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

cv_updated.tar.gz

DETR

Transformers

DETR2

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

The introduction of recurrent neural networks (RNNs)

The release of the first large-scale language model

The introduction of attention mechanisms in NLP

The development of convolutional neural networks (CNNs)

What was the significant development in NLP in 2017 that marked the rise of transformers?



What was the significant development in NLP in 2017 that marked the rise of transformers?

It enabled models to process images more efficiently.

It allowed for better handling of long-range dependencies in text.

It reduced the need for large datasets in training models.

It introduced the use of convolutional layers in language models.

Why was attention mechanism important in the field of NLP?

Why was attention mechanism important in the field of NLP?

Convolutional neural networks (ConvNets/CNNs)

In the context of image recognition, which type of network is considered impractical because it doesn’t account for local relationships in the data?

In the context of image recognition, which type of network is considered impractical because it doesn’t account for local relationships in the data?

It makes networks less sensitive to variations in data.

It increases the sensitivity of the network to variations in data.

What’s the main impact of weak inductive bias in neural networks?

What’s the main impact of weak inductive bias in neural networks?

Attention mechanisms use fixed weights, while fully connected layers use dynamic weights.

Fully connected layers focus on important features, while attention mechanisms do not.

Attention mechanisms assign varying importance weights to inputs, while fully connected layers use a single weight for all inputs.

Fully connected layers excel in NLP tasks, while attention mechanisms are better suited for image processing.

Which statement best describes the main difference between attention mechanisms and fully connected layers?

Which statement best describes the main difference between attention mechanisms and fully connected layers?

In which domain can attention mechanisms serve as a substitute for recurrent neural networks (RNNs), ensuring both flexibility and robustness?

In which domain can attention mechanisms serve as a substitute for recurrent neural networks (RNNs), ensuring both flexibility and robustness?

Test your knowledge of NLP, transformers, neural networks, and attention mechanisms in this quiz.

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

Quiz: Attention and Inductive Bias