Course Conclusion
Review core Explainable AI concepts and methods such as saliency maps, class activation maps, and counterfactual explanations. Learn to implement these algorithms and assess their quality using interpretability metrics like feature agreement and predictive faithfulness. This lesson consolidates your understanding of interpreting neural network decisions.
We'll cover the following...
In this course, we covered a popular framework known as Explainable AI (XAI) that provides us with tools to understand and interpret the internal logic behind the predictions made by a deep learning network. Using XAI algorithms, we can generate explanations that can help data scientists generate insights into the behavior and working of the model they have trained.
Saliency maps
We started by formulating the framework and taxonomy of XAI and discussed the first class of algorithms based on saliency maps. A saliency map is an image where the brightness of each pixel represents how important or salient that pixel is in the network prediction. In other words, the brighter the pixel, the more important that pixel is. If a particular region in the saliency map is concentrated with bright pixels, it means that the region or feature is important for prediction.
