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Calculus for Machine Learning
Calculus is the mathematics of change, and it's fundamental to how machine learning models 'learn'.
The most important concept is the 'derivative', which measures the instantaneous rate of change. Think of it as the slope of a curve at a single point. In machine learning, we use derivatives to find the 'error' of our model's prediction. The derivative tells us in which direction to adjust the model's parameters to reduce the error. This process is called 'gradient descent'.
An 'integral' is the reverse of a derivative. It's used to calculate the total area under a curve. In probability and statistics, which are central to AI, integrals help us find the probability of a range of outcomes.
While you don't always have to do calculus by hand (libraries do it for you), understanding what derivatives and integrals represent is key to understanding how AI works under the hood.
The most important concept is the 'derivative', which measures the instantaneous rate of change. Think of it as the slope of a curve at a single point. In machine learning, we use derivatives to find the 'error' of our model's prediction. The derivative tells us in which direction to adjust the model's parameters to reduce the error. This process is called 'gradient descent'.
An 'integral' is the reverse of a derivative. It's used to calculate the total area under a curve. In probability and statistics, which are central to AI, integrals help us find the probability of a range of outcomes.
While you don't always have to do calculus by hand (libraries do it for you), understanding what derivatives and integrals represent is key to understanding how AI works under the hood.