A paper titled this algorithm "through Time" by Paul Werbos lays the theoretical groundwork for recurrent models that repeat parameters.  In pytorch, setting requires_grad to False means that this is not applied to a parameter.  This can fail because of either "vanishing" or "exploding" gradients in particularly large neural networks, as it uses the chain rule to calculate the gradient of the loss at each layer, which in turn is used to compute how much and in what direction layer weights should be tuned. For 10 points, name this algorithm that is the basis for most forms of neural network training named for the direction that error moves through a model.
A paper titled this algorithm "through Time" by Paul Werbos lays the theoretical groundwork for recurrent models that repeat parameters.  In pytorch, setting requires_grad to False means that this is not applied to a parameter.  This can fail because of either "vanishing" or "exploding" gradients in particularly large neural networks, as it uses the chain rule to calculate the gradient of the loss at each layer, which in turn is used to compute how much and in what direction layer weights should be tuned. For 10 points, name this algorithm that is the basis for most forms of neural network training named for the direction that error moves through a model.
A paper titled this algorithm "through Time" by Paul Werbos lays the theoretical groundwork for recurrent models that repeat parameters.  