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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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Toc

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

Unleashing the Power of Transfer Learning

In the previous chapter, we covered the major concepts surrounding transfer learning. The key idea was that leveraging state-of-the-art, pretrained deep learning models in a wide variety of tasks yields superior results compared to building your own deep learning models and architectures from scratch. In this chapter, we will gain a more hands-on perspective of actually building deep learning models using transfer learning and applying them to a real-world problem. We will build various deep learning models with and without transfer learning. We will analyze their architecture and also compare and contrast their performance. We will be covering the following major aspects in this chapter:

  • The need for transfer learning
  • Building Convolutional Neural Network (CNN) models from scratch:
    • Building a basic CNN model
    • Improving our CNN model...
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