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Mastering Numerical Computing with NumPy

You're reading from   Mastering Numerical Computing with NumPy Master scientific computing and perform complex operations with ease

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Product type Paperback
Published in Jun 2018
Publisher Packt
ISBN-13 9781788993357
Length 248 pages
Edition 1st Edition
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Authors (3):
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Tiago Antao Tiago Antao
Author Profile Icon Tiago Antao
Tiago Antao
Mert Cuhadaroglu Mert Cuhadaroglu
Author Profile Icon Mert Cuhadaroglu
Mert Cuhadaroglu
Umit Mert Cakmak Umit Mert Cakmak
Author Profile Icon Umit Mert Cakmak
Umit Mert Cakmak
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Table of Contents (11) Chapters Close

Preface 1. Working with NumPy Arrays FREE CHAPTER 2. Linear Algebra with NumPy 3. Exploratory Data Analysis of Boston Housing Data with NumPy Statistics 4. Predicting Housing Prices Using Linear Regression 5. Clustering Clients of a Wholesale Distributor Using NumPy 6. NumPy, SciPy, Pandas, and Scikit-Learn 7. Advanced Numpy 8. Overview of High-Performance Numerical Computing Libraries 9. Performance Benchmarks 10. Other Books You May Enjoy

Linear Algebra with NumPy

One of the major divisions of mathematics is algebra, and linear algebra in particular, focuses on linear equations and mapping linear spaces, namely vector spaces. When we create a linear map between vector spaces, we are actually creating a data structure called a matrix. The main usage of linear algebra is to solve simultaneous linear equations, but it can also be used for approximations for non-linear systems. Imagine a complex model or system that you are trying to understand, think of it as a non-linear model. In such cases, you can reduce the complex, non-linear characteristics of the problem into simultaneous linear equations, and you can solve them with the help of linear algebra.

In computer science, linear algebra is heavily used in machine learning (ML) applications. In ML applications, you deal with high-dimensional arrays, which can easily...

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