Deep Learning Systems
The backward pass is a crucial phase in neural network training where the gradients of the loss function are computed with respect to the model's parameters. This process involves propagating the error backwards through the network, allowing for the adjustment of weights to minimize the loss. It is directly related to techniques such as backpropagation and automatic differentiation, which facilitate efficient computation of these gradients in complex models.
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