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xviii Introduction
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 program-
ming 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