In this episode, we trace how neural networks learned to model sequences—starting with recurrent neural networks, progressing through LSTMs and GRUs, and culminating in the attention mechanism and transformers. This journey explains how NLP moved from fragile, short-term memory systems to architectures capable of modeling global context at scale, forming the backbone of modern large language models.

This episode covers:

• Why feed-forward networks fail on ordered data like text and time series

• The origin of recurrence and sequence memory in RNNs • Backpropagation Through Time and the limits of unrolled sequences

• Vanishing gradients and why basic RNNs forget long-range dependencies

• How LSTMs and GRUs use gates to preserve and control memory

• Encoder–decoder models and early neural machine translation  ...