Understanding causal attention or masked self attention | Transformers for vision series

Causal or Masked Self-Attention Explained Step-by-Step (Used in GPT Models) In this lecture from the Transformers for Vision series, we dive deep into one of the most important concepts in transformer architecture — Causal Attention, also known as Masked Self-Attention. This lecture builds upon your understanding of the self-attention mechanism and explains how large language models like GPT-2 and GPT-3 generate text sequentially, token by token, without looking into the future. We start with a quick recap of self-attention, understand the purpose of query, key, and value transformations, and then move into why causal masking is needed in autoregressive models. You’ll see, step-by-step, how masking is applied, how negative infinity prevents data leakage, and how dropout regularization ensures robust learning. By the end of this lecture, you will have a clear understanding of: Why masking is essential in GPT-style models How causal attention prevents future token leakage How softmax and negative infinity work together in attention computation How dropout helps prevent overfitting in attention layers How context vectors are formed in the causal setting This lecture sets the foundation for understanding multi-head attention, which we will explore in the next video. 🔥 Two Versions of the Bootcamp Free Version (YouTube Playlist) – Follow all lectures in sequence on this channel. Pro Version (https://vision-transformer.vizuara.ai ) – Includes everything in the free version plus: Detailed handwritten notes (Miro) Private GitHub repository with code Private Discord community for collaboration and doubt clearance A PDF e-book on Transformers for Vision & Multimodal LLMs Hands-on assignments with grading Official course certificate Email support from Team Vizuara 👉 Enroll in the Pro Bootcamp here: http://vision-transformer.vizuara.ai/