Transformers and the End of Inductive Bias in Neural Networks

Explore a 12-minute video lecture that examines how Transformer architectures revolutionized deep learning by breaking away from traditional inductive biases found in CNNs and RNNs. Learn about the fundamental shift from specialized neural networks with built-in assumptions (like locality bias in CNNs and recency bias in RNNs) to the more generalized, data-driven approach of Transformers. Delve into the role of attention mechanisms in deep learning while building upon concepts from previous discussions on attention and self-attention. Drawing from seminal research papers, discover why Transformers opted for a more flexible architecture despite its increased data requirements, marking a significant departure from conventional neural network design philosophy.