This post is part of a tutorial series:

- Getting through Deep Learning – CNNs (part 1)
- Getting through Deep Learning – TensorFlow intro (part 2)
- Getting through Deep Learning – TensorFlow intro (part 3)

Alright, lets move on to more interesting stuff: linear regression. Since the main focus in TensorFlow, and given the abundancy of online resources on the subject, I’ll just assume you are familiar with Linear Regressions.

As previously mentioned, a linear regression has the following formula:

Where Y is the dependent variable, X is the independent variable, and **b0** and **b1** being the parameters we want to adjust.

Let us generate random data, and feed that random data into a linear function. Then, as opposed to using the closed-form solution, we use an iterative algorithm to progressively become closer to a minimal cost, in this case using gradient descent to fit a linear regression. Continue reading “Getting through Deep Learning – Tensorflow intro (part 3)”