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Systems Engineering Neural Networks 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 text is structured in three parts with the first part focused on systems engineering while the second will present a number of exercises through the use of two programming languages, Python and Visual Basic. This will allow readers of different academic backgrounds to interact with neural networks. Part 3 covers the theory of Neural Networks and its key components.
Chapter 3 is particularly interesting, as we will delve further into the link between the theory of Systems Engineering and Analytic Foresight. An innovative approach will be used to show how to apply neural networks to the sports business. A quirky example is the one related to LEGO sorting machines - as unusual as it may sound, sorting machines are at the basis of industrial engineering, from automotive applications to food technology.
The journey to understanding neural networks is a fascinating one though it can be perceived as arduous to the inexperienced reader. This topic requires an academic knowledge of basic calculus. Let us reach an agreement: in this book we will examine the basics of the topic, assuming that the reader will be proactive in utilizing the resources available on the Internet or in literature to close gaps in understanding.
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
Setting the Scene
A Brief Introduction
Defining a Neural Network
Engineering Neural Networks
Neural Networks in Action
Systems Thinking for Software Development
Practice Makes Perfect
Down to the Basics
Input/Output, Hidden Layer and Bias
Activation Function
Cost Function, Back-Propagation and Other Iterative Methods
Conclusions and Future Developments