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Transformers for Computer Vision Applications

Name: Transformers for Computer Vision Applications
Price: 59 USD
Author: Ammar Mohanna

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.

4.4

36 lessons

4 projects

1 AI mock interview

Created by Ammar Mohanna

Transformers for Computer Vision Applications

What's included

36 lessons across 8 modules
8 quizzes with instant feedback
4 projects in preconfigured environments
1 AI mock interview with scoring rubrics
AI Tutor on every lesson

Join 3.1 million developers at

What You'll Learn

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

36 Lessons3 Projects8 Quizzes

Introduction

Get familiar with transformers in computer vision, covering key concepts and architectures.

Introduction to the Course

Overview of Transformer Networks

Grasp the fundamentals of transformer networks, attention mechanisms, and their impact on deep learning.

Introduction to Transformers

The Rise of Transformers

Inductive Bias in DNNs

Attention: General Deep Learning Idea

Attention in NLP

Is Attention All We Need?

Quiz: Attention and Inductive Bias

Self-Attention Mechanism

Self-Attention Matrix Equations

Multihead Attention

Encoder-Decoder Attention

Transformers Pros and Cons

Unsupervised and Self-Supervised Pretraining

Quiz: Transformers and Multihead Attention

Neural Machine Translation with a Transformer and Keras

Project

Transformers in Computer Vision

9 Lessons

Break apart the application of transformers, attention mechanisms, and the encoder-decoder pattern in computer vision.

Vision Transformer for Image Classification

Project

Premium

Transformers in Image Classification

3 Lessons

Grasp the fundamentals of ViT, DeiT, and Swin Transformers in image classification.

Fine-Tuning Vision Transformers for Image Classification

Project

Transformers in Object Detection

3 Lessons

Take a closer look at object detection methods, from traditional approaches to DEtection TRansformers (DETR).

Transformers in Semantic Segmentation

3 Lessons

Focus on innovative methods using ConvNets and transformers for semantic image segmentation.

Spatio-Temporal Transformers

2 Lessons

Build on the versatility of spatio-temporal transformers for advanced video analysis tasks.

Object Detection with Vision Transformers

Project

Wrap Up

Step through key concepts of transformers in computer vision and their practical applications.

Conclusion

Autonomous Vehicle System Design

Mock Interview

Premium

Certificate of Completion

Showcase your accomplishment by sharing your certificate of completion.

About this course

WHY I WROTE 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.

Learn more about Ammar

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