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Artificial Vision and Language Processing for Robotics

You're reading from   Artificial Vision and Language Processing for Robotics Create end-to-end systems that can power robots with artificial vision and deep learning techniques

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Product type Paperback
Published in Apr 2019
Publisher Packt
ISBN-13 9781838552268
Length 356 pages
Edition 1st Edition
Languages
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Authors (3):
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Gonzalo Molina Gallego Gonzalo Molina Gallego
Author Profile Icon Gonzalo Molina Gallego
Gonzalo Molina Gallego
Unai Garay Maestre Unai Garay Maestre
Author Profile Icon Unai Garay Maestre
Unai Garay Maestre
Álvaro Morena Alberola Álvaro Morena Alberola
Author Profile Icon Álvaro Morena Alberola
Álvaro Morena Alberola
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Table of Contents (12) Chapters Close

Artificial Vision and Language Processing for Robotics
Preface
1. Fundamentals of Robotics 2. Introduction to Computer Vision FREE CHAPTER 3. Fundamentals of Natural Language Processing 4. Neural Networks with NLP 5. Convolutional Neural Networks for Computer Vision 6. Robot Operating System (ROS) 7. Build a Text-Based Dialogue System (Chatbot) 8. Object Recognition to Guide a Robot Using CNNs 9. Computer Vision for Robotics Appendix

Neural Language Models


Chapter 3, Fundamentals of Natural Language Processing introduced us to statistical language models (LMs), which are the probability distribution for a sequence of words. We know LMs can be used to predict the next word in a sentence, or to compute the probability distribution of the next word.

Figure 4.20: LM formula to compute the probability distribution of an upcoming word

The sequence of words is x1 , x2 … and the next word is xt+1. wj is a word in the vocabulary. V is the vocabulary and j is a position of a word in that vocabulary. wj is the word located in position j within V.

You use LMs every day. The keyboards on cell phones use this technology to predict the next word of a sentence, and search engines such as Google use it to predict what you want to search in their search for engine.

We talked about the n-gram model and bigrams counting the words in a corpus, but that solution has some limitations, such as long dependencies. Deep NLP and neural LMs will help...

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