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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

Compute-intensive tasks for benchmarking

Now, you'll be able to benchmark NumPy performance using different configurations, such as with or without BLAS/LAPACK, OpenBLAS, ATLAS, and Intel MKL. Let's review what you are going to compute for benchmarks.

Matrix decomposition

Matrix decomposition, or factorization methods, involves calculating the constituents of a matrix so that they can be used to simplify more demanding matrix operations. In practice, this means breaking the matrix you have into more than one matrix so that, when you calculate the product of these smaller matrices, you get your original matrix back. Some examples of matrix decomposition methods are singular-value decomposition (SVD), eigenvalue decomposition...

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