Chapter 4
The Multi-Layer Perceptron
In the last chapter we saw that while linear models are easy to understand and use, they come with the inherent cost that is implied by the word ‘linear’; that is, they can only identify straight lines, planes, or hyperplanes. And this is not usually enough, because the majority of interesting problems are not linearly separable. In Section 3.4 we saw that problems can be made linearly separable if we can work out how to transform the features suitably. We will come back to this idea in Chapter 8, but in this chapter we will instead consider making more complicated networks.
We have pretty much decided that the learning in the neural network happens in the weights. So, to perform more computation it seems ...
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