Gentlest Intro to TensorFlow #3: Matrices & Multi-feature Linear Regression – All of us are belong to machines

Summary: With concepts of single-feature linear-regression, cost function, gradient descent (from Part 1), epoch, learn-rate, gradient descent variation (from Part 2) under our belt, we are ready to progress to multi-feature linear regression with TensorFlow (TF). If you are already familiar with matrices and multi-feature linear regression, skip to the end for the multi-feature Tensorflow code cheatsheet, or even skip this entire article. The premise of the previous articles was: given any house size (square meters/sqm), which is the feature, we want to predict the house price (), the outcome. In reality, any prediction relies on multiple features, so we advance from single-feature to 2-feature linear regression; we chose 2 features to keep visualization and comprehension simple, but the concept generalizes to any number of features. We introduce a new feature, 'Rooms' (number of units in the house).