Multilayer Perceptrons › Multilayer Perceptrons

In the previous chapters, we explored linear classifiers and activation functions as separate building blocks. Linear classifiers can find decision boundaries, but only straight ones. Activation functions introduce non-linearity, but a single neuron with an activation still has limited representational power. The breakthrough comes when we stack these components together into layers, creating what we call a multilayer perceptron (MLP).

MLPs are the workhorses of deep learning. They can approximate virtually any function given enough neurons and proper training. From sentiment analysis to language modeling, understanding MLPs is essential because they form the building blocks of more complex architectures like transformers. This chapter shows you how to construct, understand, and implement MLPs from the ground up.