This part of the book introduces the basic mathematical concepts needed to
understand deep learning. We begin with general ideas from applied math that
allow us to deﬁne functions of many variables, ﬁnd the highest and lowest points
on these functions and quantify degrees of belief.
Next, we describe the fundamental goals of machine learning. We describe how
to accomplish these goals by specifying a model that represents certain beliefs,
designing a cost function that measures how well those beliefs correspond with
reality and using a training algorithm to minimize that cost function.
This elementary framework is the basis for a broad variety of machine learning
algorithms, including approaches to machine learning that are not deep. In the
subsequent parts of the book, we develop deep learning algorithms within this