A general purpose framework for building and running computational graphs.
-
Updated
Aug 29, 2024 - Python
A general purpose framework for building and running computational graphs.
Python library providing a collection of functions realizing common computer vision functionality, based on OpenCV and NumPy.
Computation Graph framework implemented using only NumPy
Network-wide estimation of traffic flow and travel time with data-driven macroscopic models
Parameter Estimation of LOGIT-based Stochastic User Equilibrium models using computational graphs and day-to-day system-level data
Yet another tensor automatic differentiation framework
a compact tensor library capable of training deep neural networks on both cpu and cuda devices
A graph-oriented algorithmic engine
Cached lazy evaluation of computational graphs
Automatic differentiation in python
Model-based Policy Gradients
AutoDiff DAG constructor, built on numpy and Cython. A Neural Turing Machine and DeepQ agent run on it. Clean code for educational purpose.
A simple library for building computational graphs with autodiff support.
Implementing a neural network classifier for cifar-10
Code for the part 1 of the tutorial on pychain
Add a description, image, and links to the computational-graphs topic page so that developers can more easily learn about it.
To associate your repository with the computational-graphs topic, visit your repo's landing page and select "manage topics."