You're reading from The Deep Learning with Keras Workshop

Product typeBook

Published inJul 2020

Reading LevelIntermediate

PublisherPackt

ISBN-139781800562967

Edition1st Edition

Languages

Python

Tools

Keras

Concepts

Deep Learning

Authors (3):

Matthew Moocarme

Mahla Abdolahnejad

Ritesh Bhagwat

View More author details

Other Regularization Methods

In this section, you will briefly learn about some other regularization techniques that are commonly used and have been shown to be effective in deep learning. It is important to keep in mind that regularization is a wide-ranging and active research field in machine learning. As a result, covering all the available regularization methods in one chapter is not possible (and most likely not necessary, especially in a book on applied deep learning). Therefore, in this section, we will briefly cover three more regularization methods, called early stopping, data augmentation, and adding noise. You will learn about their underlying ideas and gain a few tips and recommendations on how to use them.

Early Stopping

Earlier in this chapter, we discussed that the main assumption in machine learning is that there is a true function or process that produces training examples. However, this process is unknown and there is no explicit way to find it. Not only is...

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The Deep Learning with Keras Workshop

Published in: Jul 2020Publisher: PacktISBN-13: 9781800562967

Authors (3)

Matthew Moocarme

Matthew Moocarme is an accomplished data scientist with more than eight years of experience in creating and utilizing machine learning models. He comes from a background in the physical sciences, in which he holds a Ph.D. in physics from the Graduate Center of CUNY. Currently, he leads a team of data scientists and engineers in the media and advertising space to build and integrate machine learning models for a variety of applications. In his spare time, Matthew enjoys sharing his knowledge with the data science community through published works, conference presentations, and workshops.
Read more about Matthew Moocarme

Mahla Abdolahnejad

Mahla Abdolahnejad is a Ph.D. candidate in systems and computer engineering with Carleton University, Canada. She also holds a bachelor's degree and a master's degree in biomedical engineering, which first exposed her to the field of artificial intelligence and artificial neural networks, in particular. Her Ph.D. research is focused on deep unsupervised learning for computer vision applications. She is particularly interested in exploring the differences between a human's way of learning from the visual world and a machine's way of learning from the visual world, and how to push machine learning algorithms toward learning and thinking like humans.
Read more about Mahla Abdolahnejad

Ritesh Bhagwat

Ritesh Bhagwat has a master's degree in applied mathematics with a specialization in computer science. He has over 14 years of experience in data-driven technologies and has led and been a part of complex projects ranging from data warehousing and business intelligence to machine learning and artificial intelligence. He has worked with top-tier global consulting firms as well as large multinational financial institutions. Currently, he works as a data scientist. Besides work, he enjoys playing and watching cricket and loves to travel. He is also deeply interested in Bayesian statistics.
Read more about Ritesh Bhagwat

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Other Regularization Methods

Early Stopping

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