1 An overview of machine learning and deep learning

Deep learning has transformed computer vision, natural language and speech processing in particular and artificial intelligence in general. From a bag of semi-discordant tricks, none of which worked satisfactorily on a real life problem, artificial intelligence has become a formidable tool to solve real problems faced by industry, at scale. This is nothing short of a revolution going on under our very noses. If one wants to lead the curve of this revolution, it is imperative to understand the underlying principles and abstractions, rather than simply memorizing the ”how to” steps of some hands on guide. This is where the mathematics comes in.

In this first chapter we will give an overview of deep learning. This will require us to use some concepts that have been explained in subsequent chapters. The reader should not worry if there are some open questions at the end of this chapter. This chapter is aimed at orienting one’s mind towards this difficult subject. As individual concepts get clearer in subsequent chapters, the reader should consider coming back and giving this chapter a re-read.

1.1 A first look at machine/deep learning - a paradigm shift in computation

Making decisions and/or predictions is a central requirement of life. This essentially involves taking in a set of sensory or knowledge inputs and generating decisions or estimates by processing them.

1.2 A Function Approximation View of Machine Learning: Models and their Training

1.3 A simple machine learning model - the cat brain

1.4 Geometrical View of Machine Learning

1.5 Regression vs Classification in Machine Learning

1.6 Linear vs Nonlinear Models

1.7 Higher Expressive Power through multiple non-linear layers: Deep Neural Networks

Chapter Summary