Advanced Transformer Architectures: Masked Attention, Encoder-Decoder, and Beyond

Ever wondered how DALL-E understands both images and text? Or how GPT models can predict the next word while Bert understands context in both directions? This workshop dives deep into the variants of transformer architectures that power today’s most advanced AI systems.

Prerequisites

• Understanding of basic transformer architecture and self-attention
• Previous workshops on embeddings and basic transformers (recommended)
• Basic Python knowledge and familiarity with deep learning concepts

What You’ll Learn

• Different Attention Patterns & Their Uses:

• Recap on attention

• Causal/Masked (GPT-style) attention

• Cross-attention in encoder-decoder models

• Architecture Deep Dives:

• Encoder-only models (BERT family)

• Decoder-only models (GPT family)

• Encoder-decoder models (T5, BART)

• Multimodal architectures (Training a LLM with CLIP)

• Practical Applications:

• When to use each architecture

• Trade-offs between different approaches

• Real-world examples and use cases

By Workshop’s End

You’ll gain the ability to:

• Understand the key differences between major transformer variants
• Know which architecture suits which task
• Grasp how transformers handle different types of data (text, images, audio)
• Appreciate the evolution from pure text to multimodal AI

Ready to master the full spectrum of transformer architectures? Workshop Link