Sequences and Recurrent Networks (SRN)

Description

The course provides a comprehensive overview of sequential data processing and the foundational principles of recurrent neural networks (RNNs). The course explores the unique challenges posed by sequential data, including time series, natural language, and audio. Participants learn the architecture and mechanisms of RNNs, including long short-term memory (LSTM) and gated recurrent units (GRUs), understanding how these structures enable capturing dependencies over time. Through hands-on exercises and projects, students gain proficiency in implementing and training recurrent networks, addressing issues like vanishing gradients and exploding gradients. Additionally, the course delves into advanced topics such as sequence generation, attention mechanisms, and sequence-to-sequence models.