In the last post, I wrote about calculating the cost of linear regression learning models combined with using gradient descent to find the minimized cost. Quick review of the key equations. Hypothesis: $$h_\theta(x) = \theta_0 + \theta_{1}x$$ Parameters: $$\theta_0, \theta_1$$ Cost Function: $$J(\theta_0,\theta_1) = \frac{1}{2m} \sum_{i=1}^m(h_\theta(x^{(i)}) - y^{(i)})^2$$ Goal: $$\underset{\rm \theta_0,\theta_1}{\rm minimize}$$ $$J(\theta_0, \theta_1)$$ With these tools, we can perform a gradient descent, an optimization algorithm designed to find $$\underset{\rm \theta_0,\theta_1}{\rm minimize}$$ $$J(\theta_0, \theta_1)$$. [Read More]