Backpropagation
Backpropagation
A mechanism where AI works backward from the prediction-answer gap to calculate each parameter's gradient.
In Simple Terms
Backpropagation is a technique that traces the gap between an AI's prediction and the correct answer backward to calculate the gradient (the direction and degree of steepest error increase) for each parameter. It's widely used in the training process of deep learning models for tasks like image recognition and text generation. Working backward from the output, it calculates each parameter's gradient based on the error, then an optimizer shifts each parameter in the opposite direction of that gradient—efficiently improving the AI's accuracy.
Behind the Name
The name combines "back," meaning backward, and "propagation," meaning the spreading or transmitting of something. It's named this way because the technique passes the gap between the AI's output and the correct answer backward, from the output side toward the input side, to calculate how much each part needs to be adjusted.
Take a Closer Look!
Backpropagation is an algorithm that, during AI training, works backward to calculate the gradient (the direction and degree of steepest error increase) for each parameter, based on the gap between the AI's prediction and the correct answer.
It serves as the foundation for figuring out how to adjust each parameter to reduce error during AI training.
The neural networks used in deep learning stack many layers of calculations to arrive at an answer.
Roughly speaking, the process first sends input data forward to produce an answer, then propagates the resulting error backward, from the final output side toward the initial input side.
This backward calculation process determines the gradient for each parameter within the intricately connected network.
Since the gradient points in the direction where the error increases fastest, actually updating the parameters means moving them in the opposite direction of that gradient to reduce the error. This update process is handled by an optimization algorithm (optimizer).
Because there's no need to check each parameter one by one by hand, gradients can be found smoothly even when training large models.
That's why it's used as an essential core technology across a wide range of deep learning model development, including image recognition and natural language processing.