Evaluate the impact of an AI application used in the real world. (case study) : Working with Flower Images: Case Study -

The video tutorial discusses the use of Resnet in deep learning, highlighting its various variants and improvements over time. It explains the architecture of Resnet, including the concept of skip connections and bottleneck blocks, and emphasizes the importance of customization for better model performance. The tutorial also covers the practical application of Resnet, suggesting the use of open-source code from GitHub for flexibility and up-to-date features.

What is the main purpose of using ResNet in deep learning?

What are some of the variants of ResNet mentioned in the text?

How does the ResNet architecture allow for customization?

What are the advantages of using open-source code from the community for ResNet?

Why is it suggested to read the original research paper on ResNet?

What is the relationship between the number of layers and model accuracy in ResNet?

How did the introduction of ResNet address the problem of training deeper networks?

Can you explain the significance of skip connections in ResNet?

What improvements were made to ResNet in 2016?

What role does batch normalization play in the ResNet architecture?