Assertion: neural network's weight | /r/HasProperty | updated
Facets:
value: during training
type: TEMPORAL
value: during training of discrete hopfield network
type: TEMPORAL
Source sentences:
text: The weights are updated using a back-propogation algorithm which we’ll study next.
text: The weights are updated to minimize the error resulting from each neuron.
text: Finally, the weights will be updated using the following equations:
text: The amount that the weights are updated during training is referred to as the step size or the “learning rate.
text: This means that a learning rate of 0.1, a traditionally common default value, would mean that weights in the network are updated 0.1
text: Specifically, an exponentially weighted average of the prior updates to the weight can be included when the weights are updated.
text: For batch training all of the training samples pass through the learning algorithm simultaneously in one epoch before weights are updated.
text: The amount that the weights are updated during training is referred to as the step size or the “learning rate.
text: During training of discrete Hopfield network, weights will be updated.