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Sources
Machine Learning Explained
3Blue1Brown • 52.1K views
15:23
Attention Is All You Need
Vaswani et al. • 2017
15 pages
Deep Learning Guide
Towards Data Science
12 min read
Can you explain how transformers work using the paper I uploaded?
Based on the "Attention Is All You Need" paper, transformers work by using self-attention mechanisms to process sequences in parallel rather than sequentially...
How does this connect to the video example?
Understanding Transformers
Key Insights:
• Self-attention allows the model to weigh different parts of the input sequence
• No recurrence needed - fully parallelizable
• Uses queries, keys, and values to compute attention weights
"The attention mechanism allows us to focus on different parts of the input sequence. Instead of encoding the entire sequence into a single vector, we can attend to all positions."
@8:45
Attention Is All You Need
Section 3.2: Multi-Head Attention
The multi-head mechanism allows the model to attend to information from different representation subspaces at different positions.
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