Rose discusses the importance of uncertainty quantification in deep learning, emphasizing that it's not just about predicting outcomes but also providing confidence levels for those predictions. She explores various methods, comparing frequentist and Bayesian approaches, and highlights the trade-offs between computational efficiency and the accuracy of confidence intervals. Ultimately, she offers a practical recipe for practitioners to choose the right method based on their specific needs.
