Transformer Models with PyTorch

In about two hours you construct the core pieces yourself — positional encoding, attention, feed-forward sublayers — and wire them into a working transformer, which is a genuinely different kind of understanding from calling a pre-built model. That's why it's pitched at people who already know PyTorch and want to see inside the box, and the wrong course if you're new to deep learning or you just want to use ready-made models. Access is via DataCamp's subscription (about $14/month billed annually, first chapter free); the completion certificate documents the learning rather than accrediting it. The transformer architecture it dissects is foundational to the whole field (as of 2026).

Transformer Models with PyTorch opens up the architecture that powers modern LLMs. In about two hours you build the core components yourself — positional encoding, attention mechanisms, and feed-forward sublayers — and assemble them into working transformer models rather than treating them as a black box.