AI Practitioner

When training a deep learning model, you want it to   learn patterns   from the training data so it can make   accurate predictions on new, unseen data . However, sometimes models learn too little or too much. This leads to   underfitting   or   overfitting . Let’s break them down in simple terms, backed by examples, visuals, and some light math. 1. What Is the Goal of Training a Model? Imagine you're trying to teach a model to  predict house prices  based on features like size, location, and number of rooms. Your goal is to find a function  f(x)  that maps your input features  x  (like size, rooms) to a prediction  ŷ  (the house price), such that the prediction is  close to the actual price y . ŷ = f (x;θ) MSE = (1/n) ∑ (yᵢ - ŷᵢ) ² 2. Underfitting Underfitting happens when your model is  too simple  to capture the patterns in the data. It doesn’t learn enough from the training data and performs poorl...