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2. NumPy - Wikipedia

   en.wikipedia.org/wiki/NumPy

   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]

3. SciPy - Wikipedia

   en.wikipedia.org/wiki/SciPy

   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.

4. Python (programming language) - Wikipedia

   en.wikipedia.org/wiki/Python_(programming_language)

   Python uses the + operator for string concatenation. Python uses the * operator for duplicating a string a specified number of times. The @ infix operator is intended to be used by libraries such as NumPy for matrix multiplication. [103] [104] The syntax :=, called the "walrus operator", was introduced in Python 3.8. It assigns values to ...

5. CuPy - Wikipedia

   en.wikipedia.org/wiki/CuPy

   CuPy is an open source library for GPU-accelerated computing with Python programming language, providing support for multi-dimensional arrays, sparse matrices, and a variety of numerical algorithms implemented on top of them. [3] CuPy shares the same API set as NumPy and SciPy, allowing it to be a drop-in replacement to run NumPy/SciPy code on GPU.

6. scikit-learn - Wikipedia

   en.wikipedia.org/wiki/Scikit-learn

   scikit-learn (formerly scikits.learn and also known as sklearn) is a free and open-source machine learning library for the Python programming language. [3] It features various classification, regression and clustering algorithms including support-vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific ...

7. Google JAX - Wikipedia

   en.wikipedia.org/wiki/Google_JAX

   Google JAX is a machine learning framework for transforming numerical functions. [1] [2] [3] It is described as bringing together a modified version of autograd (automatic obtaining of the gradient function through differentiation of a function) and TensorFlow's XLA (Accelerated Linear Algebra).

8. pandas (software) - Wikipedia

   en.wikipedia.org/wiki/Pandas_(software)

   By default, a Pandas index is a series of integers ascending from 0, similar to the indices of Python arrays. However, indices can use any NumPy data type, including floating point, timestamps, or strings. [4]: 112 Pandas' syntax for mapping index values to relevant data is the same syntax Python uses to map dictionary keys to values.

9. Row- and column-major order - Wikipedia

   en.wikipedia.org/wiki/Row-_and_column-major_order

   Row-major order is the default in NumPy [19] (for Python). Column-major order is the default in Eigen [20] and Armadillo (both for C++). A special case would be OpenGL (and OpenGL ES) for graphics processing.