Deep Learning Techniques: Recurring Networks and LSTM Models

This lecture covers the implementation and optimization of recurring networks using LSTM models. The instructor begins by reviewing the previous assignment on recurring networks, discussing the importance of data pre-processing and packing data into batches. The lecture then delves into defining a class for the recurring network, focusing on hyperparameters such as the size of the latent variable and the embedding table. The instructor explains how to utilize PyTorch's LSTM functionalities, emphasizing the need to manage varying input lengths effectively. The session includes practical examples of training the model, evaluating its performance, and making adjustments to improve accuracy. The instructor also discusses advanced techniques like mean pooling and max pooling, highlighting their impact on model performance. The lecture concludes with a discussion on project expectations, encouraging students to explore innovative approaches in their research proposals. Overall, the session provides a comprehensive overview of recurring networks and their applications in deep learning.