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.
Mastering Numerical Computing with NumPy
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Mastering Numerical Computing with NumPy
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Overview of this book
NumPy is one of the most important scientific computing libraries available for Python. Mastering Numerical Computing with NumPy teaches you how to achieve expert level competency to perform complex operations, with in-depth coverage of advanced concepts.
Beginning with NumPy's arrays and functions, you will familiarize yourself with linear algebra concepts to perform vector and matrix math operations. You will thoroughly understand and practice data processing, exploratory data analysis (EDA), and predictive modeling. You will then move on to working on practical examples which will teach you how to use NumPy statistics in order to explore US housing data and develop a predictive model using simple and multiple linear regression techniques. Once you have got to grips with the basics, you will explore unsupervised learning and clustering algorithms, followed by understanding how to write better NumPy code while keeping advanced considerations in mind. The book also demonstrates the use of different high-performance numerical computing libraries and their relationship with NumPy. You will study how to benchmark the performance of different configurations and choose the best for your system.
By the end of this book, you will have become an expert in handling and performing complex data manipulations.
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Current Title:
Mastering Numerical Computing with NumPy
Table of Contents (11 chapters)
Preface
Free Chapter
Working with NumPy Arrays
Linear Algebra with NumPy
Exploratory Data Analysis of Boston Housing Data with NumPy Statistics
Predicting Housing Prices Using Linear Regression
Clustering Clients of a Wholesale Distributor Using NumPy
NumPy, SciPy, Pandas, and Scikit-Learn
Advanced Numpy
Overview of High-Performance Numerical Computing Libraries
Performance Benchmarks
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