Machine Learning: Origins, Developments and Implications
First published:
ISBN: 978-1-78272-445-2
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Introduction to neural networks
Introduction to neural networks
Preface
Introduction: human-machine entanglement
Machine learning: origins
Useful tools
Decision trees
Introduction to neural networks
Back-propagation
Regularisation
Optimisation
Building neural networks
Early applications of machine learning
Interpreting neural network decisions
Predicting corporate bond returns
Deep learning networks
Applications of deep learning networks
Machine intelligence
Consciousness
The future and its challenges
Artificial intelligence and the military
Final thoughts
Appendix
Epilogue
Acknowledgements
A neural network is a method in artificial intelligence that teaches computers to process data in a way that is inspired by the human brain. For example, Figure 5.1 shows a diagram of the retina of the human eye and a corresponding computer system. The mathematical model for colour vision is designed to mimic the neurons in the brain and to process light energy analogously. Before introducing networks, this chapter explores the development of the artificial neuron and its functions. In addition, neurons’ activation functions are described along with their inclusion in multilayer perceptrons. Finally, the method of training neural networks using error back-propagation is introduced.
5.1 INTRODUCTION AND EARLY HISTORY
Recall from Chapter 3 the artificial neuron introduced by McCulloch and Pitts. That neuron had binary (0 or 1) inputs that were summed and measured against a threshold for a binary output signal. The McCulloch–Pitts neuron, shown in Figure 5.2, can be represented formally as:
In Equation 5.1 the function g(x) is a summation of the inputs, xi, and θ is called the
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