Mastering numerical computing with NumPy : master the skills necessary for performing complex numerical computations and effective data manipula / Mert Cuhadaroglu, Umit Mert Cakmak.

Format:

Book

Author/Creator:

Cuhadaroglu, Mert, author.

Cakmak, Umit Mert, author.

Language:

English

Subjects (All):

Cloud computing.

Physical Description:

1 online resource (237 pages)

Edition:

1st edition

Place of Publication:

Birmingham ; Mumbai : Packt, 2018.

System Details:

text file

Summary:

Enhance the power of NumPy and start boosting your scientific computing capabilities About This Book Grasp all aspects of numerical computing and understand NumPy Explore examples to learn exploratory data analysis (EDA), regression, and clustering Access NumPy libraries and use performance benchmarking to select the right tool Who This Book Is For Mastering Numerical Computing with NumPy is for you if you are a Python programmer, data analyst, data engineer, or a data science enthusiast, who wants to master the intricacies of NumPy and build solutions for your numeric and scientific computational problems. You are expected to have familiarity with mathematics to get the most out of this book. What You Will Learn Perform vector and matrix operations using NumPy Perform exploratory data analysis (EDA) on US housing data Develop a predictive model using simple and multiple linear regression Understand unsupervised learning and clustering algorithms with practical use cases Write better NumPy code and implement the algorithms from scratch Perform benchmark tests to choose the best configuration for your system In Detail 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. Style and approach This masterin...

Contents:

Cover

Title Page

Copyright and Credits

Packt Upsell

Contributors

Table of Contents

Preface

Chapter 1: 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

Chapter 2: 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

Chapter 3: 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

Chapter 4: 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

Chapter 5: Clustering Clients of a Wholesale Distributor Using NumPy

Unsupervised learning and clustering

The loss function

Implementing our algorithm for a single variable

Modifying our algorithm

Chapter 6: NumPy, SciPy, Pandas, and Scikit-Learn

NumPy and SciPy

Linear regression with SciPy and NumPy

NumPy and pandas

Quantitative modeling with stock prices using pandas

SciPy and scikit-learn

K-means clustering in housing data with scikit-learn

Chapter 7: Advanced Numpy

NumPy internals

How does NumPy manage memory?.

Profiling NumPy code to understand the performance

Chapter 8: 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

Installing BLAS and LAPACK

Installing OpenBLAS

Installing Intel MKL

Installing ATLAS

Compute-intensive tasks for benchmarking

Matrix decomposition

Singular-value decomposition

Cholesky decomposition

Lower-upper decomposition

Eigenvalue decomposition

QR decomposition

Working with sparse linear systems

Chapter 9: Performance Benchmarks

Why do we need a benchmark?

Preparing for a performance benchmark

Performance with BLAS and LAPACK

Performance with OpenBLAS

Performance with ATLAS

Performance with Intel MKL

Results

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Index.

Notes:

Description based on print version record.

ISBN:

9781788996846

1788996844

OCLC:

1043647876