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Module 5
Calculus for Machine Learning
1
Derivatives: Rates of Change & Tangent Lines
Explain the concept of a derivative as an instantaneous rate of change and its geometric interpretation as slope
2
Derivatives of Common Functions
Compute derivatives of polynomial, exponential, and logarithmic functions
3
Mastering Derivative Rules: Product, Quotient, and Chain
Apply the product, quotient, and chain rules to compute derivatives
4
Partial Derivatives of Multivariable Functions
Compute partial derivatives of multivariable functions
5
Gradient Vectors and Steepest Ascent
Explain the concept of a gradient vector and its relation to the direction of steepest ascent
6
Gradient Descent: A Manual Walkthrough
Apply gradient descent to find the minimum of a simple function on paper
7
Gradient Descent for Single-Variable Functions in Python
Implement gradient descent in Python for a single-variable function
Previous module
Linear Algebra for Machine Learning
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Probability and Statistics for AI