# gorgonia/gorgonia

Gorgonia is a library that helps facilitate machine learning in Go.

Repository: https://github.com/gorgonia/gorgonia
Canonical: https://ross.abutalabs.com/products/gorgonia
Homepage: https://gorgonia.org/
Language: Go
License: Apache-2.0
License Family: permissive
Topics: machine-learning, artificial-intelligence, neural-network, computation-graph, differentiation, golang, go, gradient-descent, gorgonia, deep-learning, deeplearning, deep-neural-networks, automatic-differentiation, symbolic-differentiation, hacktoberfest, graph-computation
Last push: 2024-08-12T04:53:17+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 3640, "days_push": 751, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 5929, forks 450 (observed 2026-08-28T04:09:32.399963+00:00)

## What it is
Gorgonia is a Go library for machine learning that lets you define and evaluate mathematical equations over multidimensional arrays using a computation graph, similar in spirit to Theano and TensorFlow. It supports automatic and symbolic differentiation, gradient descent optimization, neural network construction, and CUDA/GPGPU computation.

## Use cases
- build neural networks in go
- automatic differentiation library for go
- train machine learning models without python
- compute gradients of mathematical expressions
- gpu-accelerated tensor math in go
- deploy deep learning models in go services
- experiment with non-standard learning algorithms

## When to choose
- your stack is Go and you want production ML without rewriting models in another language
- you need a low-level, Theano-style computation graph library in Go
- you want simple compilation and deployment of ML systems
- you're exploring experimental learning algorithms like evolutionary or neo-hebbian learning

## When to avoid
- you need the mature ecosystem, tooling, and community of PyTorch or TensorFlow
- you require OpenCL support or distributed training today
- you want cutting-edge model architectures and pretrained models
- you need rapid experimentation typical of Python-based data science workflows

## Facets
- artifact type: library
- maturity: maintenance
- function: machine-learning, deep-learning, math, gpu-computing
- domain: machine-learning, deep-learning, artificial-intelligence
- platform: go, windows
- tags: computation-graph, automatic-differentiation, neural-networks, tensors, gradient-descent, theano-like, algorithms, linux, macos, gpu

## Member repositories
- gorgonia/gorgonia (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:32.399963+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:50:49.092961+00:00, confidence not recorded.
  - readme: https://github.com/gorgonia/gorgonia (fetched 2026-08-28T04:09:32.399963+00:00, sha 0898abc7b1a5)
  - homepage: https://gorgonia.org/ (fetched 2026-08-29T08:46:36.517149+00:00, sha fed198c9fa4a)
  - site_page: https://gorgonia.org/getting-started (fetched 2026-08-29T08:46:36.519659+00:00, sha bc181d7b82ef)
  - site_page: https://gorgonia.org/getting-started/ubiquitous-language (fetched 2026-08-29T08:46:36.521607+00:00, sha 7b6e0323b8e7)
  - site_page: https://gorgonia.org/getting-started/contributing-doc (fetched 2026-08-29T08:46:36.523505+00:00, sha 81ac52229fef)
  - site_page: https://gorgonia.org/about (fetched 2026-08-29T08:46:36.525559+00:00, sha 03f2f9ac3405)
  - site_page: https://gorgonia.org/about/computation-graph (fetched 2026-08-29T08:46:36.527257+00:00, sha e621dfc6e86c)
  - site_page: https://gorgonia.org/about/differentiation (fetched 2026-08-29T08:46:36.529025+00:00, sha f105bb0d6f51)
  - site_page: https://gorgonia.org/about/differentiation/autodiff (fetched 2026-08-29T08:46:36.530737+00:00, sha cdcf03c3f208)
  - site_page: https://gorgonia.org/about/differentiation/symbolicdiff (fetched 2026-08-29T08:46:36.532840+00:00, sha 2283f050383a)
- Data as of 2026-08-30T08:39:29.467469+00:00.