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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

Text categorization

Given a set of text documents and a set of predefined categories, the objective of text categorization is to assign each document to a category. The output can be a soft assignment or a hard assignment, depending on the problem. Soft assignment means that the category assignment is defined as a probability distribution over all categories.

There are a wide range of applications of text categorization in industry. The following are a few examples:

  • Spam filtering: Given an email, classify it as spam or legitimate email.
  • Sentiment classification: Given a review text (movie review, product review), identify the user polarity—whether its a positive or negative or neural review.
  • Problem ticket assignment: Typically, in any industry, whenever a user faces an issue regarding any IT application or a software/hardware product, the fist step is to create a problem...
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