In this chapter, we have seen examples of the use of TensorFlow for two situations involving linear regression; where features are mapped to known labels that have continuous values, thus allowing predictions on unseen features to be made. We have also seen an example of logistic regression, better described as classification, where features are mapped to categorical labels, again allowing predictions on unseen features to be made. Finally, we looked at the KNN algorithm for classification.
We will now move on, in Chapter 5, Unsupervised Learning Using TensorFlow 2, to unsupervised learning, where there is no initial mapping between features and labels, and the task of TensorFlow is to discover relationships between the features.