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You're reading from  Applied Deep Learning with Keras

Product typeBook
Published inApr 2019
Reading LevelIntermediate
Publisher
ISBN-139781838555078
Edition1st Edition
Languages
Python
Tools
Keras
Concepts
Deep Learning
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Authors (3):
Ritesh Bhagwat
Ritesh Bhagwat
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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

Mahla Abdolahnejad
Mahla Abdolahnejad
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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

Matthew Moocarme
Matthew Moocarme
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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

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Regularization

Since deep neural networks are highly flexible models, overfitting is an issue that can often arise when training them. Therefore, one very important part of becoming a deep learning expert is knowing how to detect overfitting, and subsequently how to address the overfitting problem in your model. Regularization techniques are an important group of methods specifically aimed at reducing overfitting in machine learning models. Understanding regularization techniques thoroughly and being able to apply them to your deep neural networks is an essential step toward building deep neural networks in order to solve real-life problems. In this section, you will learn about the underlying concepts of regularization, providing you with the foundation required for the following sections, where you will learn how to implement various types of regularization methods using Keras.

The Need for Regularization

The main goal of machine learning is to build models that perform well on not only...

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Applied Deep Learning with Keras
Published in: Apr 2019Publisher: ISBN-13: 9781838555078
© 2019 Packt Publishing Limited All Rights Reserved

Authors (3)

author image
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

author image
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

author image
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

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