Linear Regression and Gradient Descent: From House Pricing to Deep Learning

This article uses house pricing as an example to explain linear regression and gradient descent algorithms in a clear and concise way. Linear regression predicts house prices by finding the best-fitting line, while gradient descent is an iterative algorithm used to find the optimal parameters that minimize the error function. The article compares absolute error and squared error, explaining why squared error is more effective in gradient descent because it ensures the smoothness of the error function, thus avoiding local optima. Finally, the article connects these concepts to deep learning, pointing out that the essence of deep learning is also to minimize error by adjusting parameters.