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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.
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JAX is a machine learning framework for transforming numerical functions developed by Google with some contributions from Nvidia. [2] [3] [4] It is described as bringing together a modified version of autograd (automatic obtaining of the gradient function through differentiation of a function) and OpenXLA's XLA (Accelerated Linear Algebra).
Python: Application, general, web, scripting, artificial intelligence, scientific computing Yes Yes Yes Yes Yes Yes Aspect-oriented De facto standard via Python Enhancement Proposals (PEPs) R: Application, statistics Yes Yes Yes Yes No Yes No Racket: Education, general, scripting Yes Yes Yes Yes No Yes Modular, logic, meta No Raku
OpenCog, a GPL-licensed framework for artificial intelligence written in C++, Python and Scheme. [15] PolyAnalyst: A commercial tool for data mining, text mining, and knowledge management. [89] RapidMiner, an environment for machine learning and data mining, now developed commercially. [90]
TensorFlow is a software library for machine learning and artificial intelligence. It can be used across a range of tasks, but is used mainly for training and inference of neural networks . [ 3 ] [ 4 ] It is one of the most popular deep learning frameworks, alongside others such as PyTorch and PaddlePaddle.
Deeplearning4j relies on the widely used programming language Java, though it is compatible with Clojure and includes a Scala application programming interface (API). It is powered by its own open-source numerical computing library, ND4J, and works with both central processing units (CPUs) and graphics processing units (GPUs).
Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions. [1]