I Visualized a Vision Transformer

Tales Of Tensors · Intermediate ·🧠 Large Language Models ·2mo ago
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. vision transformer vit explained vision transformer attention image transfo…
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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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5 Levels of AI Agents - From Simple LLM Calls to Multi-Agent Systems
Dave Ebbelaar (LLM Eng)