This chapter is from the book
This chapter is from the book
This chapter is from the book
Summary
AI is based on a complex network of learning methods, model structures, and data strategies. This chapter explores how machines actually learn from mapping inputs to known outcomes in supervised learning, discovering hidden patterns through unsupervised methods, and refining behavior via feedback in reinforcement learning. These three learning families are essential to understanding what kind of intelligence a model is aiming to build.
This chapter also examines how a model works by looking at what a model really is: not just a code block or an equation but a structured representation of relationships that learns through training. Parameters that are tuned to minimize prediction error define what the model learns; hyperparameters, in contrast, define how the model learns. We have explored how training data is split, how loss is measured, and how models are evaluated for generalization.
Understanding this machinery is essential as we start to explore the algorithms behind the general processes. In the next chapter, you will see how the underlying algorithms of AI operate, when to use them, and how to judge their performance in real-world applications.
