The Datumbox framework is written in Java for developing machine learning and statistical applications.
goro
The goro project provides a high level machine learning library written for Go.

About goro
goro is an open-source, high-level machine learning library for Go, built on top of Gorgonia. It aims to give Go developers a Keras-like workflow, with a sequential model API for defining layers, compiling a model with optimizer, loss and batch-size options, then fitting and predicting on single examples or batches. The repository includes an MNIST example and points to reinforcement learning examples in a companion library called Gold.
Key features
- Sequential model API for stacking layers in Go
- Layers include Conv2D, MaxPooling2D, Flatten and fully connected
- Softmax activation available on output layers
- Compile with optimizer, loss function and batch size options
- Batch fitting and prediction via FitBatch and PredictBatch
- Built on the Gorgonia library; MNIST example included
- High-level API modeled on Keras, with concise model definition code
- Open source under the Apache-2.0 license
- Built on Gorgonia, a Go deep learning library
- RNN, LSTM, summary and visualization are listed as roadmap items, so they are not described as available
- Small repository history of 11 commits
- No pricing information is provided
goro at a glance
- Website
- github.com/aunum/goro
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Yes · Source code
- Language
- English
- Last checked
- Oct 6, 2026 · Online when checked
- Listed
- Oct 5, 2026
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goro FAQ
What is goro built on?
Does goro support recurrent networks such as RNN or LSTM?
Where can I find example code?
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