repo's landing page and select "manage topics."