Book Image

Machine Learning Quick Reference

By : Rahul Kumar
Book Image

Machine Learning Quick Reference

By: Rahul Kumar

Overview of this book

Machine learning makes it possible to learn about the unknowns and gain hidden insights into your datasets by mastering many tools and techniques. This book guides you to do just that in a very compact manner. After giving a quick overview of what machine learning is all about, Machine Learning Quick Reference jumps right into its core algorithms and demonstrates how they can be applied to real-world scenarios. From model evaluation to optimizing their performance, this book will introduce you to the best practices in machine learning. Furthermore, you will also look at the more advanced aspects such as training neural networks and work with different kinds of data, such as text, time-series, and sequential data. Advanced methods and techniques such as causal inference, deep Gaussian processes, and more are also covered. By the end of this book, you will be able to train fast, accurate machine learning models at your fingertips, which you can easily use as a point of reference.
Table of Contents (18 chapters)
Title Page
Copyright and Credits
About Packt
Contributors
Preface
Index

Generative adversarial networks


Generative adversarial networks (GANs) are another form of deep neural network architecture, and is a combination of two networks that compete and cooperate with each other. It was introduced by Ian Goodfellow and Yoshua Bengio in 2014.

GANs can learn to mimic any distribution of data, which ideally means that GANs can be taught to create an object that's similar to an existing one in any domain, such as images, music, speech, and prose. It can create photos of any object that has never existed before. They are robot artists in a sense, and their output is impressive.

It falls under unsupervised learning wherein both of the networks learn their task upon training. One of the networks is called the generator and the other is called the discriminator.

To make this more understandable, we can think of a GAN as a case of a counterfeiter (generator) and a cop (discriminator). At the outset, the counterfeiter shows the cop fake money. The cop works like a detective...