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Throughout this book, we have built several deep learning models that can perform different kinds of tasks for us. For example, a handwritten digit classifier, an image-caption generator, a sentiment classifier, and more. Although we have mastered how to train and evaluate these models using PyTorch, we do not know what precisely is happening inside these models while they make predictions. Model interpretability or explainability is that field of machine learning where we aim to answer the question, why did the model make that prediction? More elaborately, what did the model see in the input data to make that particular prediction?

In this chapter, we will use the handwritten digit classification model from Chapter 1, Overview of Deep Learning Using PyTorch, to understand its inner workings and thereby explain why the model makes a certain prediction for a given input. We will first dissect the model using...