"Learning representations by back-propagating errors"

"Learning representations by back-propagating errors" is a process used in neural networks where the system improves its performance by adjusting its internal settings. When the network makes a mistake, it calculates how far off it was, then sends that error information backward through its layers. This feedback helps the network tweak its internal parameters, making future predictions more accurate. Essentially, it's like a teacher guiding a student step-by-step on how to improve, using the mistakes as lessons to refine the network's understanding and decision-making over time.