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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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 Morena Alberola Morena Alberola
Author Profile Icon Morena Alberola
Morena Alberola
 Molina Gallego Molina Gallego
Author Profile Icon Molina Gallego
Molina Gallego
 Garay Maestre Garay Maestre
Author Profile Icon Garay Maestre
Garay Maestre
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Toc

Table of Contents (11) Chapters Close

About the Book 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 1. Appendix

About the Authors

Álvaro Morena Alberola is a computer engineer and loves robotics and artificial intelligence. Currently, he is working as a software developer. He is extremely interested in the core part of AI, which is based on artificial vision. Álvaro likes working with new technologies and learning how to use advanced tools. He perceives robotics as a way of easing human lives; a way of helping people perform tasks that they cannot do on their own.

Gonzalo Molina Gallego is a computer science graduate and specializes in artificial intelligence and natural language processing. He has experience of working on text-based dialog systems, creating conversational agents, and advising good methodologies. Currently, he is researching new techniques on hybrid-domain conversational systems. Gonzalo thinks that conversational user interfaces are the future.

Unai Garay Maestre is a computer science graduate and specializes in the field of artificial intelligence and computer vision. He successfully contributed to the CIARP conference of 2018 with a paper that takes a new approach to data augmentation using variational autoencoders. He also works as a machine learning developer using deep neural networks applied to images.

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