Unverified50% confidenceFactExact time
Mainstream deep learning frameworks typically use reverse-mode autodiff, that is, building a computation graph during the forward pass and then traversing it in reverse to apply the chain rule.
1
Sources
50%
Confidence
Long-term
Relevance
11/3/2026
First Seen
Sources
Related Entities
Related Claims
Unverified自动微分的前向模式适合输入少输出多的场景,反向模式适合输入多输出少的场景,深度学习因参数多而损失为标量属于后者72% similarUnverifiedThe core algorithms of machine learning rely deeply on mathematical intuition: the gradient descent algorithm relies on partial derivatives to determine the direction of parameter updates, and backpropagation is essentially a systematic application of the chain rule70% similarUnverifiedIt is recommended that machine learning learners hand-code the backpropagation algorithm (the chain rule expansion of automatic differentiation) at least once67% similarUnverifiedCertain specific phrasings in the original prompt activate shortcuts entrenched during model training, causing the model to apply fixed patterns in its responses; after paraphrasing, the model is forced to perform more general reasoning, thereby improving output quality.66% similarUnverified微积分中的链式法则是深度学习反向传播算法的数学基础66% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1130031API
curl https://kongchang.com/api/v1/knowledge/claims/1130031MCP
get_claim(id=1130031)