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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

Intel Math Kernel Library

Intel MKL optimizes BLAS for Intel processors. There are improved routines and functions such as Level 1, 2 and 3 BLAS, LAPACK routines, solvers, FFT functions, other math and statistical functions. These improved routines and functions benefit from improvements like shared memory multiprocessing and they are used to accelerate scientific python libraries such as NumPy and SciPy in distributions such as Anaconda Distribution. If you look at its release notes (https://software.intel.com/en-us/articles/intel-math-kernel-library-release-notes-and-new-features), you can see several important improvements were made with every release, such as the improved performance of LAPACK functions.

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