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The Narrated Transformer Language Model

Jay Alammar via YouTube

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Overview

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This visual course explains the Transformer architecture for language modeling, covering feed-forward networks, self-attention, tokenization, embeddings, output probabilities, and next-token generation.

Syllabus

Intro
The Architecture of the Transformer
Model Training
Transformer LM Component 1: FFNN
Transformer LM Component 2: Self-Attention
Tokenization: Words to Token Ids
Embedding: Breathe meaning into tokens
Projecting the Output: Turning Computation into Language
Final Note: Visualizing Probabilities

Taught by

Jay Alammar

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