This book is intended primarily for readers who already have at least a basic knowledge of neural networks but are interested in learning about, experimenting with, and perhaps even programming deep belief nets. The salient features of this book are the following:
• The book provides motivation for the deep belief net paradigm.
• It presents the most important equations for the most common deep belief net components and justi es them to a modest degree.
• e book provides training, execution, and analysis algorithms for common deep belief net paradigms in language-independent forms.
• is book serves as a detailed user’s manual for the DEEP program, which is available as a free download from the author’s web site.
I describe the internal operations of the program in depth.
• The book provides C++ code for many essential deep belief net algorithms. This includes versions for multiple-thread execution on Windows-based computers, as well as CUDA C implementations for using the supercomputer capabilities of NVIDIA CUDA-capable GPU cards.
It must be noted that several items are not included in this book.
• I largely avoid detailed mathematical theory. If we want to understand the quite advanced theory behind deep belief nets, numerous papers are available on the Internet. I will identify a few of the best later in this chapter.
• I present only those models I found to be of greatest practical, real-world value in my own work. This does not imply that the omitted models are inferior, only that I have not found them to be outstandingly useful in my particular applications.
