Transformers for Computer Vision Applications

Transformers for Computer Vision Applications

This course covers transformer networks in computer vision, from basics to advanced applications, focusing on attention mechanisms and Python libraries.

Advanced

36 Lessons

5h

Certificate of Completion

This course covers transformer networks in computer vision, from basics to advanced applications, focusing on attention mechanisms and Python libraries.

AI-POWERED

Explanations

AI-POWERED

Explanations

This course includes

3 Projects
24 Playgrounds
8 Quizzes

This course includes

3 Projects
24 Playgrounds
8 Quizzes

Course Overview

This is a comprehensive course on transformer networks 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, multihead 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. ...Show More

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

What You'll Learn

An understanding of transformers and attention mechanisms

Show more

Course Content

1.

Introduction

This course offers a comprehensive exploration of transformers in computer vision.
2.

Overview of Transformer Networks

This chapter focuses on the transformers, attention mechanisms, self, multihead, encoder-decoder attention, and unsupervised/self-supervised pretraining

Neural Machine Translation with a Transformer and Keras

Project

3.

Transformers in Computer Vision

This chapter focuses on transformers in computer vision, covering encoder-decoder patterns and attention types and comparing self-attention with convolution.
4.

Transformers in Image Classification

This chapter focuses on image classification with vision transformers (ViT, DeiT) and shifter window (Swin) transformers.

Fine-Tuning Vision Transformers for Image Classification

Project

5.

Transformers in Object Detection

In this chapter, you’ll cover methods used for object detection, focusing on DEtection TRansformers (DETR).
6.

Transformers in Semantic Segmentation

3 Lessons

This chapter focuses on image segmentation techniques using ConvNets and transformers, highlighting their differences and applications.
7.

Spatio-Temporal Transformers

2 Lessons

In this chapter, you’ll cover spatio-temporal transformers for video action recognition using Python, focusing on video classification tasks.

Object Detection with Vision Transformers

Project

8.

Wrap Up

1 Lesson

This chapter summarizes key takeaways, covering attention mechanisms, transformers in NLP and computer vision, and extending to video analysis.

Course Author

Trusted by 1.4 million developers working at companies

Anthony Walker

@_webarchitect_

Emma Bostian 🐞

@EmmaBostian

Evan Dunbar

ML Engineer

Carlos Matias La Borde

Software Developer

Souvik Kundu

Front-end Developer

Vinay Krishnaiah

Software Developer

Eric Downs

Musician/Entrepeneur

Kenan Eyvazov

DevOps Engineer

Souvik Kundu

Front-end Developer

Eric Downs

Musician/Entrepeneur

Anthony Walker

@_webarchitect_

Emma Bostian 🐞

@EmmaBostian

Hands-on Learning Powered by AI

See how Educative uses AI to make your learning more immersive than ever before.

Instant Code Feedback

Evaluate and debug your code with the click of a button. Get real-time feedback on test cases, including time and space complexity of your solutions.

AI-Powered Mock Interviews

Adaptive Learning

Explain with AI

AI Code Mentor

FOR TEAMS

Interested in this course for your business or team?

Unlock this course (and 1,000+ more) for your entire org with DevPath