Artificial Vision and Language Processing for Robotics

Álvaro Morena Alberola, Gonzalo Molina Gallego, Unai Garay Maestre

ISBN 10: 183855226X / ISBN 13: 9781838552268

Verlag: Packt Publishing Limited, GB, 2019

Beschreibung

Create end-to-end systems that can power robots with artificial vision and deep learning techniquesKey FeaturesStudy ROS, the main development framework for robotics, in detailLearn all about convolutional neural networks, recurrent neural networks, and roboticsCreate a chatbot to interact with the robotBook DescriptionArtificial Vision and Language Processing for Robotics begins by discussing the theory behind robots. You'll compare different methods used to work with robots and explore computer vision, its algorithms, and limits. You'll then learn how to control the robot with natural language processing commands. You'll study Word2Vec and GloVe embedding techniques, non-numeric data, recurrent neural network (RNNs), and their advanced models. You'll create a simple Word2Vec model with Keras, as well as build a convolutional neural network (CNN) and improve it with data augmentation and transfer learning. You'll study the ROS and build a conversational agent to manage your robot. You'll also integrate your agent with the ROS and convert an image to text and text to speech. You'll learn to build an object recognition system using a video.By the end of this book, you'll have the skills you need to build a functional application that can integrate with a ROS to extract useful information about your environment.What you will learnExplore the ROS and build a basic robotic systemUnderstand the architecture of neural networksIdentify conversation intents with NLP techniquesLearn and use the embedding with Word2Vec and GloVeBuild a basic CNN and improve it using generative modelsUse deep learning to implement artificial intelligence(AI)and object recognitionDevelop a simple object recognition system using CNNsIntegrate AI with ROS to enable your robot to recognize objectsWho this book is forArtificial Vision and Language Processing for Robotics is for robotics engineers who want to learn how to integrate computer vision and deep learning techniques to create complete robotic systems. It will prove beneficial to you if you have working knowledge of Python and a background in deep learning. Knowledge of the ROS is a plus.

Bestandsnummer des Verkäufers LU-9781838552268

Bibliografische Details

• Titel: Artificial Vision and Language Processing for Robotics
• Verlag: Packt Publishing Limited, GB
• Erscheinungsjahr: 2019
• Sprache: Englisch
• ISBN-10: 183855226X
• ISBN-13: 9781838552268
• Einband: Paperback
• Zustand: New

Über diesen Titel

Inhaltsangabe

Artificial Vision and Language Processing for Robotics will help you train and deploy models that are built with advanced deep learning techniques and integrate them in a unified end-to-end application. With this book, you will learn all about the three hottest topics of artificial intelligence: convolutional neural networks, recurrent neural ...

Über die Autorinnen und Autoren

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.