Reviewing the Steps of Gradient Descent
Explore the fundamental steps of gradient descent applied to a simple linear regression problem using PyTorch. Understand how to generate synthetic data, compute predictions, calculate mean squared error loss, find gradients with respect to parameters, and update those parameters iteratively. This lesson outlines batch, stochastic, and mini-batch gradient descent types and shows how repeated epochs train the model.
We'll cover the following...
Simple linear regression
Most tutorials start with some nice and pretty image classification problems to illustrate how to use PyTorch. It may seem cool, but I believe it distracts you from learning how PyTorch works.
For this reason, in this first example, we will stick with a simple and familiar problem: a linear regression with a single feature x! It does not get much simpler than that. It has the following equation:
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