Comprehensive Guide to Transformers for Computer Vision Engineers

This comprehensive guide is designed for data scientists specializing in Computer Vision who want to learn about Transformer architectures. The content covers both theoretical foundations and practical applications, with a focus on Vision Transformers (ViT) and related models.

What You’ll Learn

• Theoretical understanding of transformer architecture and attention mechanisms
• Overview of transformer applications in Computer Vision
• Detailed setup instructions for different hardware environments (including options for laptops without GPUs)
• Hands-on practice exercises with step-by-step solutions
• References to further resources and research papers

All hands-on exercises are compatible with Google Colab, making them accessible even without dedicated GPU hardware.

Course Structure

1. Comprehensive Overview - A complete overview of the entire course content.

2. Transformer Theory - Comprehensive explanation of transformer architecture, attention mechanisms, and their adaptation for computer vision tasks.

3. Transformer Applications in Computer Vision - In-depth exploration of Vision Transformers and their applications in computer vision.

4. Setup Instructions - Detailed instructions on setting up your environment for working with Vision Transformers.

5. Hands-on Practice - Step-by-step exercises and solutions for working with Vision Transformers.

Getting Started

To begin your journey with Vision Transformers, we recommend starting with the Comprehensive Overview to get a broad understanding of the material, followed by diving into the Transformer Theory section to build a solid foundation, and then exploring practical applications and hands-on exercises.