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.

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36 Lessons

Certificate of Completion

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.

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Explanations

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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 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. ...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

Course Content

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

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

Introduction to Transformers in Computer Vision Encoder-Decoder Design Pattern Convolutional Encoders Self-Attention vs. Convolution Quiz: Encoder-Decoder Architecture and Attention Mechanism in Computer Vision Spatial vs. Channel vs. Temporal Attention Local vs. Global Attention Pros and Cons of Attention in CV Quiz: Attention in Computer Vision

Vision Transformer for Image Classification

Project

Transformers in Image Classification

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

Image Classification with Vision Transformer (ViT and DeiT)Shifter Window (Swin) Transformers Quiz: Transformers in Image Classification

Fine-Tuning Vision Transformers for Image Classification

Project

Transformers in Object Detection

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

Object Detection Methods Review DEtection TRansformers (DETR)Quiz: Transformers in Object Detection

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

1 Lesson

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

Course Author

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