Summary

Developing classification and prediction models using deep learning networks involves extensive experimentation to arrive at models with high-quality performance. To help with this process, there are various methods that are very useful for visualizing and controlling network training. In this chapter, we went over four such useful methods. We saw that TensorBoard provides a tool that we can use to assess and compare model performance after training the network with different architectures and other changes in the model. The advantage of using TensorBoard lies in the fact that it brings all the necessary information together in one place in a user-friendly way. There are also situations where we want to understand how the main features or variables on a specific prediction are influenced when using a classification or prediction model. In such situations, we can visualize...