flashsync

Like Streamlit, but fast. A proof-of-concept framework built using Vue3/Vite + FastAPI + WebSockets + MSGPACK

g-idw

Parallel GPU Inverse Distance Weighting

garage

A toolkit for reproducible reinforcement learning research

gcn-spp

Shortest Path prediction using Graph Convolutional Networks

gcn_partitioning

Graph Partitoning Using Graph Convolutional Networks

genetic-algorithm

0-1 Knapsack Approximation with Genetic Algorithms

gerapy

Distributed Crawler Management Framework Based on Scrapy, Scrapyd, Django and Vue.js

gomoku-deep-learning

gomoku AI with deep learning and monte carlo tree search

graph-convnet-tsp

Code for the paper 'An Efficient Graph Convolutional Network Technique for the Travelling Salesman Problem' (arXiv Pre-print)

graph-pointer-network

graph2seq

Graph2Seq is a simple code for building a graph-encoder and sequence-decoder for NLP and other AI/ML/DL tasks.

graph_attention_pool

Attention over nodes in Graph Neural Networks using PyTorch (NeurIPS 2019)

graph_comb_opt

Implementation of "Learning Combinatorial Optimization Algorithms over Graphs"

graphillion

Fast, lightweight graphset operation library

gridcal

GridCal, a cross-platform power systems solver written in Python with user interface and embedded python console

gridkit

GridKit is an power grid extraction toolkit

gridopt

Power Grid Optimization Package.

gridstage

gridverse

helmpy

HELMpy, open source package of power flow solvers, including the Holomorphic Embedding Load Flow Method (HELM), developed on Python 3

holomorphic-embedding-josep

home-automation

Distributed home automation system

hypernetx

Python package for hypergraph analysis and visualization.

interpolation

Spatial interpolation methods.

iot-home-guard

A tool for malicious behavior detection in IoT devices

kagen

KaGen: Communication-free Massively Distributed Graph Generators

keras-dqn-doom

Keras implementation of DQN on ViZDoom environment

keras-rl

Deep Reinforcement Learning for Keras.

l2rpn

CodaLab L2RPN: Learning to Run a Power Network

l2rpn-using-a3c

Reinforcement Learning using the Actor-Critic framework for the L2RPN challenge (https://l2rpn.chalearn.org/ & https://competitions.codalab.org/competitions/22845#learn_the_details-overview). The agent trained using this code was one of the winners of the challenge. The code runs on the pypownet environment (https://github.com/MarvinLer/pypownet). It is released under a license of LGPLv3