Recurrent Neural Networks: Training and Challenges

This lecture covers the fundamentals of recurrent neural networks (RNNs) and their training methodologies. It begins by addressing the limitations of fixed context language models, which struggle with variable-length sequences. The instructor introduces RNNs as a solution, highlighting their feedback loops that allow for the modeling of long-range dependencies. The lecture then delves into the training process of RNNs, specifically focusing on backpropagation through time and the challenges associated with it, such as vanishing gradients. Various techniques to mitigate these challenges, including Long Short-Term Memory (LSTM) networks and Gated Recurrent Units (GRUs), are discussed. The instructor emphasizes the importance of these architectures in maintaining information over longer sequences and their practical applications in natural language processing. The session concludes with a recap of the key concepts and a preview of upcoming topics, including transformers, which are set to be explored in future lectures.