If you have read other books I have written, you will know that I try to shield the reader from the mathematics behind Al. 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 results-based approach is very much the focus of the Encog project. Encog is an advanced machine learning framework that allows you to perform many advanced operations, such as neural networks, genetic algorithm, support vector machines, simulated annealing and other machine learning methods. Encog allows you to use these advanced techniques without needing 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 the 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. There are already many neural network hooks that at first glance 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 who has perhaps only college-level algebra and computer programing background. These are the only two prerequisites for understanding this book, aside from one more that I will mention later in this introduction. 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 bacicpropagation, along with 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 that you need only 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.
