James Stewart's Calculus Chapter 3 — a concise summary of absolute extrema, the Mean Value Theorem, derivatives and the shape of graphs, optimization, Newton's Method, and antiderivatives.
A collection of concept exercises applying differentiation, loss function optimization, gradient descent, and backpropagation to real-world machine learning scenarios. Each problem is grounded in situations you commonly encounter when training models in practice.
A deep dive into the core of machine learning optimization: minimizing loss functions, differentiating squared and log loss, understanding partial derivatives and gradients, and using the gradient to find minima.
Build an intuitive understanding of derivatives — the engine behind ML optimization — and master the differentiation rules for constants, polynomials, exponentials, logarithms, and trig functions, plus scalar multiplication, sum, product, and chain rules.