RNNs, LSTMs & Attention
Episode notes
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
• Why recurrence fundamentally limits parallelism on GPUs
• The emergence of attention as a solution to context bottlenecks
• Queries, keys, and values as a mechanism for global relevance
• How transformers remove recurrence to enable full parallelism
• Positional encoding and multi-head attention
• Real-world impact on translation, time series, and reinforcement learning
This episode is part of the Adapticx AI Podcast. Listen via the link provided or search “Adapticx” on Apple Podcasts, Spotify, Amazon Music, or most podcast platforms.
Sources and Further Reading
All referenced materials and extended resources are available at: