Building a Transformer with PyTorch
Deep Dive into Attention-Based Models Using PyTorch for Modern AI Mastery
Created by DataLab, Jeroen Hermans
Explore the core ideas behind transformer models and discover how attention-based architectures have transformed AI. By building a transformer from scratch with PyTorch, you'll see firsthand how these models process sequences efficiently and power modern language applications.
DataLab | Mar 2025 | 176 min
What You Will Learn
You'll start by unpacking the theory behind transformers, then move step by step through coding each component in PyTorch. By assembling the full model and training it on a practical example, you'll gain hands-on experience and a solid understanding of how each part contributes to the whole.
Key Features
- Build transformer encoders and decoders directly in PyTorch code
- Apply multi-head attention and positional encoding in real-world workflows
- Train and evaluate transformer models for sequence tasks, comparing them to RNNs
Target Audience
Designed for advanced machine learning engineers, deep learning specialists, and AI researchers who already know PyTorch and neural networks. If you want to master transformer models and understand their inner workings through practical coding, this is for you.
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