Mastering Numerical Computing With NumPy · Master Scientific Computing and Perform Complex Operations With Ease by Cuhadaroglu, Mert -- Read -- Imperial Library of Trantor

Index

Title Page Copyright and Credits

Mastering Numerical Computing with NumPy

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Preface

Who this book is for What this book covers To get the most out of this book

Download the example code files Download the color images Conventions used

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Reviews

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

SciPy and 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 Summary

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