An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale (Paper Explained) Published 2020-10-04 Download video MP4 360p Recommendations 30:49 Vision Transformer Basics 14:47 Vision Transformer for Image Classification 20:18 Why Does Diffusion Work Better than Auto-Regression? 31:21 [Classic] Deep Residual Learning for Image Recognition (Paper Explained) 36:16 The math behind Attention: Keys, Queries, and Values matrices 39:13 DINO: Emerging Properties in Self-Supervised Vision Transformers (Facebook AI Research Explained) 19:48 Transformers explained | The architecture behind LLMs 27:14 But what is a GPT? Visual intro to Transformers | Chapter 5, Deep Learning 40:57 DETR: End-to-End Object Detection with Transformers (Paper Explained) 34:38 Vision Transformer and its Applications 17:38 The moment we stopped understanding AI [AlexNet] 11:10 Swin Transformer paper animated and explained 48:07 OpenAI CLIP: ConnectingText and Images (Paper Explained) 30:27 Vision Transformers (ViT) Explained + Fine-tuning in Python 59:33 LambdaNetworks: Modeling long-range Interactions without Attention (Paper Explained) 12:48 Has Generative AI Already Peaked? - Computerphile 1:08:38 Transformers in Vision: From Zero to Hero 17:32 Exposing Scientific Dogmas - Banned TED Talk - Rupert Sheldrake Similar videos 05:26 An image is worth 16x16 words: ViT | Vision Transformer explained 1:12:39 An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale 07:02 An Image Is Worth 16x16 Words - Paper Explained 10:14 Vision Transformer (ViT) - An Image is Worth 16x16 Words: Transformers for Image Recognition 12:59 [Paper Review] An Image is worth 16x16 words: transformers for image recognition at scale 14:35 (ViT) An Image Is Worth 16x16 Words | Paper Explained 06:44 Paper Talks #1 - An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale 16:51 Vision Transformer Quick Guide - Theory and Code in (almost) 15 min More results