Google Speech-to-Text enables developers to convert audio to text by applying powerful neural network models in an easy-to-use API. The API recognizes more than 120 languages and variants to support your global user base. You can enable voice command-and-control, transcribe audio from call centers, and more. It can process real-time streaming or prerecorded audio, using Google's machine learning technology.

Google Speech-to-Text enables developers to convert audio to text by applying powerful neural network models in an easy-to-use API. The API recognizes more than 120 languages and variants to support your global user base. You can enable voice command-and-control, transcribe audio from call centers, and more. It can process real-time streaming or prerecorded audio, using Google's machine learning technology. Note that google-cloud-speech-v1 is a version-specific client library. For most uses, we recommend installing the main client library google-cloud-speech instead. See the readme for more details.

Google Speech-to-Text enables developers to convert audio to text by applying powerful neural network models in an easy-to-use API. The API recognizes more than 120 languages and variants to support your global user base. You can enable voice command-and-control, transcribe audio from call centers, and more. It can process real-time streaming or prerecorded audio, using Google's machine learning technology. Note that google-cloud-speech-v1p1beta1 is a version-specific client library. For most uses, we recommend installing the main client library google-cloud-speech instead. See the readme for more details.

Google Speech-to-Text enables developers to convert audio to text by applying powerful neural network models in an easy-to-use API. The API recognizes more than 120 languages and variants to support your global user base. You can enable voice command-and-control, transcribe audio from call centers, and more. It can process real-time streaming or prerecorded audio, using Google's machine learning technology. Note that google-cloud-speech-v2 is a version-specific client library. For most uses, we recommend installing the main client library google-cloud-speech instead. See the readme for more details.

Rumale::NeuralNetwork provides classifiers and regression algorithms based on multi-layer perceptron, radial basis function network, and random vector functional link network in the Rumale interface.

A simple feed-forward Neural Network gem

NeuralNet/DeepLearning implement for Ruby. You can use 'neural network' and 'deep learning' easily.

Simple Feed Forward Neural Network

A native Ruby C extension providing parallelized matrix and neural-network style tensor operations.

AI4R is a lightweight, educational Ruby library featuring clean implementations of core machine learning and AI algorithms—such as decision trees, neural networks, k-means, genetic algorithms, and even a bit size Transformers architecture covering encoder, decoder, and seq2seq variations. Designed with simplicity and clarity in mind, this library is ideal for students, educators, and developers who want to understand these algorithms line by line. With no external dependencies, no GPU support, and no production overhead, AI4R serves as a practical and transparent way to explore the foundations of AI in Ruby. It is a long-maintained open-source effort to bring accessible, hands-on machine learning to the Ruby community.

This is a ruby gem that lets you implement categorization systems with ease. Associative memory neural networks make it easy to identify probable patterns between sets of named data points. It can be cumbersome to interface with the neural network directly, however, as a typical implementation has a fixed size and training period, which limits how useful they can be to an integrated system. associative_memory simplifies these kind of machine learning models by offering dynamic input and output sets. This allows your code to concentrate on extrapolating meaningful patterns rather than juggling bitmasks and transposition matrices.

A simple neural network implementation in Ruby.

RubyNEAT -- Neural Evolution of Augmenting Topologies for Ruby. By way of an enhanced form of Genetic Algorithms -- the NEAT algorithm, populations of neural nets are evolved to handle pre-defined goals. RubyNEAT is the first implementation of the NEAT algorithm for Ruby, and it leverages Ruby's power to implement the NEAT algorithm in a way that would be difficult to do in other languages. The 'activation function' is largely standalone. Basically, activation is achieved by functional programming. Meaning, once your network is evolved, you can extract it as source code you can then utilize without the RubyNEAT engine. RubyNEAT can be used for nearly any Machine Learning task you can dream of, because it's also extensible and modular. See http://rubyneat.com for the details.

Neural Network

neural network ruby implementaiton

Compact Language Detector v3 (CLD3) is a neural network model for language identification.

A flexible framework for neural network for Ruby

Artificial Neural Networks in Ruby

A Ruby extension that provides a 2-Layer Back Propagation Neural Network, which can be used to categorize datasets of arbitrary size.

This is a ruby gem for bulk delete messages and files on Slack channels.