Name: Mastering Numerical Computing with NumPy
Author: Umit Mert Cakmak Mert Cuhadaroglu
ISBN: 9781788996846

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Mastering Numerical Computing with NumPy

Umit Mert Cakmak Mert Cuhadaroglu

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Contributors
About the authors
About the reviewer
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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
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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
Summary
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Mastering Numerical Computing with NumPy