Using house price prediction as an analogy to clearly explain models, parameters, training, and inference

The article uses the house price prediction formula to explain that a model is essentially a mathematical function whose coefficients are learned from data; values learned such as 50, 0.2, and 25 are parameters, where weights are connection strengths and the bias is the constant term. The author distinguishes training from inference, and compares a small house price model, GPT-3's 175 billion parameters, and the VRAM usage of the Llama 2 70B version, pointing out that a model computes answers rather than looking them up like a database. The text also reminds that a model predicts patterns rather than facts, and that prediction quality depends on the quality and diversity of the training data.