I Visualized a Vision Transformer
Follow a single image patch—the cat’s eye—through a Vision Transformer to see exactly how modern AI learns to see. This video breaks down Vision Transformers step by step, from raw pixels and patch embeddings to self-attention, positional encodings, the CLS token, and final image classification. You’ll learn how patches communicate through multi-head attention, how representations evolve across layers, and how Vision Transformers differ from CNNs, all with an intuitive, end-to-end walkthrough of the full architecture.
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vit explained
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Chapters (13)
Tokenization: Converting Text to Numbers
0:58
Embeddings and Positional Encoding
1:53
The Residual Stream
1:59
Multi-Head Self-Attention and Layer Norm
2:30
Query, Key, and Value Projections
2:51
Computing Scaled Dot-Product Attention
4:01
Residual Connections in the Attention Block
4:24
The MLP (Feed Forward Network)
5:37
Predicting the Next Token (The LM Head)
6:33
Temperature Scaling and Softmax
7:03
Sampling Strategies: Top-K and Top-P
7:26
Auto-Regressive Generation
7:41
KV Caching Optimization
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