Unverified50% confidenceFactExact time
MSE is defined as L = (1/n) * Σ(a_out - y)^2; for a single sample the loss is (a_out - y)^2, and its gradient with respect to the output layer is 2(a_out - y) * σ'(z_out).
1
Sources
50%
Confidence
Long-term
Relevance
10/19/2026
First Seen
Sources
Building a Neural Network from Scratch with NumPy: A Hands-On Deep Dive into Backpropagation
redditr/deeplearning10/19/2026
Related Entities
Related Claims
UnverifiedMean squared error (MSE) is defined as the expectation of the square of the difference between the estimate and the true value, and can be decomposed into the sum of the square of the bias and the variance.69% similarUnverifiedQuantile loss is more robust to outliers than MSE and can implicitly characterize the asymmetry of prediction errors.66% similarUnverifiedRelative L2 error is defined as the L2 norm of the difference between the predicted solution and the true solution divided by the L2 norm of the true solution; interior MSE only counts the mean squared error in the interior region of the solution field (excluding the vicinity of the boundary).62% similarUnverifiedNumerical experiments show that the new approximate variance expressions match the empirical mean squared error (MSE) highly closely, with extremely small error especially when the set size is large.59% similarUnverifiedThe hidden layer error term formula is δ_hidden = (W_out^T · δ_out) * σ'(z_hidden).58% similar
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