Backpropagation, which is short for “backward propagation of errors,” is a method used to train artificial intelligence models, especially neural networks.
Imagine you’re trying to shoot a basketball into a hoop, but you miss. You’d probably adjust your aim or the strength of your throw for the next shot, based on how you missed – did you shoot too far? Too short? Too much to the left or right? By figuring out what you did wrong and adjusting, you can improve and get closer to making the shot. That’s a bit like backpropagation.
In a neural network, backpropagation is used to adjust the model’s parameters. First, the model makes a prediction and we measure how wrong that prediction was using a loss function. The loss function is like a score of how far off the model’s prediction was from the actual answer.
Then, backpropagation calculates how much each parameter in the model contributed to the error by calculating the gradient of the loss function with respect to each parameter. The gradient is a fancy math term that basically means the rate of change or slope.
Finally, the parameters are adjusted in the opposite direction of the gradient. If the gradient is positive, the parameter is decreased, and if the gradient is negative, the parameter is increased. This is done to minimize the loss function, or in other words, to make the model’s predictions more accurate.
Backpropagation is a fundamental part of training deep learning models and is used in many areas of artificial intelligence.
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