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Mastering Numerical Computing with NumPy

By : Mert Cakmak, Tiago Antao, Cuhadaroglu

5 (1)

Buy this Book

Mastering Numerical Computing with NumPy

5 (1)

By: Mert Cakmak, Tiago Antao, Cuhadaroglu

Buy this Book

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.

Preface

Who this book is for

What this book covers

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Free Chapter

Working with NumPy Arrays

Technical requirements

Why do we need NumPy?

Who uses NumPy?

Introduction to vectors and matrices

Basics of NumPy array objects

NumPy array operations

Working with multidimensional arrays

Indexing, slicing, reshaping, resizing, and broadcasting

Summary

Linear Algebra with NumPy

Vector and matrix mathematics

What's an eigenvalue and how do we compute it?

Computing the norm and determinant

Solving linear equations

Computing gradient

Summary

Exploratory Data Analysis of Boston Housing Data with NumPy Statistics

Loading and saving files

Exploring our dataset

Looking at basic statistics

Computing histograms

Explaining skewness and kurtosis

Trimmed statistics

Box plots

Computing correlations

Summary

Predicting Housing Prices Using Linear Regression

Supervised learning and linear regression

Independent and dependent variables

Hyperparameters

Loss and error functions

Univariate linear regression with gradient descent

Using linear regression to model housing prices

Summary

Clustering Clients of a Wholesale Distributor Using NumPy

Unsupervised learning and clustering

Hyperparameters

The loss function

Implementing our algorithm for a single variable

Modifying our algorithm

Summary

NumPy, SciPy, Pandas, and Scikit-Learn

NumPy and SciPy

NumPy and pandas

SciPy and scikit-learn

Summary

Advanced Numpy

NumPy internals

Summary

Overview of High-Performance Numerical Computing Libraries

BLAS and LAPACK

ATLAS

Intel Math Kernel Library

OpenBLAS

Configuring NumPy with low-level libraries using AWS EC2

Compute-intensive tasks for benchmarking

Summary

Performance Benchmarks

Why do we need a benchmark?

Preparing for a performance benchmark

Results

Summary

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Results

Of course, the t2.micro instance is fairly weak and you should know more about how Amazon provides this computing power for EC2 instances. You can read more about them at https://aws.amazon.com/ec2/instance-types/.

If you use more powerful machines with a higher number of cores, the performance difference will be more visible between different configurations.

When it comes to results, it's no surprise that the default installation of BLAS and LAPACK gave us the baseline performance and optimized versions, such as OpenBLAS, ATLAS, and Intel MKL, gave better performance.

As you have noted, you haven't changed a single line of code in your Python script and by just linking your NumPy library against different accelerators, you had huge performance gains.

If you will dig deeper into these low-level libraries to understand what are the specific routines and functions...