Understanding Transformers: From Self-Attention to Complete Architecture

Ever wondered how ChatGPT can understand context across paragraphs? Or how language models can maintain coherence in long conversations? The secret lies in the transformer architecture and its groundbreaking self-attention mechanism - and this workshop will demystify it all.

Prerequisites

• Basic understanding of neural networks
• Familiarity with Python and basic matrix operations
• Previous workshop on embeddings and tokenization (recommended but not required)

What You’ll Learn

• The core ideas behind self-attention and why it revolutionized NLP
• How transformers process sequences in parallel, unlike traditional RNNs
• The complete transformer architecture, from embeddings to output
• Practical intuition behind key components:
  • Multi-head attention
  • Positional encodings
  • Feed-forward networks
  • Layer normalization

By Workshop’s End

You’ll gain the ability to:

• Understand how transformers process relationships between words
• Visualize attention patterns and what they mean
• Follow modern AI research papers with greater confidence
• Connect the dots between embeddings, attention, and the full transformer pipeline

Ready to understand the architecture powering modern AI? Workshop Link