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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

Formulating our objective

The main objective of our real-world case study is image captioning or scene recognition. This is a supervised learning problem to an extent, but not a traditional classification problem. Here, we will be working on an image dataset, known as Flickr8K, with samples of images or scenes and corresponding natural language captions describing them. The idea is to build a system that can learn from these images and start captioning images automatically.

As I mentioned earlier, a traditional image classification system typically classifies or categorizes images into predefined classes. We have already built such a system in previous chapters. However, the output from an image captioning system is generally a sequence of words forming a textual description in natural language; this makes it more difficult than a traditional supervised classification system.

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