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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 FREE CHAPTER 2. Introduction to Computer Vision 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

NLP in Python


Python has become very popular in recent years, by combining the power of general-purpose programming languages with the use of specific domain languages, such as MATLAB and R (designed for mathematics and statistics). It has different libraries for data loading, visualization, NLP, image processing, statistics, and more. Python has the most powerful libraries for text processing and machine learning algorithms.

Natural Language Toolkit (NLTK)

NLTK is the most common kit of tools for working with human language data in Python. It includes a set of libraries and programs for processing natural language and statistics. NLTK is commonly used as a learning tool and for carrying out research.

This library provides interfaces and methods for over 50 corpora and lexical resources. NLTK is capable of classifying text and performing other functions, such as tokenization, stemming (extracting the stem of a word), tagging (identifying the tag of a word, such as person, city…), and parsing...

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