Introduction to the Course

This course offers a comprehensive exploration of transformers in computer vision, making it a valuable choice for anyone interested in staying at the forefront of deep learning and computer vision technologies. Enrolling in this course leads to:

• Gain a deep understanding of transformer networks and their significance in modern AI.

• Explore state-of-the-art architectures for various computer vision applications, including image classification, semantic segmentation, object detection, and video processing.

• Learn how to practically apply architectures such as vision transformer (ViT), DEtection TRansformers (DETR), and shifted window (Swin).

• Acquire a fundamental understanding of attention mechanisms as a general concept in deep learning, which is crucial for various AI applications.

• Expand knowledge of inductive bias and its correlation with modeling assumptions in deep learning models.

• Understand how transformers have made a huge impact not only in computer vision but also in natural language processing (NLP) and machine translation.

The target audience for this course

This course is suitable for a diverse audience, including:

• Aspiring data scientists and machine learning engineers looking to deepen their understanding of cutting-edge AI technologies

• Computer vision enthusiasts interested in staying updated with the latest developments in the field

• Developers and engineers who want to apply transformer models to solve real-world computer vision problems

• Students and researchers seeking a comprehensive overview of attention mechanisms and their applications in deep learning

• Professionals in the fields of AI, NLP, or computer vision who want to broaden their knowledge base and stay competitive in their careers

Outcomes of completing this course

Upon course completion, learners will have gained a wealth of knowledge and practical skills, including:

• A deep understanding of transformer networks and their relevance in various domains

• Proficiency in state-of-the-art architectures for computer vision applications such as image classification, semantic segmentation, object detection, and video processing

• Practical experience in implementing cutting-edge vision transformer models like ViT, DETR, and Swin

• A solid grasp of attention mechanisms as a foundational concept in deep learning, applicable beyond computer vision

• Insight into inductive bias and its role in shaping deep learning models

• The ability to apply transformer models not only in computer vision but also in NLP and machine translation

• Knowledge about different types of attention mechanisms in the context of computer vision

Requirements or prerequisites for this course

While this course is designed to provide a comprehensive understanding of transformers in computer vision, it is advisable to have some prior knowledge in related areas to make the most of the learning experience. The following prerequisites are recommended:

1. Moderate knowledge of machine learning

2. Moderate knowledge of computer vision

3. Introductory level knowledge of natural language processing

Course organization

Throughout the journey, the exciting world of transformers and their applications in various fields will be explored. This course has been organized into two main sections to facilitate straightforward learning.

Section 1: Understanding transformers

In the first section, we’ll take a high-level overview of transformers and their significance in artificial intelligence and machine learning.

1. Overview of Transformers Networks: This chapter introduces the fundamental ideas and structure of transformers.

Section 2: Advanced transformer applications

The second part of the course is all about getting technical with transformers and applying them in real-world scenarios for image data. Each chapter in this section is designed with a clear format to make learning easy:

2. Transformers in Computer Vision: Exploring how transformers are applied in the field of computer vision

3. Transformers in Image Classification: Understanding the role of transformers in image classification tasks

4. Transformers in Object Detection: Discovering how transformers enhance object detection techniques

5. Transformers in Semantic Segmentation: Exploring the application of transformers in semantic segmentation tasks

6. Spatio-Temporal Transformers: Exploring the spatio-temporal transformers and their applications in depth

Essentially, this course provides a versatile skill set, enabling proficiency in addressing complex AI challenges and unlocking new opportunities in the research, development, and application of transformer models.