Here's a practical example and hopefully easy to understand example to illustrate this:

Imagine you're trying to predict the height of a person based on their weight. You might start by creating an equation that takes the weight of a person (x) and multiplies it by a number (w) to give you the estimated height (y). But, this equation might not always work correctly for everyone. For example, it might overestimate the height for lighter people and underestimate the height for heavier people.

In this case, the bias term acts like a tweak to the equation to help it make better predictions. If the equation is overestimating the height for lighter people, you can adjust the bias term to bring down the predicted height, and vice versa for heavier people.

So, the bias term helps the artificial neuron to make better predictions by providing a way to fine-tune the results based on the specific problem.
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