Sequence to Sequence Models: Overview and Applications

This lecture provides an in-depth overview of sequence to sequence models, focusing on their architecture, applications, and training methodologies. It begins with a recap of recurrent neural networks (RNNs) and their limitations, particularly the vanishing gradient problem that affects long-range dependencies. The instructor introduces encoder-decoder models, explaining how they can effectively handle tasks like machine translation and code generation by separating the encoding and decoding processes. The lecture highlights the importance of paired data for training these models and discusses the challenges associated with obtaining such data. Attention mechanisms are introduced as a solution to the temporal bottleneck in sequence to sequence models, allowing the decoder to focus on relevant parts of the input sequence at each step. The lecture concludes with a discussion on the interpretability of attention and its implications for model performance. Overall, this session equips students with foundational knowledge essential for understanding advanced topics in natural language processing and machine learning.