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Applied Deep Learning with Keras

You're reading from Applied Deep Learning with Keras

Product type Book

Published in Apr 2019

Publisher

ISBN-13 9781838555078

Pages 412 pages

Edition 1st Edition

Languages

Concepts

Authors (3):

Ritesh Bhagwat

Mahla Abdolahnejad

Matthew Moocarme

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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

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

Chapter 2. Machine Learning versus Deep Learning

Note

Learning Objectives

By the end of this chapter, you will be able to:

Explain deep learning and how it is different from machine learning
Apply linear transformations with Python
Build a logistic regression model with Keras

Note

In this chapter, we will learn how deep learning is different from machine learning. We will apply linear transformations and lastly build regression models.

The rest of the chapter is locked

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Authors (3)

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 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 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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