LLM Mastery 03: Transformer Attention All You Need
Welcome to the third episode of LLM Mastery! In this video, we delve deep into the core mechanism that makes Transformers powerful: Attention. Understanding how attention works is crucial for mastering Large Language Models (LLMs).
In this episode, you will learn:
Self-Attention Mechanism:
What is self-attention?
How does it work within a Transformer model?
Key steps: Query, Key, and Value vectors
Multi-Head Attention:
Explanation of multi-head attention
How it improves the model's ability to focus on different parts of the input
Positional Encoding:
The role of positional encoding in Transformers
How it helps the model understand the order of tokens
Attention in Practice:
Real-world applications of attention mechanisms
Examples from models like BERT and GPT-3
Why should you watch this video?
Gain a comprehensive understanding of the attention mechanism
Learn how self-attention and multi-head attention work
See practical examples of attention in action
Enhance your knowledge of key concepts in LLMs
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