Book Image

SciPy Recipes

By : V Kishore Ayyadevara, Ruben Oliva Ramos
Book Image

SciPy Recipes

By: V Kishore Ayyadevara, Ruben Oliva Ramos

Overview of this book

With the SciPy Stack, you get the power to effectively process, manipulate, and visualize your data using the popular Python language. Utilizing SciPy correctly can sometimes be a very tricky proposition. This book provides the right techniques so you can use SciPy to perform different data science tasks with ease. This book includes hands-on recipes for using the different components of the SciPy Stack such as NumPy, SciPy, matplotlib, and pandas, among others. You will use these libraries to solve real-world problems in linear algebra, numerical analysis, data visualization, and much more. The recipes included in the book will ensure you get a practical understanding not only of how a particular feature in SciPy Stack works, but also of its application to real-world problems. The independent nature of the recipes also ensure that you can pick up any one and learn about a particular feature of SciPy without reading through the other recipes, thus making the book a very handy and useful guide.
Table of Contents (11 chapters)

Calculating the singular value decomposition of a matrix

Singular value decomposition (SVD) is one of the more useful techniques in typical data science techniques.

  • One of the most important applications of SVD is in recommendation systems, where the matrix of user-item purchase behavior is broken into multiple matrices that are simpler to implement.
  • Similarly, SVD is used in image compression algorithms, where we try to capture the information within algorithms by using as few pixels as possible.
The SVD of a matrix A is the decomposition or factorization of A into the product of three matrices: A=UxΣxVt.

The size of the individual matrices is as follows, if you know that matrix A is of size M x N:

  • Matrix U is of size M x M
  • Matrix V is of size N x N
  • Matrix Σ is of size M x N
...