.. _chapter_linear:
Linear Neural Networks
======================
Before we get into the details of deep neural networks, we need to cover
the basics of neural network training. In this chapter, we will cover
the entire training process, including defining simple neural network
architecures, handling data, specifying a loss function, and training
the model. In order to make things easier to grasp, we begin with the
simplest concepts. Fortunately, classic statistical learning techniques
such as linear and logistic regression can be cast as *shallow* neural
networks. Starting from these classic algorthms, we’ll introduce you to
the basics, providing the basis for more complex techniques such as
softmax regression (introduced at the end of this chapter) and
multilayer perceptrons (introduced in the next chapter).
.. toctree::
:maxdepth: 2
linear-regression
linear-regression-scratch
linear-regression-gluon
softmax-regression
fashion-mnist
softmax-regression-scratch
softmax-regression-gluon