GitHub topics: computational-graph
hovsep/fmesh
FBP (flow based programming) inspired framework
Language: Go - Size: 1.39 MB - Last synced at: 5 days ago - Pushed at: 5 days ago - Stars: 14 - Forks: 0
gugugu12138/AdaptoFlux
An algorithm that implements intelligence based on a Method pool (a collection containing multiple types of functions). 一种基于方法池（包含多种类型的函数的集合）实现智能的算法
Language: Python - Size: 7.15 MB - Last synced at: 12 days ago - Pushed at: 12 days ago - Stars: 36 - Forks: 0
b-ionut-r/neurograd
A Pure Python Deep Learning Framework with Automatic Differentiation and PyTorch-like API
Language: Jupyter Notebook - Size: 54.4 MB - Last synced at: 22 days ago - Pushed at: 2 months ago - Stars: 2 - Forks: 0
mntsx/thoad
Lightweight performat Python 3.12+ automatic differentiation system that leverages PyTorch’s computational graph to compute arbitrary-order partial derivatives.
Language: Python - Size: 461 KB - Last synced at: 2 months ago - Pushed at: 2 months ago - Stars: 6 - Forks: 1
msminhas93/ferric-micrograd
A rust implementation of Andrej Karpathy's Micrograd
Language: Rust - Size: 41 KB - Last synced at: about 1 month ago - Pushed at: 7 months ago - Stars: 9 - Forks: 1
mehanix/dhrw
🎢 IaaS visual editor to create & deploy data processing pipelines - python, rmq, react, meteorjs
Language: JavaScript - Size: 1.88 MB - Last synced at: 8 months ago - Pushed at: 10 months ago - Stars: 0 - Forks: 0
Banyc/neural_network
vanilla, simple, node-oriented, compositive, optimized, frameworkn't{torchn't, TFn't, candlen't}
Language: Rust - Size: 303 KB - Last synced at: 9 months ago - Pushed at: about 1 year ago - Stars: 1 - Forks: 0
ThisIsRaphael/COSIG
A process-based computational graph framework in C# for OSINT
Language: C# - Size: 919 KB - Last synced at: 24 days ago - Pushed at: about 1 year ago - Stars: 2 - Forks: 0
VasixG/ComputationalGraph
Realization of computational graph as multilist in C with neural network implementation.
Language: C - Size: 24.6 MB - Last synced at: about 2 months ago - Pushed at: almost 2 years ago - Stars: 9 - Forks: 0
LiYan-97/CG_SF64
This code uses computational graph and neural network to solve the five-layer traffic demand estimation in Sioux Falls network. It also includes comparison of models and 10 cross-validations.
Language: Python - Size: 8.32 MB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 0 - Forks: 0
