Backpropagation is the method that allows neural networks to learn. First, the network makes a prediction in the forward pass, then it compares the output to the true answer and calculates the error. That error is pushed backward through the layers, telling each weight how much it contributed to the mistake. Using these gradients, the network adjusts step by step until predictions improve. It’s like structured trial and error, repeated thousands of times.
A breakthrough example of backprop in action was AlexNet in 2012, created by Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton. They trained a massive convolutional neural network on GPUs, using backpropagation to handle millions of parameters. AlexNet crushed the ImageNet competition and proved that, with enough data and compute, backprop could unlock real-world breakthroughs in image recognition. This moment put deep learning at the center of AI.
In my endurance training work, I see a similar process. When I guide athletes, I give them a plan, they try it in training, and then we review the result. If their pacing is off, or recovery feels wrong, I adjust the plan, just like backprop adjusts weights. Over time, the repeated cycle of training, feedback, and correction makes them stronger and faster. Backprop is essentially what I do in my work: refine through feedback until performance improves.
“He who has a why to live can bear almost any how.”
Friedrich Nietzsche
Like backpropagation and training runs, progress takes time, each adjustment, each step bringing us closer to mastery.
OGRap 