#Introduction Computational graph for backpropagation is a tool used in deep learning to efficiently compute gradients during the training process. It represents the computations performed in a neural ...

This is a simple implementation of backpropagation using computational graphs. Computational Graphs are directed acyclic graphs that represent mathematical expressions and facilitate the efficient ...

We propose a class of neural models for graphs that do not rely on backpropagation for training, thus making learning more biologically plausible and amenable to parallel implementation in hardware.