Advanced Calculus for Machine Learning

Machine Learning

445 learners

Advanced calculus concepts are crucial for understanding optimization and gradients in machine learning, particularly in multivariable scenarios. This course builds upon basic calculus to cover multivariable functions, their derivatives, and numerical methods for gradients.

Python

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4 lessons

18 practices

2 hours

Mathematics and Statistics

Lessons and practices

Lesson 1: Exploring Second Derivative

Plotting a Function and Its Derivatives

Plot and Analyze the Derivatives

Visualizing Second Derivative for a Quadratic Function

Plotting Sigmoid Function and Its Derivatives

Lesson 2: Multivariable Functions

Define and Visualize a Multivariable Function

Plotting a Multivariable Sine-Cosine Function

Calculate the Area of a Rectangle Using Multivariable Function

Calculating Total Revenue with Multivariable Functions

Define and Plot a 2-Variable Function as a Contour

Visualize Two Variable Function with Contourf and 3D Plot

Lesson 3: Derivatives for Multivariable Functions

Calculating Partial Derivatives for a Given Function

Computing Partial Derivatives for a 3-Variable Function Using Finite Difference

Calculating Partial Derivatives in Stock Trading

Lesson 4: Understanding Gradient

Plot and Analyze Gradient Vector of a Given Function

Calculating and Plotting the Negative Gradient

Visualize and Compute Gradients of a Function at Multiple Points

Calculating Gradients at Multiple Points Near the Function's Maximum

Calculating Gradient at the Function's Maximum

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