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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

Why do we need a benchmark?

As you advance with your programming skills, you will start to implement more efficient programs. You will search dozens of code repositories to see how others are solving similar problems, and you will find those rare gems that will amaze you.

Throughout this progress of writing better software and implementing systems, you will need ways to measure and track the rate of improvement. You will generally consider your starting point as a baseline and see how the improvements you make will add up to performance metrics.

Once you set the baseline, you will benchmark several different implementations and will have a chance to compare these in terms of the performance metrics you choose. You can choose various metrics and need to decide these in advance.

Performance metrics for these benchmarks will be kept rather simple, and only the time spent metric will...

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