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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

Pre-Trained Sets and Transfer Learning

Humans learn by experience. We apply the knowledge we gain in one situation to similar situations we face in the future. Suppose you want to learn how to drive an SUV. You have never driven an SUV; all you know is how to drive a small hatchback car.

The dimensions of the SUV are considerably larger than the hatchback, so navigating the SUV in traffic will surely be a challenge. Still, some basic systems (such as the clutch, accelerator, and brakes) remain similar to that of the hatchback. So, knowing how to drive a hatchback will surely be of great help to you when you are learning to drive the SUV. All the knowledge that you acquired while driving a hatchback can be used when you learn to drive a big SUV.

This is precisely what transfer learning is. By definition, transfer learning is a concept in machine learning in which we store and use the knowledge gained in one activity while learning another similar activity. The hatchback-SUV model...

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