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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 2. Linear Algebra with NumPy FREE CHAPTER 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

Summary

In this chapter, you explored the performance of different configurations when you perform compute-intensive linear algebra operations.

Benchmarking is a serious business, and you at least have the basic skills now to run benchmarks. The material you have studied in this chapter is nowhere near complete, but it gave you an idea where to start, and you can definitely improve on many things.

One thing you can look at is how performance metrics behave when you increase the size of vectors and matrices gradually. Ideally, you'll need more powerful hardware, but t2.micro instances are free in most cases or very cheap to provision.

As you will need to handle more compute-intensive workloads, it's important to understand what your options are, and which one will give you the best performance. You can run these kinds of simple experiment to at least have an idea about...

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