Introduction

Introduction#

Why this Book?#

There are many resources for machine learning on the internet. However, most of them are either

Too long. It takes half an hour just to read through.
Too math heavy. It takes you forever to understand.
Too confusing. The concepts are not straight-forward.

This book aims to solve all of that. It tries to be as concise but easy to grasp as possible.

What is this book?#

This book is for learners who want to quickly grasp an idea, without diving deep into a topic (it takes way too long!). The book is a handbook for people who want to preserve their time.

How to use this book?#

Don’t use this book as a reference, use it as a handbook instead. We’ll cover all the basics.

Before we start#

In this book, we’re mainly focused on deep learning, which is a branch of machine learning that leverages of a lot of computing power and yield incredible result. Sometimes it’s called artificial intelligence.

Note

AI (artificial intelligence), ML (machine learning), DL (deep learning). This book use these terms interchangeably. They are not equal terms outside of this book, however, in the subset we cover in this book they can be seen as equal.

Why learn machine learning?#

There are many reason to learn ML. The most obvious reason is that ML is quite powerful and useful, and many systems use it ranging from Google’s search to snapchat filters. Secondly, it’s quite the rage nowadays, so it certainly would help you land a prestigious job. Thirdly, in the case where robots take over the world (like Elon Musk feared), having learned machines’ ways of dealing things may be your only path to survival! Jokes aside, learning ML does help to calm your nerves since most of the theories on the Internet about robots are just over the top. And learning ML helps keeping you from being tricked by those deceptive information.