Learning Path

Convolutional Neural Networks (CNNs) → Applications of Vision Transformers

This learning path equips computer vision professionals with the knowledge and skills to apply transformer architectures to vision tasks. It covers the foundational attention mechanisms, the original Vision Transformer (ViT), and advanced variants like Swin Transformer and DETR, along with hybrid architectures. The path emphasizes the conceptual prerequisites and practical applications, enabling learners to design and implement transformer-based vision models for real-world problems.