The Gentlest Introduction to Tensorflow – Part 1

We are going to solve an overly simple, and unrealistic problem, which has the upside of making understanding the concepts of ML and TF easy. We want to predict a single scalar outcome, house price (in) based on a single feature, house size (in square meters, sqm). This eradicates the need to handle multi-dimensional data, enabling us to focus solely on defining a model, implementing, and training it in TF. We start with a set of data points that we have collected (chart below), each representing the relationship between two values --an outcome (house price) and the influencing feature (house size). However, we cannot predict values for features that we don't have data points for (chart below) We can use ML to discover the relationship (the'best-fit prediction line' in the chart below), such that given a feature value that is not part of the data points, we can predict the outcome accurately (the intersection between the feature value and the prediction line.