gorse alternatives and similar packages

Based on the "Machine Learning" category.
Alternatively, view gorse alternatives based on common mentions on social networks and blogs.

GoLearn

9.6 0.0 gorse VS GoLearn

Machine Learning for Go
Gorgonia

9.1 2.8 gorse VS Gorgonia

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

InfluxDB - Power Real-Time Data Analytics at Scale

Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

Promo www.influxdata.com

gosseract

8.4 6.7 gorse VS gosseract

Go package for OCR (Optical Character Recognition), by using Tesseract C++ library
m2cgen

8.4 0.0 gorse VS 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
tfgo

8.3 1.5 gorse VS tfgo

Tensorflow + Go, the gopher way
envd

8.0 9.0 gorse VS envd

🏕️ Reproducible development environment
goml

7.9 0.0 gorse VS goml

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

7.3 0.0 gorse VS gago

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

7.2 2.3 gorse VS bayesian

Naive Bayesian Classification for Golang.
CloudForest

7.1 0.0 gorse VS CloudForest

Ensembles of decision trees in go/golang.
ocrserver

7.0 0.0 gorse VS ocrserver

A simple OCR API server, seriously easy to be deployed by Docker, on Heroku as well
onnx-go

6.8 2.3 gorse VS onnx-go

onnx-go gives the ability to import a pre-trained neural network within Go without being linked to a framework or library.
gobrain

6.7 0.0 gorse VS gobrain

Neural Networks written in go
go-deep

6.6 3.5 gorse VS go-deep

Artificial Neural Network
sklearn

6.0 0.0 gorse VS sklearn

bits of sklearn ported to Go #golang
regommend

5.9 0.0 gorse VS regommend

Recommendation engine for Go
Goptuna

5.5 6.4 gorse VS Goptuna

A hyperparameter optimization framework, inspired by Optuna.
go-galib

5.5 0.0 gorse VS go-galib

Genetic Algorithms library written in Go / golang
goga

5.3 0.0 gorse VS goga

Golang Genetic Algorithm
goRecommend

5.3 0.0 gorse VS goRecommend

Collaborative Filtering (CF) Algorithms in Go!
shield

5.2 0.0 gorse VS shield

Bayesian text classifier with flexible tokenizers and storage backends for Go
go-fann

4.6 0.0 gorse VS go-fann

Go bindings for FANN, library for artificial neural networks
goscore

4.4 0.0 gorse VS goscore

Go Scoring API for PMML
neat

4.2 0.0 gorse VS neat

Plug-and-play, parallel Go framework for NeuroEvolution of Augmenting Topologies (NEAT).
go-featureprocessing

4.1 5.2 gorse VS go-featureprocessing

🔥 Fast, simple sklearn-like feature processing for Go
fonet

4.1 0.0 gorse VS fonet

fonet is a deep neural network package for Go.
libsvm

4.0 0.0 gorse VS libsvm

libsvm go version
NEUGO

3.8 0.0 gorse VS NEUGO

NEUGO: Neural Networks in Go
go-pr

3.8 0.0 gorse VS go-pr

Pattern recognition package in Go lang.
GoMind

3.7 0.0 gorse VS GoMind

A simplistic Neural Network Library in Go
neural-go

3.7 0.0 gorse VS neural-go

A multilayer perceptron network implemented in Go, with training via backpropagation.
Varis

3.5 0.0 gorse VS Varis

Golang Neural Network
golinear

3.4 0.0 gorse VS golinear

liblinear bindings for Go
go-cluster

3.2 0.0 gorse VS go-cluster

k-modes and k-prototypes clustering algorithms implementation in Go
EAGO

2.9 0.0 gorse VS EAGO

EAGO: Evolutionary Algorithms in Go
godist

2.8 0.0 gorse VS godist

Probability distributions and associated methods in Go
randomforest

2.8 2.6 gorse VS randomforest

Random Forest implementation in golang
evoli

2.8 0.0 gorse VS evoli

Genetic Algorithm and Particle Swarm Optimization
ddt

2.3 0.0 gorse VS ddt

Golang Dynamic Decision Tree
probab

2.0 0.0 gorse VS probab

Automatically exported from code.google.com/p/probab
mlgo

1.2 0.0 gorse VS mlgo

Automatically exported from code.google.com/p/mlgo

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README

Gorse Recommender System Engine

Gorse is an open-source recommendation system written in Go. Gorse aims to be a universal open-source recommender system that can be quickly introduced into a wide variety of online services. By importing items, users, and interaction data into Gorse, the system will automatically train models to generate recommendations for each user. Project features are as follows.

Multi-source Recommendation: For a user, recommended items are collected from different ways (popular, latest, user-based, item-based, and collaborative filtering) and ranked by click-through rate prediction.
AutoML: Choose the best recommendation model and strategy automatically by model searching in the background.
Distributed Recommendation: Single node training, distributed prediction, and ability to achieve horizontal scaling in the recommendation stage.
RESTful API: Provide RESTful APIs for data CRUD and recommendation requests.
Dashboard: Provide dashboard for data import and export, monitoring, and cluster status checking.

Quick Start

The playground mode has been prepared for beginners. Just set up a recommender system for GitHub repositories by following commands.

Linux/macOS:

curl -fsSL https://gorse.io/playground | bash

Docker:

docker run -p 8088:8088 zhenghaoz/gorse-in-one --playground

The playground mode will download data from GitRec and import it into Gorse. For more information：

Architecture

Gorse is a single node training and distributed prediction recommender system. Gorse stores data in MySQL, MongoDB, Postgres, or ClickHouse, with intermediate results cached in Redis, MySQL, MongoDB and Postgres.

The cluster consists of a master node, multiple worker nodes, and server nodes.
The master node is responsible for model training, non-personalized item recommendation, configuration management, and membership management.
The server node is responsible for exposing the RESTful APIs and online real-time recommendations.
Worker nodes are responsible for offline recommendations for each user.

In addition, the administrator can perform system monitoring, data import and export, and system status checking via the dashboard on the master node.