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

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Transformers

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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. temporal attention, you’ll grasp nuances in applying transformer architectures to visual data. The course also explores spatio-temporal transformers, bridging the gap between static images and dynamic data.

By the end of the course, you’ll be equipped with the knowledge and skills to leverage transformer networks across diverse applications in deep learning and artificial intelligence.

Transformers for Computer Vision Applications

Explore the advantages and drawbacks of full attention and transformer architectures.

Introduction

Overview of Transformer Networks

Neural Machine Translation with a Transformer and Keras

Transformers in Computer Vision

Transformers in Image Classification

Fine-Tuning Vision Transformers for Image Classification

Transformers in Object Detection

Transformers in Semantic Segmentation

Spatio-Temporal Transformers

Object Detection with Vision Transformers

Wrap Up

Transformers Pros and Cons

Advantages and drawbacks of transformer architectures