Chapter 7. Visualizing data and models
This chapter covers
- How to use tfjs-vis to perform custom data visualization
- How to peek at the internal workings of models after they are trained and gain useful insights
Visualization is an important skill for machine-learning practitioners because it is involved in every phase of the machine-learning workflow. Before we build models, we examine our data by visualizing it; during model engineering and training, we monitor the training process through visualization; after the model is trained, we use visualization to get a sense about how it works.
In chapter 6, you learned the benefits of visualizing and understanding data before applying machine learning on it. We described how to use Facets, a browser-based tool that helps you get a quick, interactive look at your data. In this chapter, we will introduce a new tool, tfjs-vis, which helps you visualize your data in custom, programmatic ways. The benefit of doing so, versus just looking at the data in its raw format or using off-the-shelf tools such as Facets, is the more flexible and versatile visualization paradigm and the deeper understanding of data that it leads to.