Linear regression may be one of the most important algorithms in statistics, machine learning, and science in general. It's one of the most widely used algorithms, and it is very important to understand how to implement it and its various flavors. One of the advantages that linear regression has over many other algorithms is that it is very interpretable. We end up with a number for each feature that directly represents how that feature influences the target or dependent variable. In this chapter, we will introduce how linear regression is classically implemented, and then move on to how to best implement it in the TensorFlow paradigm.
Remember that all the code is available on GitHub at https://github.com/nfmcclure/tensorflow_cookbook ...
