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

A general purpose framework for building and running computational graphs.

A graph-oriented algorithmic engine

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

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.

A framework for developing async applications.

Yet another tensor automatic differentiation framework

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

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

Code for the part 2 of the tutorial on pychain

Small tools for Tensorflow.

Automatic differentiation