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. vision transformer vit explained vision transformer attention image transformer transformer for vision patch embeddings cls token self attention vision multi head attention positional embeddings vit architecture how vision transformers work cnn vs vision transformer deep learning vision computer vision transformer 00:00 Tokenization: Converting Text to Numbers 00:58 Embeddings and Positional Encoding 01:53 The Residual Stream 01:59 Multi-Head Self-Attention and Layer Norm 02:30 Query, Key, and Value Projections 02:51 Computing Scaled Dot-Product Attention 04:01 Residual Connections in the Attention Block 04:24 The MLP (Feed Forward Network) 05:37 Predicting the Next Token (The LM Head) 06:33 Temperature Scaling and Softmax 07:03 Sampling Strategies: Top-K and Top-P 07:26 Auto-Regressive Generation 07:41 KV Caching Optimization