Before we get into classic machine learning, neural networks, and language models, this chapter takes a different shape: no history, no philosophy, just the minimum toolkit of math for AI you need for the rest of the series to actually click. No heavy formulas, no exams, no flashbacks to university. Just the intuition behind three […]
READ MOREThree different ways to learn: with correct answers, without them, or by trial and error. Three ways to learn In 1959, IBM engineer Arthur Samuel published a paper with a title that sounds obvious today and was a complete novelty back then: “Some Studies in Machine Learning Using the Game of Checkers.” That’s where the […]
READ MOREFour algorithms that have been working for decades without a single neural network. The toolbox Chapter 5 laid out the three schools of machine learning, supervised, unsupervised and reinforcement. Those were three ways of framing a problem, not three ways of solving one. This chapter goes down a floor into the engine room, the classic […]
READ MORETraining is not feeding data to a machine and hoping. It is a process with rules. What training actually means Chapter 6 left four algorithms on the table and one phrase repeated without ever being unpacked, “it gets trained on the data”. Time to open that box. Training a model is the process by which […]
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