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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

Problem statement

Photographs help us preserve events in time. They don't just help us relive memories but also provide insights into important events from the past. Until color photography became mainstream, our photographic history was captured in black and white. The task of image colorization is to transform a given grayscale image into a plausible color version.

The task of image colorization can be undertaken from different perspectives. The manual process is very time-consuming and requires amazing skills (see the r/Colorization subreddit at https://www.reddit.com/r/Colorization/). Researchers in the field of computer vision and deep learning have been working on different ways of automating the process. Through this chapter, we will work toward understanding how a deep neural network can be leveraged for such a task. We will also try to utilize the power of transfer...

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