Deep Learning for Robot Perception and Cognition

Häftad, Engelska, 2022

1 209 kr

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Beskrivning

Deep Learning for Robot Perception and Cognition introduces a broad range of topics and methods in deep learning for robot perception and cognition together with end-to-end methodologies. The book provides the conceptual and mathematical background needed for approaching a large number of robot perception and cognition tasks from an end-to-end learning point-of-view. The book is suitable for students, university and industry researchers and practitioners in Robotic Vision, Intelligent Control, Mechatronics, Deep Learning, Robotic Perception and Cognition tasks.

Presents deep learning principles and methodologies
Explains the principles of applying end-to-end learning in robotics applications
Presents how to design and train deep learning models
Shows how to apply deep learning in robot vision tasks such as object recognition, image classification, video analysis, and more
Uses robotic simulation environments for training deep learning models
Applies deep learning methods for different tasks ranging from planning and navigation to biosignal analysis

Produktinformation

Utgivningsdatum:2022-03-10
Mått:191 x 235 x 35 mm
Vikt:1 310 g
Format:Häftad
Språk:Engelska
Antal sidor:634
Förlag:Elsevier Science
ISBN:9780323857871

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Mer om författaren

Alexandros Iosifidis is a Professor at Aarhus University, Denmark. He leads the Machine Learning andComputational Intelligence group at the Department of Electrical and Computer Engineering. He received his Ph.D.from the Department of Informatics at Aristotle University of Thessaloniki, Greece in 2014. He participated in morethan 15 research and development projects financed by national and European funds. Anastasios Tefas received the B.Sc. in Informatics in 1997 and the Ph.D. degree in Informatics in 2002, both fromthe Aristotle University of Thessaloniki, Greece. Since 2017, he has been an Associate Professor at the Department ofInformatics, Aristotle University of Thessaloniki. Dr. Tefas participated in 20 research projects financed by national andEuropean funds. He is the coordinator of the H2020 project OpenDR, “Open Deep Learning Toolkit for Robotics.

Innehållsförteckning

1. Introduction2. Neural Networks and Backpropagation3. Convolutional Neural Networks4. Graph Convolutional Networks5. Recurrent Neural Networks6. Deep Reinforcement Learning7. Lightweight Deep Learning8. Knowledge Distillation9. Progressive and Compressive Deep Learning10. Representation Learning and Retrieval11. Object Detection and Tracking12. Semantic Scene Segmentation for Robotics13. 3D Object Detection and Tracking14. Human Activity Recognition15. Deep Learning for Vision-based Navigation in Autonomous Drone Racing16. Robotic Grasping in Agile Production17. Deep learning in Multiagent Systems18. Simulation Environments19. Biosignal time-series analysis20. Medical Image Analysis21. Deep learning for robotics examples using OpenDR