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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

Understanding the data

Let's take a look at the data we will be using to build our model. To keep things simple, we will be using the Flickr8K dataset. This dataset includes images obtained from Flickr, a popular image sharing website. To download the dataset, you can request it by filling in a form at https://forms.illinois.edu/sec/1713398 from the Department of Computer Science, University of Illinois, and you should get the download link in your email.

To check out the details pertaining to each image, you can refer to their website, http://nlp.cs.illinois.edu/HockenmaierGroup/8k-pictures.html, which talks about each image, its source, and five text-based captions for each image. In general, any sample image would have several captions similar to the following:

You can clearly see the image and its corresponding captions. It is quite evident that all the captions try...

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