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

The video tutorial discusses building a model using Resnet, focusing on reusing previous work and making minor adjustments for image processing. It covers data preprocessing, including image scaling and augmentation, and explains the importance of conducting an ablation run to ensure the model functions correctly. The tutorial highlights issues of overfitting and accuracy, particularly in imbalanced datasets, and suggests using AUC for more reliable model evaluation. The video emphasizes the importance of understanding and adapting code for different projects.

What is the significance of using the same Resnet model in the current project?

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Explain the importance of checking for overfitting during model training.

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What could be the reasons for the drop in accuracy observed during training?

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How does class imbalance affect model training and evaluation?

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What alternative metric can be used instead of accuracy for evaluating model performance?

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