Explores the impact of Deep learning on Digital Humanities, focusing on non conceptual knowledge systems and recent advancements in AI.
Explores handling network data, including types of graphs, real-world network properties, and node importance measurement.
Delves into the impact of deep learning on non-conceptual knowledge systems and the advancements in transformers and generative adversarial networks.
Covers the importance of preventive maintenance for pavement distress detection and introduces machine learning concepts for engineers.
Explores the inductive bias of different nonlinear supervised learning methods and the challenges of hyper-parameter tuning.
Delves into graph-enhanced machine learning, focusing on fraud detection, malware detection, and recommendation systems.
