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The Deep Learning with Keras Workshop

You're reading from   The Deep Learning with Keras Workshop Learn how to define and train neural network models with just a few lines of code

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Product type Paperback
Published in Jul 2020
Publisher Packt
ISBN-13 9781800562967
Length 496 pages
Edition 1st Edition
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Authors (3):
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Matthew Moocarme Matthew Moocarme
Author Profile Icon Matthew Moocarme
Matthew Moocarme
Mahla Abdolahnejad Mahla Abdolahnejad
Author Profile Icon Mahla Abdolahnejad
Mahla Abdolahnejad
Ritesh Bhagwat Ritesh Bhagwat
Author Profile Icon Ritesh Bhagwat
Ritesh Bhagwat
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Table of Contents (11) Chapters Close

Preface
1. Introduction to Machine Learning with Keras 2. Machine Learning versus Deep Learning FREE CHAPTER 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

Introduction

In the previous chapter, we learned how to create a Convolutional Neural Network (CNN) from scratch with Keras. We experimented with different architectures by adding more convolutional and Dense layers and changing the activation function. We compared the performance of each model by classifying images of cars and flowers into their respective classes and comparing their accuracies.

In real-world projects, however, you almost never code a convolutional neural network from scratch. You always tweak and train them as per the requirements. This chapter will introduce you to the important concepts of transfer learning and pre-trained networks (also known as pre-trained models), both of which are used in the industry.

We will use images and, rather than building a CNN from scratch, we will match these images on pre-trained models to try and classify them. We will also tweak our models to make them more flexible. The models we will use in this chapter are called VGG16...

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