### Numerical Linear Algebra for Programmers

### aren’t these two friends adorable? the book is too!

#### or scroll down to read the pitch, download sample chapters,

Open Source Libraries

##### Why?

#### basically…

# a book for programmers

#### interactive & dynamic

#### a direct link from theory to implementation

#### incredible performance

#### Intel & AMD CPUs (MKL)

#### Nvidia GPUs (CUDA and cuBLAS)

#### AMD GPUs (yes, OpenCL too!)

#### Clojure (it’s magic!)

#### Java Virtual Machine (without Java boilerplate!)

#### complete source code

#### beautiful typesetting (see the sample chapters below)

##### No Middleman

### no middleman!

### 100% of the revenue goes towards my open source work!

##### For Programmers

## the only AI book that walks the walk

### complete, 100% executable, code inside

### step-by-step instructions

### full path from theory to implementation *in actual code*

### superfast implementation

Other books are either math-only monographs for academics, or written for non-technical end users.

##### Linear Algebra

# learn linear algebra with code examples

#### explore it on the CPU

#### run it on the GPU!

#### integrate with Intel’s MKL and Nvidia’s cuBLAS performance library

#### learn the nuts and bolts

#### understand how to use it to solve practical problems

#### …and much more!

##### Interactive

# immediate dynamic feedback

#### see the result of executing each line

#### experiment in a live environment

#### no C++ build hell

### no C++ syntax hell!

#### Java Virtual Machine, but without Java boilerplate

#### Clojure, the nicest language on earth :-)

# no C++ at all!!!

##### Fast

# optimized

### yet, high-level (you don’t touch C++)

## CPUs

#### learn Intel MKL

## GPUs

#### learn CUDA & cuBLAS

#### learn OpenCL

#### all hardware: Nvidia, AMD, Intel

### you don’t touch C++!!!

##### Download

# download sample chapters (DRAFTS)

##### buy

#### read drafts now

#### get new chapters as they are written

#### all revenue goes towards funding my work on the open source libraries used in the book

#### your name in the book’s acknowledgments

#### special copy with a personalized thank you note

#### personalized handcrafted hardcovers for chapter sponsors

##### Contents

## Table of Contents

### Part 1: Getting Started (IN PROGRESS)

#### Vectors, matrices, and linear algebra API (SOON)

#### Polymorphic acceleration (SOON)

### Part 2: Linear algebra refresher (AVAILABLE)

#### Eigenvalues and eigenvectors (AVAILABLE)

#### Matrix transformations (AVAILABLE)

#### Linear transformations (AVAILABLE)

### Part 3: High performance matrix computations (AVAILABLE)

#### Using matrices efficiently (AVAILABLE)

#### Linear systems and factorization (AVAILABLE)

#### Singular value decomposition (SVD) (AVAILABLE)

#### Orthogonalization and least squares (AVAILABLE)

### Part 4: GPU acceleration

#### GPU computing crash course

#### CUDA and cuBLAS on Nvidia GPUs

#### OpenCL and CLBlast on AMD GPUs

### Part 5: In practice (AVAILABLE)

#### Generating random matrices (AVAILABLE)

#### Mean, variance, and correlation (AVAILABLE)

#### Principal component analysis (PCA) (AVAILABLE)

### Appendix

#### Setting up the environment and the JVM

##### Subscribe

#### read drafts now

#### get new chapters as they are written

#### all revenue goes towards funding my work on the open source libraries used in the book

#### your name in the book’s acknowledgments

#### special copy with a personalized thank you note

#### personalized handcrafted hardcovers for chapter sponsors

## get in early and help make this book awesome!

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