What if you could build a neural network that understands language without ever needing to process it step-by-step? In this episode, we break down the 2017 breakthrough paper "Attention Is All You Need" and the birth of the Transformer architecture.

We explore why traditional Recurrent Neural Networks (RNNs) and CNNs reached their limits and how the Transformer’s reliance on self-attention allowed for massive parallelisation and lightning-fast training. From Multi-Head Attention to Positional Encodings, discover the technical innovations that shattered state-of-the-art records in machine translation and paved the way for the future of sequence modelling.