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Hands-On Transfer Learning with Python

You're reading from   Hands-On Transfer Learning with Python Implement advanced deep learning and neural network models using TensorFlow and Keras

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Product type Paperback
Published in Aug 2018
Publisher Packt
ISBN-13 9781788831307
Length 438 pages
Edition 1st Edition
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Authors (4):
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Nitin Panwar Nitin Panwar
Author Profile Icon Nitin Panwar
Nitin Panwar
Raghav Bali Raghav Bali
Author Profile Icon Raghav Bali
Raghav Bali
Tamoghna Ghosh Tamoghna Ghosh
Author Profile Icon Tamoghna Ghosh
Tamoghna Ghosh
Dipanjan Sarkar Dipanjan Sarkar
Author Profile Icon Dipanjan Sarkar
Dipanjan Sarkar
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Table of Contents (14) Chapters Close

Preface 1. Machine Learning Fundamentals FREE CHAPTER 2. Deep Learning Essentials 3. Understanding Deep Learning Architectures 4. Transfer Learning Fundamentals 5. Unleashing the Power of Transfer Learning 6. Image Recognition and Classification 7. Text Document Categorization 8. Audio Event Identification and Classification 9. DeepDream 10. Style Transfer 11. Automated Image Caption Generator 12. Image Colorization 13. Other Books You May Enjoy

Leveraging transfer learning with pretrained CNN models

So far, we have built our CNN deep learning models from scratch by specifying our own architecture. In this section, we will leverage a pretrained model that is basically an expert in the computer vision domain and renowned for image classification and categorization. We recommend you to check out Chapter 4, Transfer Learning Fundamentals, for a brief refresher around pretrained models and their applications in this domain.

Pretrained models are used in the following two popular ways when building new models or reusing them:

  • Using a pretrained model as a feature extractor
  • Fine-tuning the pretrained model

We will cover both of them in detail in this section. The pretrained model we will be using in this chapter is the popular VGG-16 model, created by the Visual Geometry Group at the University of Oxford, which specializes...

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