itsubaki/autograd
Automatic differentiation library for Go
This is an automatic differentiation library for Go, enabling developers to compute derivatives of complex functions. It takes mathematical expressions or neural network definitions as input and outputs the gradients, which are essential for optimizing models. Developers working with machine learning, numerical optimization, or scientific computing in Go would use this.
Use this if you are a Go developer building machine learning models or performing numerical optimization and need to automatically calculate gradients.
Not ideal if you are not a Go developer or if you need a pre-built, high-level machine learning framework rather than a low-level differentiation tool.
Stars
22
Forks
3
Language
Go
License
MIT
Category
Last pushed
Mar 07, 2026
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/itsubaki/autograd"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
SPFlow/SPFlow
Sum Product Flow: An Easy and Extensible Library for Sum-Product Networks
gomlx/gomlx
GoMLX: An Accelerated Machine Learning Framework For Go
montanaflynn/stats
A well tested and comprehensive Golang statistics library package with no dependencies.
mattn/go-tflite
Go binding for TensorFlow Lite
james-bowman/sparse
Sparse matrix formats for linear algebra supporting scientific and machine learning applications