Unverified60% confidenceFactExact time
Modern deep learning frameworks use automatic differentiation to build a computation graph during forward propagation and automatically compute gradients in reverse along the graph during backpropagation.
2
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60%
Confidence
Long-term
Relevance
7/18/2026
First Seen
Sources
Related Claims
UnverifiedModern deep learning frameworks PyTorch and TensorFlow represent neural networks as computational graphs and compute gradients in reverse via automatic differentiation (Autograd).83% similarUnverified在深度学习中，雅可比矩阵的计算是反向传播算法的数学基础，描述梯度如何在网络层间流动80% similarUnverifiedBackpropagation is the core algorithm for deep learning training; it uses the chain rule to propagate the error from the output layer back through the network layer by layer, computing the gradient of each parameter with respect to the loss function.79% similarUnverifiedBackpropagation is the cornerstone of modern deep learning training, and virtually all large language model training is built on the gradient descent framework.75% similarUnverifiedCurrent mainstream deep learning architectures (including Transformers and convolutional neural networks) inherit the core assumptions of the information-processing paradigm: information can be encoded as numerical vectors, hierarchically transformed, and read from or written to explicit locations.74% similar
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