A walkthrough of the foundational 2021 Google paper 'An Image is Worth 16x16 Words', which introduced Vision Transformers (ViT) for image classification. The session covers how ViT splits images into fixed-size patches, converts them into embeddings with positional information, and feeds them into a standard transformer encoder — analogous to how NLP transformers handle word tokens. Key concepts explained include self-supervised pre-training, fine-tuning, multi-head attention in the visual context, MLP blocks, inductive bias differences between CNNs and transformers, and position embedding interpolation for higher-resolution fine-tuning. The core finding: transformers outperform CNNs on image classification when trained at scale. The session also briefly contrasts ViT with diffusion-based models used in modern image/video generation.