Unverified50% confidenceFactExact time
In backpropagation, the output layer error term formula is δ_out = (a_out - y) * σ'(z_out).
1
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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
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Related Claims
UnverifiedThe formula δ_out = (a_out - y) * σ'(z_out) in the text omits the constant factor 2, which in practice does not affect the direction of gradient descent and is merely equivalent to a scaling of the learning rate.75% similarUnverifiedUnder the total variation metric, the average error is Õ_p(n^(−β/(d_x+d_y+2β))).66% similarUnverifiedThe weight update formula in AdaBoost derives from the step-by-step minimization of the exponential loss function L=Σ_i e^(-y_i F(x_i)).63% similarVerifiedGradient descent updates parameters through the iterative formula θ = θ - α∇J(θ), where α is the learning rate and ∇J(θ) is the vector of partial derivatives of the loss function with respect to the parameters.62% similarUnverifiedWhen the quantile level τ=0.5, quantile loss degenerates to Mean Absolute Error (MAE).61% similar
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