Phase 1: Calculus & Optimization · ~60 minutes · Python
Calculus for Machine Learning
Derivatives tell you which way is downhill. That is all a neural network needs to learn.
What you will learn
- Compute numerical and analytical derivatives for common ML functions (x^2, sigmoid, cross-entropy)
- Implement gradient descent from scratch to minimize a loss function in 1D and 2D
- Derive the gradient of a linear regression model and train it via manual weight updates
- Explain the Hessian matrix, Taylor series approximations, and their connection to optimization methods
Introduction
- Type: Learn
- Language: Python
- Prerequisites: Earlier lessons in this course
- Time: ~60 minutes
Objective
Learning objectives
- Compute numerical and analytical derivatives for common ML functions (x^2, sigmoid, cross-entropy)
- Implement gradient descent from scratch to minimize a loss function in 1D and 2D
- Derive the gradient of a linear regression model and train it via manual weight updates
- Explain the Hessian matrix, Taylor series approximations, and their connection to optimization methods
Unlock the full lesson
You've read the first 2 sections. The rest of this lesson covers The Problem, The Concept, Build, Check Yourself, Key Terms & Next — plus a hands-on lab, quiz, and project artifact.
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