Mastering Numerical Computing with NumPy

封面

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Contributors

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Preface

Who this book is for

What this book covers

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Conventions used

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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

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

Summary

Advanced Numpy

NumPy internals

How does NumPy manage memory?

Profiling NumPy code to understand the performance

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

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

Summary

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