
Mastering Java Machine Learning
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Historically, artificial neural networks have been largely identified by multi-layer feed-forward perceptrons, and so we will begin with a discussion of the primitive elements of the structure of such networks, how to train them, the problem of overfitting, and techniques to address it.
A single neuron or perceptron is the same as the unit described in the Linear Regression topic in Chapter 2, Practical Approach to Real-World Supervised Learning. In this chapter, the data instance vector will be represented by x and has d dimensions, and each dimension can be represented as . The weights associated with each dimension are represented as a weight vector w that has d dimensions, and each dimension can be represented as
. Each neuron has an extra input b, known as the bias, associated with it.
Neuron pre-activation performs the linear transformation of inputs given by:
The activation function...