We’ve talked about how neural networks make predictions through forward propagation, and how gradient descent helps them learn by adjusting weights. But we’ve been glossing over a crucial detail – how does the network actually know which weights to adjust and by how much? That’s where backpropagation comes in – one of the most elegant algorithms in computer science and the secret sauce that makes deep learning possible. by Christopher Miller