Introduction to the Math of Neural Networks released to beta

I am trying something new with my books. I am making the ebooks available for purchase while they are still in a "beta" state. This worked well with the Encog3 book, and now I am going to try it with my latest book.

This book is called "Introduction to the Math of Neural Networks". It is currently in "complete draft" form. This means I've written every chapter, and it is about to enter "editing". This will both be review/editing by myself(the author). I will be looking for, and correcting any errors. The book will also be professionally edited. If you buy the beta now, you will will also get the final version, as well.

This book is also a "short book". I am planning on releasing a series of shorter books that focus on very specific topics. This book has 112 pages, and is targeted primarily as an ebook. Because it is shorter, it is also less expensive, only $9.99. More information about purchase can be found here.

http://www.heatonresearch.com/book/introduction-neural-network-math.html

What is this book about? Here is a section from the book's introduction...

If you have read other books by me you will know that I try to shield the reader from the mathematics behind AI. Often you do not need to know the exact math that is used to train a neural network or perform a cluster operation. You simply want the result.
This is very much the idea of the Encog project. Encog is an advanced machine learning framework that allows you to perform many advanced operations such as neural networks, genetic algorithms, support vector machines, simulated annealing, and other machine learning methods. You are allowed to use these advanced techniques without the need to know what is happening behind the scenes.

However, sometimes you really do want to know what is going on behind the scenes. You do want to know that math that is involved. In this book you will learn what happens, behind the scenes, with a neural network. You will also be exposed to the math. I will present the material in mathematical terms.

There are already many neural network books that at first glance would appear as a math text. This is not what I seek to produce here. There are already several very good books that achieve a pure mathematical introduction to neural networks. My goal is to produce a mathematically based neural network book that targets someone with perhaps only a college algebra and computer programming background. These are the only two prerequisites for this book. Actually, there is a third prerequisite, but I will get to that in a moment.

Neural networks overlap several bodies of mathematics. Neural network goals, such as classification, regression and clustering come from statistics. The gradient descent that goes into backpropagation, and other training methods, requires knowledge of Calculus. Advanced training, such as Levenberg Marquardt, require both Calculus and Matrix Mathematics.
To read nearly any academic level neural network, or machine learning, targeted book you will need some knowledge of Algebra, Calculus, Statistics and Matrix Mathematics. However, the reality is only need a relatively small amount of knowledge from each of these areas. The goal of this book, is to teach you enough math to understand neural networks and their training. You will understand exactly how a neural network functions, and should be able to implement your own in any computer language you are familiar with.

As areas of mathematics are needed, I will provide an introductory-level tutorial on the math. I only assume that you know basic algebra to start out with. This book will discuss such mathematical concepts as derivatives, partial derivatives, matrix transformation, gradient descent and more.

If you have not done this sort of math in awhile, I plan for this book to be a good refresher. If you have never done this sort of math, then this book could serve as a good introduction. If you are very familiar with math, you can still learn neural networks from this book. However, you may want to skip some of the sections that seem too basic.

This book is not about Encog. Nor is it about how to program in any particular programming language. I assume you will likely apply these principles to programming languages. If you want examples of how I apply the principles in this book there is Encog. This book is really more about the algorithms and mathematics behind neural networks.

I did say there was one other prerequisite to this book. That is, other than basic algebra and programming knowledge in any language. That is knowledge of what a neural network is, and how it is used. You should already be familiar with what neural networks are and how they are used. If you do not yet know how to use a neural network, you may want to start here.

http://goo.gl/8ESx

The above article, that I wrote, provides a brief crash course on what neural networks are. You may want to look at some of the Encog examples, as well. You can find more information about Encog at the following URL.

http://www.heatonresearch.com/encog/

If neural networks are cars, then this book is a mechanics guide. If I am going to teach you to repair and build cars, I have two basic assumptions in order of importance. First is that you’ve actually seen a car, and know what one is used for. The second assumption is that you can actually drive a car. If neither of these are true, then why do you care about learning the internals of how a car works? The same is true of neural networks.

To purchase this book, visit the following link.

http://www.heatonresearch.com/book/introduction-neural-network-math.html