.. _chap_preliminaries:
Preliminaries
=============
To get started with deep learning, we will need to develop a few basic
skills. All machine learning is concerned with extracting information
from data. So we will begin by learning the practical skills for
storing, manipulating, and preprocessing data.
Moreover, machine learning typically requires working with large
datasets, which we can think of as tables, where the rows correspond to
examples and the columns correspond to attributes. Linear algebra gives
us a powerful set of techniques for working with tabular data. We will
not go too far into the weeds but rather focus on the basic of matrix
operations and their implementation.
Additionally, deep learning is all about optimization. We have a model
with some parameters and we want to find those that fit our data *the
best*. Determining which way to move each parameter at each step of an
algorithm requires a little bit of calculus, which will be briefly
introduced. Fortunately, the ``autograd`` package automatically computes
differentiation for us, and we will cover it next.
Next, machine learning is concerned with making predictions: what is the
likely value of some unknown attribute, given the information that we
observe? To reason rigorously under uncertainty we will need to invoke
the language of probability.
In the end, the official documentation provides plenty of descriptions
and examples that are beyond this book. To conclude the chapter, we will
show you how to look up documentation for the needed information.
This book has kept the mathematical content to the minimum necessary to
get a proper understanding of deep learning. However, it does not mean
that this book is mathematics free. Thus, this chapter provides a rapid
introduction to basic and frequently-used mathematics to allow anyone to
understand at least *most* of the mathematical content of the book. If
you wish to understand *all* of the mathematical content, further
reviewing :numref:`chap_appendix_math` should be sufficient.
.. toctree::
:maxdepth: 2
ndarray
pandas
linear-algebra
calculus
autograd
probability
lookup-api