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Machine Learning & Data Mining with JRuby
The main features of paulgoetze/weka-jruby are: Machine Learning, Machine Learning Frameworks, Machine Learning Libraries.
Projects with overlapping indexed features include: jax-ml/jax — This project is a high-performance numerical computing library designed for large-scale scientific and machine… lightning-ai/pytorch-lightning — PyTorch Lightning is a deep learning research framework that provides a structured environment for organizing machine… catboost/catboost — CatBoost is a gradient boosting machine learning library used to train decision tree ensembles for regression,… deeplearning4j/deeplearning4j — Deeplearning4j is a JVM-based deep learning framework and tensor computing library. It provides a computational graph… keras-team/keras — Keras is a high-level deep learning framework designed for constructing and training neural networks through the… pytorch/pytorch — PyTorch is a machine learning framework centered on a GPU-ready tensor library that supports multi-dimensional array…
Deeplearning4j is a JVM-based deep learning framework and tensor computing library. It provides a computational graph engine for defining and executing deep learning workflows and mathematical operations within the Java Virtual Machine. The project includes a dedicated importer for loading and running pretrained models exported from Keras, TensorFlow, and ONNX formats. Its tensor computing capabilities are driven by a modular native C++ math core to execute high-performance linear algebra operations. The framework covers neural network training, deep learning model inference, and the constru
This project is a high-performance numerical computing library designed for large-scale scientific and machine learning workloads. It functions as an automatic differentiation framework and a just-in-time compilation engine, transforming high-level Python code into optimized machine instructions. By enforcing pure functional programming patterns and immutable array semantics, the library ensures that mathematical functions remain compatible with automated graph transformations and symbolic differentiation. The platform distinguishes itself through its distributed array computing capabilities,
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
Keras is a high-level deep learning framework designed for constructing and training neural networks through the composition of modular, functional layers. It serves as a comprehensive modeling toolkit that provides standardized procedures for defining, evaluating, and deploying complex architectures. By utilizing a directed acyclic graph approach, the framework allows users to build intricate models with multiple inputs, outputs, and shared layers, ensuring consistent numerical execution through functional state management. The project distinguishes itself as a multi-backend machine learning