Coding Swin Transformer from Scratch in PyTorch

About this course

This course is a complete, hands-on guide to building the Swin Transformer from scratch in PyTorch, one of the most powerful modern architectures in computer vision.

Instead of just using pre-built libraries, you will understand and implement every core component step-by-step, including Patch Embedding, Window-based Multi-Head Self Attention (W-MSA), Shifted Window Attention (SW-MSA), Relative Position Bias, and Patch Merging.

We start from the fundamentals of Vision Transformers and gradually move toward building a full hierarchical Swin Transformer architecture, similar to those used in state-of-the-art research and production systems.

By the end of this course, you will not only understand how Swin Transformer works internally, but you will also be able to train and apply it to real-world image classification tasks using PyTorch.

This course is designed for deep learning enthusiasts, computer vision engineers, and students who want to move beyond theory and gain real implementation skills in modern transformer architectures.

What makes this course special?

You build everything from scratch (no black-box libraries)Deep focus on intuition + implementation

Clear explanation of complex concepts like shifted windows

Practical PyTorch coding for real datasets

Research-level architecture made simple

Whether you are preparing for AI research, interviews, or building advanced vision models, this course will take you to the next level in deep learning and computer vision.

Prices, discounts and availability are set by Udemy. We may earn a commission when you purchase through links on this site.