This episode unpacks the attention mechanism at the heart of Transformer models. We explain how self-attention helps models weigh different parts of the input, how it scales in multi-head form, and what makes it different from older architectures like RNNs or CNNs. You’ll walk away with an intuitive grasp of key terms like query, key, value, and how attention layers help with context handling in language, vision, and beyond.