Linear Regression, Machine Learning, Introduction, Gradient Descent.
we may miss the minimum. This can be formulated as Gradient descent: calculate iteratively the next value for the weight To summarize, we start with an arbitrary value w_1, when we use this w_1 for our model to approximate our data, we will probably have a quite large error. We then take a step of