Anastasios Tefas – författare

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

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

This book constitutes the refereed proceedings of the 23rd International Conference on Engineering Applications of Neural Networks, EANN 2022, held in Chersonisos, Crete, Greece, in June 2022.The 37 revised full papers and 5 revised short papers presented were carefully reviewed and selected from 72 submissions. The papers are organized in topical sections on Bio inspired Modeling / Novel Neural Architectures; Classification / Clustering; Machine Learning; Convolutional / Deep Learning; Datamining / Learning / Autoencoders; Deep Learning / Blockchain; Machine Learning for Medical Images / Genome Classification; Reinforcement /Adversarial / Echo State Neural Networks; Robotics / Autonomous Vehicles, Photonic Neural Networks; Text Classification / Natural Language.