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SciPy (pronounced / ˈ s aɪ p aɪ / "sigh pie" [2]) is a free and open-source Python library used for scientific computing and technical computing. [3]SciPy contains modules for optimization, linear algebra, integration, interpolation, special functions, FFT, signal and image processing, ODE solvers and other tasks common in science and engineering.
Dlib is a modern C++ library with easy to use linear algebra and optimization tools which benefit from optimized BLAS and LAPACK libraries. Eigen is a vector mathematics library with performance comparable with Intel's Math Kernel Library; Hermes Project: C++/Python library for rapid prototyping of space- and space-time adaptive hp-FEM solvers.
The National Programme on Technology Enhanced Learning(NPTEL) is an Indian e-learning platform for university-level science, technology, engineering, and mathematics (STEM) subjects. NPTEL is the largest e-repository in the world of courses in engineering, basic sciences and selected humanities and management subjects. [1]
In computer programming, a scientific programming language can refer to two degrees of the same concept. In a wide sense, a scientific programming language is a programming language that is used widely for computational science and computational mathematics. In this sense, C/C++ and Python can be considered scientific programming languages.
Python [24] [25] with well-known scientific computing packages: NumPy, SymPy and SciPy. [26] [27] [28] R is a widely used system with a focus on data manipulation and statistics which implements the S language. [29] Many add-on packages are available (free software, GNU GPL license). SAS, [30] a system of software products for statistics.
SciPy – Python library for scientific computing that contains the stats sub-package which is partly based on the venerable |STAT (a.k.a. PipeStat, formerly UNIX|STAT) software scikit-learn – extends SciPy with a host of machine learning models (classification, clustering, regression, etc.)
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]
The FEniCS Project is a collection of free and open-source software components with the common goal to enable automated solution of differential equations.The components provide scientific computing tools for working with computational meshes, finite-element variational formulations of ordinary and partial differential equations, and numerical linear algebra.