Deep Learning for Programmers

Interactive Programming for Artificial Intelligence series; Deep Learning for Programmers: An Interactive Tutorial with CUDA, OpenCL, MKL-DNN, Java, and Clojure

aren’t these surfers adorable? the book is too!

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

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Open Source Libraries

Why?

basically…

this is the only DL book for programmers

interactive & dynamic

step-by-step implementation

incredible performance, yet no C++ hell (!)

Intel & AMD CPUs (MKL-DNN)

Nvidia GPUs (CUDA and cuDNN)

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

step-by-step instructions

full path from theory to implementation in actual code

superfast implementation

Other books are math-only monographs for academics, or are written for non-technical readers.

Deep Learning

learn DL by implementing it from scratch

classic neural networks using fast linear algebra

build an optimized backpropagation algorithm step-by-step

explore it on the CPU

run it on the GPU!

design an elegant neural network API

add tensor support

integrate with Intel’s MKL-DNN and Nvidia’s cuDNN performance libraries

learn the nuts and bolts

build convolutional layers

build RNN support

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-DNN

GPUs

learn CUDA & cuDNN

learn OpenCL

all hardware: Nvidia, AMD, Intel

you don’t touch C++!!!

Download

download sample chapters (DRAFTS)

Representing Layers and Connections

Bias and Activation Function

GPU Computing with CUDA and OpenCL

many free articles at dragan.rocks

or subscribe now and get the latest version of the book and the the source code.

buy

subscribe now for early access

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

subscription perks

your name in the book’s acknowledgments

special copy with a personalized thank you note

personalized handcrafted hardcovers for chapter sponsors

get the book and help make it awesome!

Contents

Table of Contents

Part 1: Getting Started

4-6 chapters, (TO BE DETERMINED)

Part 2: Inference (AVAILABLE)

Representing layers and connections (AVAILABLE)

Bias and activation function (AVAILABLE)

Fully connected inference layers (AVAILABLE)

Increasing performance with batch processing (AVAILABLE)

Sharing memory (AVAILABLE)

GPU computing with CUDA and OpenCL (AVAILABLE)

Part 3: Learning (AVAILABLE)

Gradient descent and backpropagation (AVAILABLE)

The forward pass (AVAILABLE)

The activation and its derivative (AVAILABLE)

The backward pass (AVAILABLE)

Part 4: A simple neural networks API (AVAILABLE)

Inference API (AVAILABLE)

Training API (AVAILABLE)

Initializing weights (AVAILABLE)

Regression: learning a known function (AVAILABLE)

Part 5: Training optimizations (IN PROGSESS)

Weight decay (AVAILABLE)

Momentum and Nesterov momentum (AVAILABLE)

Adaptive learning rates (AVAILABLE)

Regression: Boston housing prices (AVAILABLE)

Dropout (SOON)

Stochastic gradient descent (SOON)

Classification: IMDB sentiments (SOON)

Part 6: Tensors (TO BE DETERMINED, BUT SOON ENOUGH)

Tensors, Matrices, and ND-arrays (TBD)

Tensors on the CPU with MKL-DNN (TBD)

Tensors on the GPU with cuDNN (TBD)

Tensor API (TBD)

Part 7: Convolutional layers (TBD)

4-6 Chapters, (TBD)

Part 8: Recurrent networks (TBD)

4-6 Chapters, (TBD) 1

Subscribe

subscribe now for early access

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

subscription perks

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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Numerical Linear Algebra for Programmers

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Interactive Programming for Artificial Intelligence series; Numerical Linear Algebra for Programmers: An Interactive Tutorial with GPUs, CUDA, OpenCL, MKL, Java, and Clojure