Episode 34 — Calculus for ML: Derivatives as “Slope,” Partial Derivatives, and the Chain Rule
This episode introduces calculus concepts as intuitive tools for understanding learning and optimization, focusing on meaning rather than computation, which aligns with how DataX frames these ideas. You will define a derivative as a measure of how output changes when an input changes, and you’ll connect this to the idea of “slope” on a loss surface that tells an algorithm which direction reduces error. We’ll introduce partial derivatives as focusing on one parameter at a time while holding others fixed, which mirrors how multi-parameter models are tuned. The chain rule will be explained as linking simple changes through layers of computation, which is foundational for understanding how complex models adjust internal parameters. You will practice mapping scenario language like “gradient,” “optimization,” or “backpropagation” to these core ideas without relying on formulas. Troubleshooting considerations include recognizing when gradients vanish or explode conceptually, and why scaling, initialization, and architecture choices matter for stable learning. Real-world framing includes understanding why optimization may stall, why learning rates matter, and why some models train faster or more reliably than others. By the end, you will be able to reason about learning behavior in exam questions and explain optimization in clear, non-mathematical language. Produced by BareMetalCyber.com, where you’ll find more cyber audio courses, books, and information to strengthen your educational path. Also, if you want to stay up to date with the latest news, visit DailyCyber.News for a newsletter you can use, and a daily podcast you can commute with.
