.. _chap_perceptrons:

Multilayer Perceptrons
======================


In this chapter, we will introduce your first truly *deep* network. The
simplest deep networks are called multilayer perceptrons, and they
consist of multiple layers of neurons each fully connected to those in
the layer below (from which they receive input) and those above (which
they, in turn, influence). When we train high-capacity models we run the
risk of overfitting. Thus, we will need to provide your first rigorous
introduction to the notions of overfitting, underfitting, and model
selection. To help you combat these problems, we will introduce
regularization techniques such as weight decay and dropout. We will also
discuss issues relating to numerical stability and parameter
initialization that are key to successfully training deep networks.
Throughout, we aim to give you a firm grasp not just of the concepts but
also of the practice of using deep networks. At the end of this chapter,
we apply what we have introduced so far to a real case: house price
prediction. We punt matters relating to the computational performance,
scalability, and efficiency of our models to subsequent chapters.

.. toctree::
    :maxdepth: 2

    mlp
    mlp-scratch
    mlp-concise
    underfit-overfit
    weight-decay
    dropout
    backprop
    numerical-stability-and-init
    environment
    kaggle-house-price