In this episode, we explore the three Transformer model families that shaped modern NLP and large language models: BERT, GPT, and T5. We explain why they were created, how their architectures differ, and how each one defines a core capability of today’s AI systems.

We show how self-attention moved NLP beyond static word embeddings, enabling deep contextual understanding and large-scale pretraining. From there, we break down how encoder-only, decoder-only, and encoder–decoder models emerged—and why their training objectives matter as much as their architecture.

This episode covers:

• Why early NLP models failed to generalize

• How self-attention enabled contextual language understanding

• BERT and encoder-only models for analysis and comprehension

• GPT and decoder-only models for fluent text  ...