7 Deep Learning

Deep learning has been very successful, especially in tasks that involve images and texts such as image classification and language translation. The success story of deep neural networks began in 2012, when the ImageNet image classification challenge ^[1] was won by a deep learning approach. Since then, we have witnessed a Cambrian explosion of deep neural network architectures, with a trend towards deeper networks with more and more weight parameters.

To make predictions with a neural network, the data input is passed through many layers of multiplication with the learned weights and through non-linear transformations. A single prediction can involve millions of mathematical operations depending on the architecture of the neural network. There is no chance that we humans can follow the exact mapping from data input to prediction. We would have to consider millions of weights that interact in a complex way to understand a prediction by a neural network. To interpret the behavior and predictions of neural networks, we need specific interpretation methods. The chapters assume that you are familiar with deep learning, including convolutional neural networks.

There are two reasons why it makes sense to consider interpretation methods developed specifically for neural networks: First, neural networks learn features and concepts in their hidden layers and we need special tools to uncover them. Second, the gradient can be utilized to implement interpretation methods that are more computationally efficient than model-agnostic methods that look at the model “from the outside”. Also most other methods in this book are intended for the interpretation of models for tabular data. Image and text data require different methods.

The chapter covers the following techniques that answer the question:

• What features has the neural network learned?