How neural networks learn — backpropagation and gradient descent Forward pass computes output. Loss measures error. Backward pass traces gradients through the chain rule. Weights update. The backward pass alone costs 2x the forward — and residual connections are gradient highways. What AI concept confuses you most? #AIExplained #DeepLearning #Backpropagation #NeuralNetworks #GradientDescent #TechTok
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