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This article mainly introduces "what are the commonly used machine learning python libraries". In daily operation, I believe many people have doubts about which commonly used machine learning python libraries. The editor consulted all kinds of materials and sorted out simple and easy-to-use operation methods. I hope it will be helpful to answer the questions of "what commonly used machine learning python libraries". Next, please follow the editor to study!

1. NumPy: a general array processing software package that provides high-performance multidimensional array objects and tools for processing these arrays, and a basic python software package for scientific computing. The core function of NumPy is ndarray, which represents the n-dimensional array data structure.

2. Scipy: a free and open source python library for scientific and technical computing. Contains modules for optimization, linear algebra, integration, interpolation, special functions, FFT, signal and image processing, ODE solvers, and other common tasks in science and engineering. The basic data structure used by Scipy is a multidimensional array provided by the NumPy module.

3. Tensorflow: a free and open source software library for data flow and differentiable programming for a variety of tasks. It is the second generation system of Google Brain. TensorFlow can run on multiple CPU and GPU, while TensorFlow can be used on 64-bit Linux, macOS, Windows and mobile computing platforms.

4. Pytorch: an open source machine learning library based on Torch library for applications such as computer vision and natural language processing, mainly developed by Facebook's AI research laboratory. Has a better python interface, mainly focusing on development, as well as C++ interface.

5. Keras: an open source neural network library written in python that can run on TensorFlow, Microsoft Cognitive Toolkit, R, Theano or PlaidML. Committed to easy to use, modular and extensible.

At this point, the study of "what are the commonly used machine learning python libraries" is over. I hope to be able to solve your doubts. The collocation of theory and practice can better help you learn, go and try it! If you want to continue to learn more related knowledge, please continue to follow the website, the editor will continue to work hard to bring you more practical articles!

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