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Transformers for Computer Vision Applications
Learn about transformer networks, self-attention, multi-head attention, and spatiotemporal transformers in this course, focusing on their applications in computer vision and deep learning.
4.5
36 Lessons
4 Projects
5h
Updated 5 months ago
Join 2.9 million developers at
Join 2.9 million developers at
LEARNING OBJECTIVES
- An understanding of transformers and attention mechanisms
- Hands-on implementation of computer vision techniques with transformer models
- The ability to apply transfer learning for image classification
- A strong grasp of object detection and segmentation using transformers
Learning Roadmap
2.
Overview of Transformer Networks
Overview of Transformer Networks
Grasp the fundamentals of transformer networks, attention mechanisms, and their impact on deep learning.
Introduction to TransformersThe Rise of TransformersInductive Bias in DNNsAttention: General Deep Learning IdeaAttention in NLPIs Attention All We Need?Quiz: Attention and Inductive BiasSelf-Attention MechanismSelf-Attention Matrix EquationsMultihead AttentionEncoder-Decoder AttentionTransformers Pros and ConsUnsupervised and Self-Supervised PretrainingQuiz: Transformers and Multihead Attention
3.
Transformers in Computer Vision
Transformers in Computer Vision
9 Lessons
9 Lessons
Break apart the application of transformers, attention mechanisms, and the encoder-decoder pattern in computer vision.
4.
Transformers in Image Classification
Transformers in Image Classification
3 Lessons
3 Lessons
Grasp the fundamentals of ViT, DeiT, and Swin Transformers in image classification.
5.
Transformers in Object Detection
Transformers in Object Detection
3 Lessons
3 Lessons
Take a closer look at object detection methods, from traditional approaches to DEtection TRansformers (DETR).
6.
Transformers in Semantic Segmentation
Transformers in Semantic Segmentation
3 Lessons
3 Lessons
Focus on innovative methods using ConvNets and transformers for semantic image segmentation.
7.
Spatio-Temporal Transformers
Spatio-Temporal Transformers
2 Lessons
2 Lessons
Build on the versatility of spatio-temporal transformers for advanced video analysis tasks.
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Developed by MAANG Engineers
ABOUT THIS COURSE
This is a comprehensive course on vision transformers and their use cases in computer vision. You’ll begin by exploring the rise of transformers and attention mechanisms and their role in deep neural networks.
You’ll gain insights into self-attention mechanisms, multi-head attention, and the pros and cons of transformers building a strong foundation. Next, you’ll discover how transformers reshape image analysis. Comparing self-attention with convolutional encoders and understanding spatial vs. channel vs. temporal attention, you’ll grasp nuances in applying transformer architectures to visual data.
The course also explores spatiotemporal transformers, bridging the gap between static images and dynamic data. After completing this course, you’ll have the knowledge and skills to leverage transformer networks across diverse applications in deep learning and artificial intelligence.
ABOUT THE AUTHOR
Ammar Mohanna
Ammar Mohanna, a Ph.D. holder in Edge AI, is an AI Lead at Assentify, specializing in InsurTech. With a Master's in Software Engineering, he previously served as an R&D AI Engineer, excelling in various projects.
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