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Systems Engineering Neural Networks (Hardcover)
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Systems Engineering Neural Networks (Hardcover)
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A complete and authoritative discussion of systems engineering and
neural networks In Systems Engineering Neural Networks, a team of
distinguished researchers deliver a thorough exploration of the
fundamental concepts underpinning the creation and improvement of
neural networks with a systems engineering mindset. In the book,
you'll find a general theoretical discussion of both systems
engineering and neural networks accompanied by coverage of relevant
and specific topics, from deep learning fundamentals to sport
business applications. Readers will discover in-depth examples
derived from many years of engineering experience, a comprehensive
glossary with links to further reading, and supplementary online
content. The authors have also included a variety of applications
programmed in both Python 3 and Microsoft Excel. The book provides:
A thorough introduction to neural networks, introduced as key
element of complex systems Practical discussions of systems
engineering and forecasting, complexity theory and optimization and
how these techniques can be used to support applications outside of
the traditional AI domains Comprehensive explorations of input and
output, hidden layers, and bias in neural networks, as well as
activation functions, cost functions, and back-propagation
Guidelines for software development incorporating neural networks
with a systems engineering methodology Perfect for students and
professionals eager to incorporate machine learning techniques into
their products and processes, Systems Engineering Neural Networks
will also earn a place in the libraries of managers and researchers
working in areas involving neural networks.
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