Seq2Seq Models: Attention vs. No Attention

This lecture covers the implementation of Sequence-to-Sequence (Seq2Seq) models with and without attention mechanisms. It explains the architecture of encoder-decoder models, the role of context vectors, and the process of decoding source sentences to target sentences. The lecture also delves into the use of bidirectional RNNs, teacher forcing, and different types of attention mechanisms like additive and self-attention. Additionally, it discusses the importance of weight initialization, training loops, and the applications of attention mechanisms in various ML and NLP tasks.