Unverified50% confidenceFactExact time
The 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.
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50%
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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
UnverifiedIn backpropagation, the output layer error term formula is δ_out = (a_out - y) * σ'(z_out).75% 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.68% similarUnverifiedRidge regression shrinks regression coefficients toward zero but does not set them to zero by adding an L2 regularization term to the loss function, whereas LASSO (L1 regularization) produces sparse solutions.65% similarUnverified测试时计算扩展的边际收益并非线性，存在明显的收益递减区间63% similarUnverifiedIn practice, almost no one uses raw batch gradient descent; instead, stochastic gradient descent (SGD) or mini-batch gradient descent (Mini-batch SGD) is adopted63% similar
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