Gentlest Introduction to Tensorflow (Part 2)

Summary: We show in illustrations how the machine learning'training' process happens in Tensorflow, and tie them back to the Tensorflow code. This paves the way for discussing'training' variations, namely stochastic/mini-batch/batch, and adaptive learning rate gradient descent. The'training' variation code snippets presented serve to reinforce the understanding of the role of Tensorflow placeholders. In the previous article, we used Tensorflow (TF) to build and learn a linear regression model with a single feature so that given a feature value (house size/sqm), we can predict the outcome (house price/). In machine learning (ML) literature, we come across the term'training' very often, let us literally look at what that means in TF.