A small preface
"Originally, this work has been prepared in the framework of a seminar of the
University of Bonn in Germany, but it has been and will be extended (after
being presented and published online under www.dkriesel.com on
5/27/2005). First and foremost, to provide a comprehensive overview of the
subject of neural networks and, second, just to acquire more and more
knowledge about L
A
T
E
X . And who knows – maybe one day this summary will
become a real preface!"
Abstract of this work, end of 2005
The above abstract has not yet become a
preface but at least a little preface, ever
since the extended text (then 40 pages
long) has turned out to be a download
hit.
Ambition and intention of this
manuscript
The entire text is written and laid out
more effectively and with more illustra-
tions than before. I did all the illustra-
tions myself, most of them directly in
L
A
T
E
X by using XYpic. They reflect what
I would have liked to see when becoming
acquainted with the subject: Text and il-
lustrations should be memorable and easy
to understand to offer as many people as
possible access to the field of neural net-
works.
Nevertheless, the mathematically and for-
mally skilled readers will be able to under-
stand the definitions without reading the
running text, while the opposite holds for
readers only interested in the subject mat-
ter; everything is explained in both collo-
quial and formal language. Please let me
know if you find out that I have violated
this principle.
The sections of this text are mostly
independent from each other
The document itself is divided into differ-
ent parts, which are again divided into
chapters. Although the chapters contain
cross-references, they are also individually
accessible to readers with little previous
knowledge. There are larger and smaller
chapters: While the larger chapters should
provide profound insight into a paradigm
of neural networks (e.g. the classic neural
network structure: the perceptron and its
learning procedures), the smaller chapters
give a short overview – but this is also ex-
v
