Dive deep into the world of Non-Parametric Transformers with this comprehensive 46-minute video lecture. Explore the key concepts from the paper "Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learning". Learn about the NPT architecture, its connections to BERT, Graph Neural Networks, and CNNs, and understand how it achieves impressive results on tabular data benchmarks. Discover how NPT learns underlying relational and causal mechanisms, and examine its ability to attend to similar vectors. Gain valuable insights into this innovative approach to machine learning through detailed explanations and visual aids.