A DataCamp course that opens up the architecture powering modern LLMs. In about two hours you build the core components yourself — positional encoding, attention mechanisms and feed-forward sublayers — and assemble them into a transformer. Advanced and hands-on, best for people who already know PyTorch and want to understand transformers from the inside.
Good for: Understanding transformer internals by building them in PyTorch.
Less suitable if: You are new to deep learning or want to just use pre-built models.
Requirements: PyTorch and deep-learning fundamentals.
Realistic time: Around 2 hours.
About this course
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.
What you'll learn
Explain the transformer architecture
Implement positional encoding
Build attention mechanisms
Construct feed-forward sublayers
Assemble a transformer in PyTorch
Reason about how LLMs are built
This course includes
2h
On-demand video
Yes
Certificate
Yes
Mobile access
English
Language
What it costs
DataCamp runs on a subscription — roughly $14/month billed annually (more month-to-month), with the first chapter of each course free to try. A certificate of completion is included with the subscription.
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