Deploy Python Apps & Docs For Free

A new platform is looking for Alpha testers. Sign up, it's free!


Explainable deep learning models for healthcare


This course will introduce the concepts of interpretability and explainability in machine learning applications. The learner will understand the difference between global, local, model-agnostic and model-specific explanations. State-of-the-art explainability methods such as Permutation Feature Importance (PFI), Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanation (SHAP) are explained and applied in time-series classification.

Subsequently, model-specific explanations such as Class-Activation Mapping (CAM) and Gradient-Weighted CAM are explained and implemented. The learners will understand axiomatic attributions and why they are important. Finally, attention mechanisms are going to be incorporated after Recurrent Layers and the attention weights will be visualised to produce local explanations of the model.Read more.

This resource is offered by an affiliate partner. If you pay for training, we may earn a commission to support this site.

Career Relevance by Data Role

The techniques and tools covered in Explainable deep learning models for healthcare are most similar to the requirements found in Data Scientist job advertisements.

Similarity Scores (Out of 100)