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OpenML: [494] Web platform with Python, R, Java, and other APIs for downloading hundreds of machine learning datasets, evaluating algorithms on datasets, and benchmarking algorithm performance against dozens of other algorithms. PMLB: [495] A large, curated repository of benchmark datasets for evaluating supervised machine learning algorithms ...
Environment for DeveLoping KDD-Applications Supported by Index-Structures is a similar project to Weka with a focus on cluster analysis, i.e., unsupervised methods. H2O.ai is an open-source data science and machine learning platform; KNIME is a machine learning and data mining software implemented in Java.
It is mostly used for numerical analysis, computational science, and machine learning. [6] C# can be used to develop high level machine learning models using Microsoft’s .NET suite. ML.NET was developed to aid integration with existing .NET projects, simplifying the process for existing software using the .NET platform.
This improved performance on computers without GPU or other dedicated hardware, which was a goal of the project. [3] [10] [11] llama.cpp gained traction with users who lacked specialized hardware as it could run on just a CPU including on Android devices. [10] [12] [13] While initially designed for CPUs, GPU inference support was later added. [14]
Orange is an open-source software package released under GPL and hosted on GitHub.Versions up to 3.0 include core components in C++ with wrappers in Python.From version 3.0 onwards, Orange uses common Python open-source libraries for scientific computing, such as numpy, scipy and scikit-learn, while its graphical user interface operates within the cross-platform Qt framework.
Project Jupyter's name is a reference to the three core programming languages supported by Jupyter, which are Julia, Python and R. Its name and logo are an homage to Galileo 's discovery of the moons of Jupiter , as documented in notebooks attributed to Galileo.
Machine learning and data mining often employ the same methods and overlap significantly, but while machine learning focuses on prediction, based on known properties learned from the training data, data mining focuses on the discovery of (previously) unknown properties in the data (this is the analysis step of knowledge discovery in databases).
The OCaml 4.0 release in 2012 added Generalized Algebraic Data Types (GADTs) and first-class modules to increase the flexibility of the language. [11] The OCaml 5.0.0 release in 2022 [ 13 ] is a complete rewrite of the language runtime, removing the global GC lock and adding effect handlers via delimited continuations .