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TensorFlow Deep Learning Projects

You're reading from   TensorFlow Deep Learning Projects 10 real-world projects on computer vision, machine translation, chatbots, and reinforcement learning

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Product type Paperback
Published in Mar 2018
Publisher Packt
ISBN-13 9781788398060
Length 320 pages
Edition 1st Edition
Languages
Concepts
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Authors (5):
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Alberto Boschetti Alberto Boschetti
Author Profile Icon Alberto Boschetti
Alberto Boschetti
Rajalingappaa Shanmugamani Rajalingappaa Shanmugamani
Author Profile Icon Rajalingappaa Shanmugamani
Rajalingappaa Shanmugamani
Luca Massaron Luca Massaron
Author Profile Icon Luca Massaron
Luca Massaron
Abhishek Thakur Abhishek Thakur
Author Profile Icon Abhishek Thakur
Abhishek Thakur
Alexey Grigorev Alexey Grigorev
Author Profile Icon Alexey Grigorev
Alexey Grigorev
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Toc

Table of Contents (12) Chapters Close

Preface 1. Recognizing traffic signs using Convnets 2. Annotating Images with Object Detection API FREE CHAPTER 3. Caption Generation for Images 4. Building GANs for Conditional Image Creation 5. Stock Price Prediction with LSTM 6. Create and Train Machine Translation Systems 7. Train and Set up a Chatbot, Able to Discuss Like a Human 8. Detecting Duplicate Quora Questions 9. Building a TensorFlow Recommender System 10. Video Games by Reinforcement Learning 11. Other Books You May Enjoy

Creating fuzzy features

The next set of features are based on fuzzy string matching. Fuzzy string matching is also known as approximate string matching and is the process of finding strings that approximately match a given pattern. The closeness of a match is defined by the number of primitive operations necessary to convert the string into an exact match. These primitive operations include insertion (to insert a character at a given position), deletion (to delete a particular character), and substitution (to replace a character with a new one).

Fuzzy string matching is typically used for spell checking, plagiarism detection, DNA sequence matching, spam filtering, and so on and it is part of the larger family of edit distances, distances based on the idea that a string can be transformed into another one. It is frequently used in natural language processing and other applications...

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