Articles

Huber Loss is a smooth and robust loss function that blends MSE and MAE to handle outliers effectively in regression tasks.

In statistics and machine learning, the bias–variance tradeoff describes the relationship between a model's complexity, the accuracy of its predictions, and how well it can make predictions on previously unseen data that were not used to train the model.

In machine learning, our goal is almost always to find the "best" set of parameters for a model a process we can visualize as searching for the lowest point in a vast, hilly landscape of error. The fundamental tool for this search is Gradient Descent, but the strategy for taking each "downhill step" can make the difference between getting stuck on a treacherous slope and efficiently reaching the bottom.