go-deep VS go-fann

Compare go-deep vs go-fann and see what are their differences.

go-fann

Go bindings for FANN, library for artificial neural networks (by vksnk)
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go-deep go-fann
0 0
381 109
- -
1.5 0.0
7 months ago over 7 years ago
Go Go
MIT License MIT License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

go-deep

Posts with mentions or reviews of go-deep. We have used some of these posts to build our list of alternatives and similar projects.

We haven't tracked posts mentioning go-deep yet.
Tracking mentions began in Dec 2020.

go-fann

Posts with mentions or reviews of go-fann. We have used some of these posts to build our list of alternatives and similar projects.

We haven't tracked posts mentioning go-fann yet.
Tracking mentions began in Dec 2020.

What are some alternatives?

When comparing go-deep and go-fann you can also consider the following projects:

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

GoLearn - Machine Learning for Go

gosseract - Go package for OCR (Optical Character Recognition), by using Tesseract C++ library

go-galib - Genetic Algorithms library written in Go / golang

bayesian - Naive Bayesian Classification for Golang.

tfgo - Tensorflow + Go, the gopher way

sklearn - bits of sklearn ported to Go #golang

goml - On-line Machine Learning in Go (and so much more)

neural-go - A multilayer perceptron network implemented in Go, with training via backpropagation.

gago - :four_leaf_clover: Evolutionary optimization library for Go (genetic algorithm, partical swarm optimization, differential evolution)

m2cgen - Transform ML models into a native code (Java, C, Python, Go, JavaScript, Visual Basic, C#, R, PowerShell, PHP, Dart, Haskell, Ruby, F#, Rust) with zero dependencies