[Machine learning] List of frequently used packages

In this article, I'd like to review some of the most commonly used packages in machine learning. Before we dive into the finer library types, I'd like to sort out the differences between python modules, packages, and frameworks.

Module

A module is a file that ends with an extension of .py that contains one or more Python functions, classes, global variables, etc.

Package

A package is a directory that combines multiple modules or packages. Also called a library in C or C ++. The package always contains a special file called _init_.py.

Framework

The framework is the foundation of the development of a web application that contains multiple packages. Ex. Django, Flask

List of packages used in machine learning

package name Use Reference document
Pandas Data frame processing https://pandas.pydata.org/pandas-docs/stable/
Numpy Matrix calculation https://docs.scipy.org/doc/numpy-1.17.0/user/
Scipy Matrix calculation https://docs.scipy.org/doc/scipy/reference/
Matplotlib drawing https://matplotlib.org/3.1.1/contents.html
Seaborn drawing https://seaborn.pydata.org
NetworkX Graph network calculation https://networkx.github.io
scikit-learn Machine learning https://scikit-learn.org/stable/
TensorFlow Deep learning https://www.tensorflow.org
PyTorch Deep learning https://pytorch.org
Keras Deep learning https://keras.io

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