Enroll Course: https://www.udemy.com/course/vit-transformer/
The Udemy course titled ‘ViT（Vision Transformer）原理与代码精讲’ offers an in-depth exploration of one of the most groundbreaking developments in computer vision—Vision Transformers (ViT). The course is ideal for learners who are eager to understand the underlying principles of ViT and see its implementation in PyTorch. Starting with a solid overview of Transformer architectures, the course delves into the specifics of how ViT adapts this technology for image classification, highlighting its superior performance on large datasets like JFT-300M. The course also covers the evolution of ViT, including comparisons with CNN-based models like ResNet, and explains the architecture in detail. The practical segment is particularly valuable, as it provides step-by-step coding tutorials using Jupyter Notebooks, demonstrating how to implement ViV with both the timm library and einops/einsum for more flexible and efficient code. Whether you’re a student, a researcher, or a developer aiming to enhance your understanding of cutting-edge vision models, this course is highly recommended for its clarity, depth, and practical insights. It equips learners with the theoretical knowledge and coding skills needed to innovate or improve upon existing Vision Transformer models.
Enroll Course: https://www.udemy.com/course/vit-transformer/