Alexandros Iosifidis,
Anastasios Tefas
Deep Learning for Robot Perception and Cognition
Paperback Engels 2022 9780323857871Levertijd ongeveer 9 werkdagen
Gratis verzonden
Samenvatting
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.
Specificaties
Lezersrecensies
Wees de eerste die een lezersrecensie schrijft!
Inhoudsopgave
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
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
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