Go is a popular open-source programming language created by google researchers. It has many attractive features like garbage collector, cross-platform, efficient concurrency and so on. Machine learning helped everyone( including non-CS people too) over the past few years. We know that Go provides faster speeds at accessing requests over the internet, and also it can be used for something practical- like Machine learning. Here we’ve listed out some top machine learning libraries in Golang. Let’s see which are those.
You can know the reasons to use golang at- Why Golang Is Better Than Other Languages?
This library helps in facilitating machine learning completely in Go language. Main goal of this library is to be a highly performant machine learning and graph computation-based library that can scale across multiple machines. Also, it provides a platform for the exploration of non-standard deep learning and neural network related research. It can perform processes like neo-Hebbian learning, corner-cutting calculations, etc. Some of its features are-
Gorgonia can perform automatic differentiation, symbolic differentiation, gradient descent optimization and numerical stabilisation.
Library provides various convenience functions to help create neural networks.
This library supports CUDA and GPGPU computation.
It is a popular library in Go language and known as the ‘batteries included’ machine learning library for Go. Golearn aims to contribute simplicity paired with customizability. It has great features like-
Data are loaded in as Instances. You can then perform matrix like operations on them, and pass them to estimators.
This library is like popular Scikit-learn library in Python because it implements the Scikit-learn interface of Fit/Predict.
Golearn includes helper functions for data such as train splitting, test splitting and cross-validation.
It is a machine learning library written in Golang that allows developers to include machine learning into their apps. Goml includes many models that lets you to learn in an online, reactive manner by data transfer to streams held on channels. Those models include traditional, batch learning interfaces etc. Library includes extensive documentation, comprehensive tests, and expressive, clean, modular source code. Community contribution is heavily encouraged. All code of Goml is well documented and the source is readable if you’d like to make sense of it all.
It is a framework to implement evolutionary algorithms in Go language. Evo exposes a clean and flexible API oriented around two interfaces — genome and population. Population represents the architecture under which genomes are evolved, whereas Genomes represent both the function being optimised and the representation of solutions. It’s features are as follows-
- Library can be used to write modular web apps in Golang focussed on both backend and frontend.
- It is extensive and modular
- It provides high performance
- Clean and flexible API oriented
eaopt is an evolutionary optimisation library written in Go language. Evolutionary optimization algorithms are a subdomain of evolutionary computation. It’s main goal is to minimize/maximize a function without use of any gradient information. It shares the common property of exploring the search space by breeding, mutating, evaluating and sorting so called individuals. Many evolutionary algorithms are designed to deal with real valued functions, however they are usually used for handling more exotic issues. For instance, genetic algorithms can be used to find the optimal structure of a neural network.
This library provides implementation for various evolutionary optimization algorithms. According to the implementation, algorithms can be written as special cases of genetic algorithms. For sure this is made possible by using a generic definition of a genetic algorithm by permitting the mutation, crossover, selection, and replacement procedures to be modified freely. GA struct is flexible struct of eaopt and another algorithms are written on top of it. You can also write your own operators, if you don’t find any algorithms that suits your requirements.
It is a graph library written in Go language and having a goal to contribute simple, unifying interfaces, including implementations of graph algorithms and data structures which can scale from small graphs to huge graphs. Gogl is based on the reason that working with graphs can be decomplected by concentrating primarily on the natural constraints in graph theory.
Generally graph systems are big and complicated affairs. Some of it comes with the territory – graphs are not a simple datastructure. But gogle attempts to be not that, generally by concentrating on getting the basics right. Gogl’s operant standards are as follows-
- Performance- has faster design constraints and allows the best known algorithms.
- Simplicity- It has correctly modeling graph theoretic concepts
- Flexibility- Be unopinionated about vertices, and minimally opinionated about edges.
- Functional- Library orient towards transform, functors and streams, achieve other styles through layering
- Extensibility- Expect other to run google’s graph datastructures by their own algorithms, and gogl’s algorithms with their graph implementations
It is a neural network library written in Go language. Gomind supports a single hidden layer. In this library, the network learns from a training set using the back-propagation algorithm. It supports activation functions like Sigmoid, ReLU, Leaky ReLU etc. This library estimates ideal number of hidden layers neurons for given input and output size if a count is given during model configuration. It uses Mean Squared Error function to calculate error while back-propagating.