Explores trajectory forecasting in autonomous vehicles, focusing on deep learning models for predicting human trajectories in socially-aware transportation scenarios.
Explores perception in deep learning for autonomous vehicles, covering image classification, optimization methods, and the role of representation in machine learning.
Concludes with key takeaways on the increasing visibility of technology in humanitarian contexts, emphasizing the evaluation of technology against expected benefits and risks.
Introduces the concept of risk resilience and the principles of Resilience Engineering, emphasizing the importance of proactive risk management strategies.
Covers the fundamentals of multilayer neural networks and deep learning, including back-propagation and network architectures like LeNet, AlexNet, and VGG-16.
Covers the foundational concepts of deep learning and the Transformer architecture, focusing on neural networks, attention mechanisms, and their applications in sequence modeling tasks.
