Скачать или смотреть 🤖 The Magic Behind ChatGPT: Tokens & Context Explained (LLMs for Beginners - Lesson 2)

Have you ever wondered how models like ChatGPT and GPT-4 understand context and relationships in language so well? The secret is the Attention Mechanism.

In this lesson (Part 2 of our Large Language Models course), we break down the revolutionary concept that taught computers how to focus. We move from simple intuition to the core mechanics, explaining one of the most important ideas in modern AI.

What you'll learn in this video:
🔹 The "Why": An intuitive explanation of why attention is necessary for understanding language.
🔹 Attention Scores: How models quantify the importance of different words in a sentence.
🔹 Query, Key, Value (QKV): A simple analogy to understand the core components of attention, thinking of it like a database search.
🔹 Scaled Dot-Product Attention: We'll look at the famous formula and break down what each part does.
​🔹 Self-Attention Matrix: A visual look at how a sentence "sees" itself, revealing the hidden linguistic structures the model learns.

This mechanism is the foundational building block of the Transformer architecture, which powers almost every state-of-the-art language model today.

Timestamps:
(0:00) - Intro: Lesson 2 - The Attention Mechanism
(0:18) - Why Do We Need Attention?
(0:56) - Understanding Attention Scores
(1:40) - The Query, Key, and Value Mechanism
(2:34) - The Scaled Dot-Product Attention Formula
(3:27) - Attention in Action: A Practical Example
(4:10) - The Self-Attention Matrix
(5:06) - End of Lesson

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