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[3] the predecessor of numpy, numeric, was originally created by jim hugunin with contributions from several other developers Numba was started by travis oliphant in 2012 and has since been under active development with frequent releases Travis oliphant is an american data scientist, software developer, and entrepreneur known for his contributions to the python scientific computing ecosystem

He is the primary creator of numpy, a foundational package for numerical computation in python, and a founding contributor to scipy, which together form the bedrock on which modern ai and machine learning development was built It offers a range of options for parallelising python code for cpus and gpus, often with only minor code changes Numpy serves as the backbone for a number of other numerical libraries, notably scipy

De facto standard for matrix/tensor operations in python.

In the broad sense, a scientific programming language is one that is applied to numerical modeling, simulation, data analysis, and visualization [3] conversely, the strict sense emphasizes languages that provide built‐in. [1] it has a simple, flexible and easily readable syntax [2] its popularity results in a vast ecosystem of libraries, including for deep learning, such as pytorch, tensorflow, keras, google jax

The library numpy can be used for manipulating arrays, scipy for scientific and mathematical. They are a core facility in newer languages, such as julia and recent versions of fortran These capabilities are also provided via standard extension libraries for other general purpose programming languages (such as the widely used numpy library for python). Matplotlib (portmanteau of matlab, plot, and library [3]) is a plotting library for the python programming language and its numerical mathematics extension numpy

There is also a procedural pylab interface based on a state machine (like opengl.

Its design philosophy emphasizes code readability with the use of significant indentation Guido van rossum began working on python in. Designed to work with other languages, including c, c++, c#, java, fortran, python, etc Can be compiled to multithreaded c++ (and optionally opencl) code with no explicit indications from the programmer of how or what to parallelize.

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