DADataCamp
Transformer Models with PyTorch
Advanced 2 hours English Completion Certificate
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
Comparison · LBS
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Prices & availability can change — confirm on the provider's site. We're not affiliated with any single provider.
Instructor
JC
James Chapman
DataCamp instructor
— learners— courses — instructor rating
Created by James Chapman, a DataCamp curriculum developer focused on deep learning and modern AI architectures.
Requirements
- Solid PyTorch and deep-learning basics
Who this course is for
- ML and AI engineers
- LLM engineers going deeper
- Advanced PyTorch users
About this provider
DA
DataCamp
Data science and analytics learning platform. 10M+ learners, hands-on coding exercises.
4.4 trust score
Frequently asked questions
Yes — it's an advanced course that assumes solid PyTorch and deep-learning fundamentals. Take Introduction to Deep Learning with PyTorch first.
Yes — you implement the core components (positional encoding, attention, feed-forward sublayers) and assemble them, rather than only using pre-built ones.
About two hours.
Yes — DataCamp provides a shareable statement of completion.
A subscription from around $29/month, with limited free access to the first chapter.