AutoDiff DAG constructor, built on numpy and Cython. A Neural Turing Machine and DeepQ agent run on it. Clean code for educational purpose.

Model-based Policy Gradients

Computational graph-based discrete choice models

Computation Graph framework implemented using only NumPy

Automatic differentiation in python

Parameter Estimation of LOGIT-based Stochastic User Equilibrium models using computational graphs and day-to-day system-level data

Network-wide estimation of traffic flow and travel time with data-driven macroscopic models

A general purpose framework for building and running computational graphs.

A graph-oriented algorithmic engine

Implementing a neural network classifier for cifar-10

Python library providing a collection of functions realizing common computer vision functionality, based on OpenCV and NumPy.

This is an experiment version of calibrating origin-destination matrix estimation using link traffic counts

A framework for developing async applications.

a compact tensor library capable of training deep neural networks on both cpu and cuda devices

Yet another tensor automatic differentiation framework

Automatic differentiation

Cached lazy evaluation of computational graphs

Code for the part 1 of the tutorial on pychain