error backpropagation

Error backpropagation is a method used by neural networks to improve their accuracy by learning from mistakes. When the network makes a prediction, it compares it to the correct answer and calculates an error. This error then travels backwards through the network, adjusting the connections (or weights) between nodes. These adjustments minimize future errors by fine-tuning how the network processes information. Essentially, it's a systematic way for the model to learn from its errors, improving its ability to make correct predictions over time through continuous feedback and update.