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In the equation y = mx + b, the variable m represents the slope or gradient of the line, and it can be viewed as a weight. Here, y is the predicted output (or dependent variable);  x is the input feature (or independent variable);  m is the weight associated with the input feature x; and b is the y-intercept, representing the point where the line crosses the y-axis. The weight m captures the amount of change we've observed in our label in response to a small change in our feature.

The learning rate is a hyperparameter that determines the size of the step that is taken during the optimization process in weight space. Weight space refers to the space of possible values for the parameters (weights) of a machine learning model.

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