Deep Learning from First Principles
Deep Learning from First Principles Deep learning often feels like magic, but at its core it is a simple problem: learn a function from examples by minimizing error. If you start from the basics, you can see why models learn, where mistakes come from, and how to improve them without chasing every new trick. Understanding the objective In supervised learning, we choose a loss that measures how far the model’s predictions are from reality. The goal is to minimize the expected loss over the data you care about. In practice we estimate it with our dataset. Common choices include mean squared error for numbers and cross-entropy for categories. The exact function matters less than the idea: push predictions toward true values. ...