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.
A module is a file that ends with an extension of .py
that contains one or more Python functions, classes, global variables, etc.
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
.
The framework is the foundation of the development of a web application that contains multiple packages. Ex. Django, Flask
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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