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Ruby library for interfacing with FANN (Fast Artificial Neural Network)
The main features of tangledpath/ruby-fann are: Machine Learning, Machine Learning Libraries, Neural Networks and Deep Learning.
Open-source alternatives to tangledpath/ruby-fann include: fchollet/keras — Keras is a high-level deep learning API used to design, build, and train neural networks for tasks such as computer… h2oai/h2o-3 — h2o-3 is a distributed machine learning platform and automated machine learning framework designed for training and… deeplearning4j/deeplearning4j — Deeplearning4j is a JVM-based deep learning framework and tensor computing library. It provides a computational graph… catboost/catboost — CatBoost is a gradient boosting machine learning library used to train decision tree ensembles for regression,… cjlin1/libsvm — This project is a support vector machine library implemented in C, providing an engine for classification and… febeling/rb-libsvm — Ruby language bindings for LIBSVM.
Keras is a high-level deep learning API used to design, build, and train neural networks for tasks such as computer vision, natural language processing, and time series forecasting. It provides a framework for defining model architectures and optimizing weights through a structured interface. The project is defined by a backend-agnostic design that allows the same model code to run across different compute engines. This multi-backend execution enables users to swap underlying engines to optimize for specific hardware or performance requirements. The system supports distributed model training
h2o-3 is a distributed machine learning platform and automated machine learning framework designed for training and deploying predictive models using distributed in-memory computing. It functions as a deep learning framework and a distributed model scoring engine, capable of operating as a Kubernetes ML cluster to process large datasets in parallel. The platform distinguishes itself through automated machine learning capabilities that automatically select the best algorithms and hyperparameters to optimize model performance. It provides specialized deep learning toolkits for tasks including i
This project is a support vector machine library implemented in C, providing an engine for classification and regression tasks. It functions as a machine learning kernel library and a statistical model validator used to categorize data points and predict continuous numerical values. The library allows for the definition of custom kernel functions to calculate similarity between data points in specialized datasets. It also includes tools for probabilistic modeling, such as estimating class membership, data density, and distribution boundaries. Broad capabilities cover model training for multi
CatBoost is a gradient boosting machine learning library used to train decision tree ensembles for regression, classification, and ranking tasks. It functions as a high-performance framework that provides a categorical data processor for transforming non-numeric features, a distributed trainer for large-scale datasets, and GPU acceleration to speed up model construction. The library distinguishes itself through native handling of categorical data and text features, removing the need for manual encoding. It includes a specialized model interpretability tool that leverages SHAP values and featu