Deep Learning for Robot Perception and Cognition (eBook, ePUB)

Redaktion: Iosifidis, Alexandros; Tefas, Anastasios

• Format: ePub

Produktbeschreibung

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

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Produktdetails

• Verlag: Elsevier Science & Techn.
• Seitenzahl: 634
• Erscheinungstermin: 4. Februar 2022
• Englisch
• ISBN-13: 9780323885720
• Artikelnr.: 63393384

Inhaltsangabe

1. Introduction 2. Neural Networks and Backpropagation 3. Convolutional
Neural Networks 4. Graph Convolutional Networks 5. Recurrent Neural
Networks 6. Deep Reinforcement Learning 7. Lightweight Deep Learning 8.
Knowledge Distillation 9. Progressive and Compressive Deep Learning 10.
Representation Learning and Retrieval 11. Object Detection and Tracking 12.
Semantic Scene Segmentation for Robotics 13. 3D Object Detection and
Tracking 14. Human Activity Recognition 15. Deep Learning for Vision-based
Navigation in Autonomous Drone Racing 16. Robotic Grasping in Agile
Production 17. Deep learning in Multiagent Systems 18. Simulation
Environments 19. Biosignal time-series analysis 20. Medical Image Analysis
21. Deep learning for robotics examples using OpenDR