Neural Network Architectures: Recurrent & Sequence Models Questions

Comprehensive understanding of RNNs, LSTMs, GRUs, and Transformer architectures for sequential data. Understand the motivation for each (vanishing gradient problem, LSTM gates), attention mechanisms, self-attention, and multi-head attention. Know applications in NLP, time series, and other domains. Discuss Transformers in detail—they've revolutionized NLP and are crucial for generative AI.