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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/