Multivariable Calculus explores functions of multiple variables, including partial derivatives, multiple integrals, and vector calculus.
In data science, computers learn from data by adjusting many variables to minimize error. This process uses multivariable calculus.
Gradient descent is an algorithm that uses gradients (from partial derivatives) to find the lowest point of a function—like the bottom of a valley.
Training neural networks involves optimizing functions with thousands of variables using these calculus tools.
Data science applications include voice recognition, self-driving cars, and personalized recommendations.
Tuning the weights in a neural network to improve handwriting recognition.
Optimizing marketing strategies by minimizing costs and maximizing impact.