Class Central is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

YouTube

Interpreting Deep Neural Networks Towards Trustworthiness

International Mathematical Union via YouTube

Overview

Explore a 46-minute lecture on interpreting deep neural networks for trustworthiness, presented by Bin Yu for the International Mathematical Union. Delve into the concept of interpretable machine learning and learn about the agglomerative contextual decomposition (ACD) method for neural network interpretation. Discover the adaptive wavelet distillation (AWD) technique, an extension of ACD into the frequency domain, and its applications in cosmology and cell biology predictions. Examine the importance of a quality-controlled data science life cycle for building trustworthy interpretable models, and understand the Predictability Computability Stability (PCS) framework. Access accompanying presentation slides to enhance your understanding of these complex topics in deep learning and interpretability.

Syllabus

Bin Yu: Interpreting Deep Neural Networks towards Trustworthiness

Taught by

International Mathematical Union

Reviews

Start your review of Interpreting Deep Neural Networks Towards Trustworthiness

Never Stop Learning.

Get personalized course recommendations, track subjects and courses with reminders, and more.

Someone learning on their laptop while sitting on the floor.