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The Deep Learning with Keras Workshop

You're reading from The Deep Learning with Keras Workshop

Product type Book

Published in Jul 2020

Publisher Packt

ISBN-13 9781800562967

Pages 496 pages

Edition 1st Edition

Languages

Concepts

Authors (3):

Matthew Moocarme

Mahla Abdolahnejad

Ritesh Bhagwat

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Table of Contents (11) Chapters

Preface

1. Introduction to Machine Learning with Keras

2. Machine Learning versus Deep Learning

3. Deep Learning with Keras

4. Evaluating 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

Appendix

4. Evaluating Your Model with Cross-Validation Using Keras Wrappers

Overview

This chapter introduces you to building Keras wrappers with scikit-learn. You will learn to apply cross-validation to evaluate deep learning models, and create user-defined functions to implement deep learning models along with cross-validation. By the end of this chapter, you will be able to build robust models that perform as well on new, unseen data as they do on the trained data.

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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.

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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.

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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.

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