Libraries for Numerical Analysis

NumPy is a fundamental library for scientific computing in Python, offering a wide range of functions for numerical operations, linear algebra, and data manipulation.

SciPy builds upon NumPy and offers a collection of modules for optimization, integration, linear algebra, statistics, and more.

TensorFlow is a popular library for deep learning, providing a flexible framework for building and training neural networks.

PyTorch is a popular library for deep learning, known for its dynamic computation graph and ease of use.

Matplotlib is a library for creating 2D plots and interactive visualizations of data.

Pandas is a library for data analysis and manipulation, offering data structures and operations for working with large datasets.

Sympy is a library for symbolic mathematics, providing tools for manipulating mathematical expressions and performing symbolic calculations.

BLAS (Basic Linear Algebra Subprograms) is a library for high-performance linear algebra operations, widely used in scientific computing.

LAPACK (Linear Algebra PACKage) is a library for solving linear systems, eigenvalue problems, and other matrix computations.

CVXPY is a library for optimization, providing algorithms for finding minima or maxima of functions.

Statsmodels is a library for high-performance statistical computing, providing a wide range of statistical functions and methods.

Scikit-learn is a library for machine learning, providing algorithms for classification, regression, and clustering.

Dask is a library for high-performance data analysis and manipulation, including operations like filtering, sorting, and grouping.

Ray is a library for parallel and distributed computing, allowing users to distribute tasks across multiple cores or machines.

QUADPACK is a library for high-performance numerical integration and differentiation routines.