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Applied Deep Learning with Keras

You're reading from Applied Deep Learning with Keras Solve complex real-life problems with the simplicity of Keras

Product type Paperback

Published in Apr 2019

Publisher

ISBN-13 9781838555078

Length 412 pages

Edition 1st Edition

Languages

Python

Tools

Keras

Concepts

Deep Learning

Authors (3):

Matthew Moocarme

Mahla Abdolahnejad

Ritesh Bhagwat

View More author details

Table of Contents (12) Chapters

Applied Deep Learning with Keras

Preface

Preface

1. Introduction to Machine Learning with Keras

2. Machine Learning versus Deep Learning FREE CHAPTER

3. Deep Learning with Keras

4. Evaluate Your Model with Cross-Validation using Keras Wrappers

5. Improving Model Accuracy

6. Model Evaluation

7. Computer Vision with Convolutional Neural Networks

8. Transfer Learning and Pre-Trained Models

9. Sequential Modeling with Recurrent Neural Networks

L1 and L2 Regularization

The most common type of regularization for deep learning models is the one that keeps the weights of the network small. This type of regularization is called weight regularization and has two different variations: L2 regularization and L1 regularization. In this section, you will learn about these regularization methods in detail, along with how to implement them in Keras. Additionally, you will practice applying them to real-life problems and observe how they can improve the performance of a model.

L1 and L2 Regularization Formulation

In weight regularization, a penalizing term is added to the loss function. This term is either L2 norm (the sum of the squared values) of the weights, or L1 norm (the sum of the absolute values) of the weights. If L1 norm is used, then it will be called L1 regularization. If L2 norm is used, then it will be called L2 regularization. In each case, the sum is multiplied by a hyperparameter called a regularization parameter (lambda).

Therefore...

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Authors (3)

Ritesh Bhagwat

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.

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Mahla Abdolahnejad

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.

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Matthew Moocarme

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.

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