# Step 2a - Compute the Loss

Learn about the difference between error and loss, the relationship between gradient descent and loss, and how the loss can be computed.

## Difference between error and loss

There is a subtle but fundamental difference between error and loss.

The error is the difference between the **actual value (label)** and the **predicted value** computed for a single data point. So, for a given *i*^{th} point (from our dataset of `N`

points), its error is:

$error_i = \hat{y_i} - y_i$

The error of the first point in our dataset (*i* = 0) can be represented like this:

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