Optional dependencies

Some useful optional dependencies are listed in the following list:

• NumPy: This is an open source library of the Python programming language, which adds support for multidimensional and large vectors, and even matrices with high-level mathematical functions to work with.
• SciPy: This is an open source library of mathematical algorithms and tools. It contains modules for optimization, linear algebra, integration, special functions, fast fourier transform (FFT), signal and image processing, ordinary differential equation (ODT) solvers, and other common tools in science and engineering.
• Scikit-learn: This is an open source machine learning library for the Python programming language. It contains classification, regression, clustering algorithms, support vector machines, logistic regression, bayesian classifier, k-means, and DBSCAN, and is designed to work with the NumPy and SciPy libraries.
• cuDNN: This is a GPU accelerated library of primitives for deep neural networks. cuDNN provides highly tuned implementations for standard routines, such as forward and backward convolution, pooling, normalization, and activation layers.
• HDF5: This is a data model, library, and file format for storing and managing data. It supports an unlimited variety of datatypes, and is designed to be flexible and efficient.
• H5py: This is a Python interface to the HDF5 binary data format.
• Graphviz: This is an open source program used to draw graphs described in the DOT language. It provides libraries for applications using the tools provided. Graphviz is free software licensed under the Common Public License (CPL).
• Pydot: This is a Python interface for Graphviz and the DOT language.

We can now proceed with the installation of every single library or install all the dependencies with a single line of code. Alternatively, you can install the Anaconda Python module, which will automatically install these libraries and a lot of other libraries that are needed for scientific computing.