AI & ML Master Playground

Evaluating Performance: The cost function acts as a measurement tool that evaluates how well the model is performing. By quantifying how far off predictions are from the actual values, it allows us to systematically improve the system.
Defining Model Parameters: The variables w and b are the parameters of your model—variables you adjust during training to improve fit. In technical discussions, these are often referred to as coefficients or weights .
Generating the Fit: Each unique choice for these parameters results in a different function, f(x). Visually, this generates a different line on the graph, allowing the model to adapt to the specific shape of your data.
The Goal of Learning: The ultimate objective is to find the specific combination of $w$ and $b$ that results in the smallest possible value for the cost function. This ensures the resulting line fits the training data as accurately as possible.

Edit these values. You can inject this data directly into the Loss Graph or the Dynamic Architecture playgrounds.