 
FPGA Tutorial: Free 'Insider's Guide' (2009 Edition)
Altera? Xilinx? FPGA boards or design tools as from Altium or Mentor Graphics? What do users and newbies think about new FPGA technologies? Find out in our free tutorial on FPGAs, tools, and boards.
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book
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A Statistical Approach to Neural Networks for Pattern Recognition
This book presents a statistical treatment of the Multilayer Perceptron (MLP), which is the most widely used of the neural network models, in a language that is familiar to practicing statisticians.
Questions arise when statisticians are first confronted with such a model, and this book's aim is to provide thorough answers.
The following are a few questions that are considered in this book and are explored: how robust is the model to...
preview:
http://www.amazon.com
date: 6/11/2007
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book
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Applications of Neural Networks in Electromagnetics
This book aims to bridge the gap by explaining numerous neural network architectures and how they have been used to solve various electromagnetic problems.
Through comparison with classical solutions, the merits of the method are demonstrated.
preview:
http://www.amazon.com
date: 1/1/2001
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book
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Applied Neural Networks for Signal Processing
Applied Neural Networks for Signal Processing is the first book to provide a comprehensive introduction to this broad field, covering basic principles and practical implementation procedures.
A key feature of the book is that many carefully designed simulation examples are included to help guide the reader in the development of systems for new applications.
1999
preview:
http://www.amazon.com
date: 1/1/1999
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book
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Applying Neural Networks : A Practical Guide
This book is designed to enable the reader to design and run a neural network-based project.
It presents everything the reader will need to know to ensure the success of such a project.
The book contains a free disk with C and C++ programs, which implement many of the techniques discussed in the book.
preview:
http://www.amazon.com
date: 4/1/1999
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book
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Bayesian Networks and Decision Graphs
Bayesian networks and decision graphs are formal graphical languages for representation and communication of decision scenarios requiring reasoning under uncertainty.
Their strengths are two-sided.
It is easy for humans to construct and to understand them, and when communicated to a computer, they can easily be compiled.
preview:
http://www.amazon.com
date: 7/2/2001
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