redis-rb VS ruby-fann

Compare redis-rb vs ruby-fann and see what are their differences.

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redis-rb ruby-fann
8 1
3,947 489
0.3% 4.1%
7.5 6.0
6 days ago about 1 month ago
Ruby C
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.

redis-rb

Posts with mentions or reviews of redis-rb. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-08-12.

ruby-fann

Posts with mentions or reviews of ruby-fann. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-09-13.

What are some alternatives?

When comparing redis-rb and ruby-fann you can also consider the following projects:

Clickhouse - A Ruby database driver for Clickhouse

AI4R - Artificial Intelligence for Ruby - A Ruby playground for AI researchers

Redic - Lightweight Redis Client

Ruby Units - A unit handling library for ruby

Cassandra Driver - [MAINTENANCE ONLY] DataStax Ruby Driver for Apache Cassandra

Nerve - This is a basic implementation of a neural network for use in C and C++ programs. It is intended for use in applications that just happen to need a simple neural network and do not want to use needlessly complex neural network libraries.

TinyTDS - TinyTDS - Simple and fast FreeTDS bindings for Ruby using DB-Library.

lab - A customisable 3D platform for agent-based AI research

mongo-ruby-driver - The Official MongoDB Ruby Driver

bhook - :fire: ByteHook is an Android PLT hook library which supports armeabi-v7a, arm64-v8a, x86 and x86_64.

DataObjects

XNNPACK - High-efficiency floating-point neural network inference operators for mobile, server, and Web