Introduction

In those series of articles in general, we are going to introduction you to the field of generative modeling. And with that, we shall first look at what it means to say that a model is generative and learn how it differs from the more widely studied discriminative modeling. Then I will introduce the framework and core mathematical ideas that will allow us to structure our general approach to problems that require a generative solution.

What Is Generative Modeling?

We can define a generative model as follows:

A generative model describes how a dataset is generated, in terms of a probabilistic model. By sampling from this model, we are able to generate new data.

Suppose we have a dataset containing images of horses. We may wish to build a model that can generate a new image of a horse that has never existed but still looks real because the model has learned the general rules that govern the appearance of a horse. This is the kind of problem that can be solved using generative modeling. A summary of a typical generative modeling process is shown in Figure 1-1.

First, we require a dataset consisting of many examples of the entity we are trying to generate. This is known as the training data, and one such data point is called an observation.

Generative Versus Discriminative Modeling

Advances in Machine Learning

The Rise of Generative Modeling

The Generative Modeling Framework

Probabilistic Generative Models

First Few Lines

Your First Probabilistic Generative Model

Naive Bayes

Back To Your First Few Lines

The Challenges of Generative Modeling

Representation Learning

Summary

Now we came to the end of this article, but we are just starting the series. You will find this article not interesting, but it is important to dig into the various flavors of machine learning. And by that, you learned that a machine learning algorithm is either supervised or unsupervised and either parametric or nonparametric.

Furthermore, we explored exactly what makes these four different groups of algorithms distinct. Until now, we’ve stayed at a conceptual level as you got your bearings in the field as a whole and your place in it.

In the next aricle Building Simple Discriminative Model, you’ll build your first neural network, and all subsequent articles will be project based. So, stay tuned.