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.
Hiring signal: Understanding of calculus for machine learning internals
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: Phase 1, Lessons 01-03 Time: ~60 minutes
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
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