In this book, we aim to introduce the subject of neural networks as directly and simply as possible for an easy
understanding of the methodology. Most of the important neural network architectures are covered, and we
earnestly hope that our efforts have succeeded in presenting this subject matter in a clear and useful fashion.
The number of models available in neural network literature is quite large. Very often the treatment is
mathematical and complex. This book provides illustrative examples in C++ that the reader can use as a basis
for further experimentation. A key to learning about neural networks to appreciate their inner workings is to
experiment. Neural networks, in the end, are fun to learn about and discover. Although the language for
description used is C++, you will not find extensive class libraries in this book. With the exception of the
backpropagation simulator, you will find fairly simple example programs for many different neural network
architectures and paradigms. Since backpropagation is widely used and also easy to tame, a simulator is
provided with the capacity to handle large input data sets. You use the simulator in one of the chapters in this
book to solve a financial forecasting problem. You will find ample room to expand and experiment with the
code presented in this book.