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.
Plotting a Function and Its Derivatives
Plot and Analyze the Derivatives
Visualizing Second Derivative for a Quadratic Function
Plotting Sigmoid Function and Its Derivatives
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
Calculating Partial Derivatives for a Given Function
Computing Partial Derivatives for a 3-Variable Function Using Finite Difference
Calculating Partial Derivatives in Stock Trading
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