Vision Transformer (ViT) Explained | Theory + PyTorch Implementation from Scratch
About this lesson
In this video, we learn about the Vision Transformer (ViT) step by step: * The theory and intuition behind Vision Transformers. * Detailed breakdown of the ViT architecture and how attention works in computer vision. * Hands-on implementation of Vision Transformer from scratch in PyTorch. Transformers changed the world of natural language processing (NLP) with Attention is All You Need. Now, Vision Transformers are doing the same for computer vision. If you want to understand how ViT works and build one yourself in PyTorch, this video will guide you from theory to code. 📄 Papers & Resources: - Vision Transformer (ViT) Paper: https://arxiv.org/abs/2010.11929 - Attention is All You Need Paper: https://arxiv.org/abs/1706.03762 - Attention is All You Need (Video Explanation): https://www.youtube.com/watch?v=66seIToeguE 💻 Code Implementation: https://github.com/developershutt/Transformers-in-action ✨ What you’ll learn in this video: - How Vision Transformers process images by splitting them into patches. - Why self-attention is powerful for both text and images. - How to implement ViT from scratch using PyTorch and deep learning fundamentals. 🔍 Keywords for learners: Vision Transformer, ViT, Transformers in Computer Vision, PyTorch implementation, Deep Learning, Attention Mechanism, Self-Attention, Machine Learning, Neural Networks, AI, Vision AI.
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