Introduction to Artificial Neural Networks
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Overview
Artificial Neural Networks (ANNs) provide a general, practical method for learning real-valued, discrete-valued, and vector-valued functions from examples.
The Backpropagation algorithm — the cornerstone of ANN learning — uses gradient descent to tune network parameters to best fit a training set of input-output pairs.
Why ANNs Matter
ANN learning has been proven effective in:
- Recognising handwritten characters (LeCun et al., 1989)
- Recognising spoken words (Lang et al., 1990)
- Recognising faces (Cottrell, 1990)
- Steering autonomous vehicles (Pomerleau, 1993)
4.1 Biological Motivation
The study of ANNs was inspired by the observation that biological learning systems are built of densely interconnected neurons.