Ghost tweaks threads backward.
Backpropagation is the algorithm that trains neural networks by calculating how much each weight contributed to the error and adjusting them to reduce it.
Imagine you're trying to throw a paper ball into a trash can. Backpropagation is like having a friend tell you exactly how to adjust your throw—how much to change your arm angle, strength, and aim—based on how far you missed. It works backward from the miss to fix each part of your throw so you get closer next time.
Backpropagation is the backbone of modern AI, enabling everything from voice assistants to self-driving cars to learn from data. Without it, deep neural networks couldn't efficiently adjust millions of parameters, and most AI breakthroughs would be impossible.
Many think backpropagation is a separate learning algorithm, but it's just a method to compute gradients efficiently. It doesn't train the network by itself—it provides the direction for weight updates, which are then applied by an optimizer like gradient descent.
Backpropagation is a supervised learning algorithm that computes the gradient of the loss function with respect to each weight in a neural network by applying the chain rule from calculus. It propagates the error backward from the output layer to the input layer, allowing efficient gradient computation for gradient-based optimization methods.