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Matrix Toolkit Java is a linear algebra library based on BLAS and LAPACK. ojAlgo is an open source Java library for mathematics, linear algebra and optimisation. exp4j is a small Java library for evaluation of mathematical expressions. SuanShu is an open-source Java math library. It supports numerical analysis, statistics and optimization.
JAX is a Python library that provides 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).
OptimJ is a mathematical Java-based modeling language for describing and solving high-complexity problems for large-scale optimization. Perl Data Language, [22] [23] also known as PDL, an array extension to Perl ver.5, used for data manipulation, statistics, numerical simulation and visualization.
NumPy (pronounced / ˈ n ʌ m p aɪ / NUM-py) is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays. [3]
Download and install the latest Java Virtual Machine in Internet Explorer. 1. Go to www.java.com. 2. Click Free Java Download. 3. Click Agree and Start Free Download. 4. Click Run. Notes: If prompted by the User Account Control window, click Yes. If prompted by the Security Warning window, click Run. 5.
jblas is a linear algebra library, created by Mikio Braun, for the Java programming language built upon BLAS and LAPACK. Unlike most other Java linear algebra libraries, jblas is designed to be used with native code through the Java Native Interface and comes with precompiled binaries. When used on one of the targeted architectures, it will ...
Dask is an open-source Python library for parallel computing.Dask [1] scales Python code from multi-core local machines to large distributed clusters in the cloud. Dask provides a familiar user interface by mirroring the APIs of other libraries in the PyData ecosystem including: Pandas, scikit-learn and NumPy.
Keras allows users to produce deep models on smartphones (iOS and Android), on the web, or on the Java Virtual Machine. [8] It also allows use of distributed training of deep-learning models on clusters of graphics processing units (GPU) and tensor processing units (TPU) .