Transformers have revolutionized the field of AI, especially in natural language processing, powering models like GPT and BERT. In this code-along, you’ll dive deep into the architecture that started it all—the Transformer, introduced in the seminal paper Attention Is All You Need. Through a hands-on PyTorch implementation, you’ll gain a clear understanding of key components like self-attention, positional encoding, multi-head attention, and feedforward layers. Each part of the architecture is broken down and explained, allowing you to build a working transformer model from the ground up. By applying the model to a simple use case, you’ll see how transformers process sequences in parallel, enabling faster and more efficient learning than traditional RNNs. Whether you’re aiming to understand how large language models work or want to build your own, this session offers a solid foundation in modern deep learning. Create your own DataLab workbook for this code along: (https://www.datacamp.com/datalab/new?accountType=personal&_tag=workspace&workspaceId=189c9a05-1123-42bd-be3d-ae7dd7667cec&title=Building%20a%20Transformer%20with%20PyTorch&visibility=private)