### Numerical Linear Algebra for Programmers

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

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

Open Source Libraries

##### Why

#### basically…

# a book for programmers

#### interactive & dynamic

#### direct link from theory to implementation

#### incredible speed

#### Nvidia GPU (CUDA and cuBLAS)

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

#### Intel & AMD CPU (MKL)

#### Clojure (magic!)

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

#### complete source code

#### beautiful typesetting (see 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

#### put it on the GPU!

#### integrate it with Intel’s MKL performance libray

#### integrate it with Nvidia’s cuBLAS performance library

#### learn nuts and bolts

#### understand how to use it

#### …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++!

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

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

# no C++!!!

##### Fast

# optimized

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

## GPU

#### learn CUDA & cuBLAS

#### learn OpenCL

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

## CPU

#### learn Intel MKL

### you don’t touch C++!!!

##### Download

# download sample chapters (DRAFTS)

##### buy

#### read drafts now

#### get new chapters as soon as they are written

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

#### name in the acknowledgments

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

#### personalized handcrafted hardcovers for chapter sponsors

##### Contents

## Table of Contents

### Part 1: Getting Started (SOON)

#### Hello world (SOON)

#### 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 (IN PROGRESS)

#### Use matrices efficiently (AVAILABLE)

#### Linear systems and factorization (AVAILABLE)

#### Orthogonalization and least squares (SOON)

### Part 4: GPU acceleration

#### GPU computing crash course

#### CUDA and cuBLAS on Nvidia GPU

#### OpenCL and CLBlast on AMD GPU

### Part 5: In practice

#### Random matrices

#### Mean and variance

#### Broadcasting

#### Principal component analysis

### Appendices

#### Setting up the environment and the JVM

##### Subscribe

#### read drafts now

#### get new chapters as soon as they are written

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

#### name in the acknowledgments

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

#### personalized handcrafted hardcovers for chapter sponsors

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

##### More Books

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