Summary
In this chapter, we've explored linear regression and gradient descent. Linear regression is a simple parametric model. It makes a certain assumption about data shape and error distribution. We were also acquainted with the Accelerate framework, a powerful hardware-accelerated framework from Apple for numerical computations.
In the next chapter, we'll continue by building different, more complex models on top of linear regression: polynomial regression, regularized regression, and logistic regression.