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High-performance mathematical libraries for tensor and matrix operations implemented in JavaScript.
Distinct from Linear Algebra Routines: The candidates focus on general linear algebra or specific sparse routines; this targets the JS ecosystem specifically.
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Deeplearnjs is a JavaScript deep learning framework and automatic differentiation engine designed for building and training artificial intelligence models within a web browser environment. It functions as a machine learning library that leverages WebGL to provide hardware acceleration for neural networks. The project serves as a high-performance linear algebra library, using the GPU to execute operations on multi-dimensional arrays. This enables the implementation of deep learning models and the execution of client-side machine learning inference. The framework covers the complete automatic
Provides a high-performance toolkit for complex mathematical operations on multi-dimensional arrays within JavaScript.
Accelerates linear algebra by calling native BLAS libraries with pure JavaScript fallbacks.