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Machine Learning Techniques for Text

You're reading from   Machine Learning Techniques for Text Apply modern techniques with Python for text processing, dimensionality reduction, classification, and evaluation

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Product type Paperback
Published in Oct 2022
Publisher Packt
ISBN-13 9781803242385
Length 448 pages
Edition 1st Edition
Languages
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Author (1):
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Nikos Tsourakis Nikos Tsourakis
Author Profile Icon Nikos Tsourakis
Nikos Tsourakis
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Table of Contents (13) Chapters Close

Preface 1. Chapter 1: Introducing Machine Learning for Text 2. Chapter 2: Detecting Spam Emails FREE CHAPTER 3. Chapter 3: Classifying Topics of Newsgroup Posts 4. Chapter 4: Extracting Sentiments from Product Reviews 5. Chapter 5: Recommending Music Titles 6. Chapter 6: Teaching Machines to Translate 7. Chapter 7: Summarizing Wikipedia Articles 8. Chapter 8: Detecting Hateful and Offensive Language 9. Chapter 9: Generating Text in Chatbots 10. Chapter 10: Clustering Speech-to-Text Transcriptions 11. Index 12. Other Books You May Enjoy

Introducing deep neural networks

Nature has always inspired mathematicians and engineers to devise appropriate algorithms, designs, and artifacts for any given problem. So incorporating solutions that have proven themselves over millennia seems like a good idea. We can refer to numerous examples such as bats’ echolocation that inspired human-made sonars, the high-speed trains that have a shape that resembles the elongated beak of kingfisher birds to prevent sonic booms, the flight of drones as a flock of birds to avoid collisions, and many more.

Throughout this chapter, we discussed many times how algorithms learn from data. What is more natural than to think that emulating the human brain and its functionalities can enhance artificial cognition? Exploiting the mode of operation of this astonishingly complex organ of the human nervous system might permit the creation of sophisticated algorithms in any domain. This section provides a gentle introduction to the topic and presents...

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