Explore the intricacies of Vision Transformers in this comprehensive 49-minute tutorial presented by Bharath Ramsundar and Amal Sebastian at MICDE University of Michigan. Delve into the cutting-edge application of transformer architectures in computer vision tasks, gaining insights into their structure, functionality, and advantages over traditional convolutional neural networks. Learn about the key components of Vision Transformers, including self-attention mechanisms and positional encodings, and understand how these elements contribute to their remarkable performance in image recognition and classification tasks. Discover practical implementation techniques, best practices, and potential challenges when working with Vision Transformers, equipping yourself with valuable knowledge to leverage this powerful technology in your own computer vision projects.