Assertion: neural network's weight | /r/HasProperty | tuned
Facets:
value: after training
type: TEMPORAL
value: to the training examples
type: OTHER
value: correctly
type: MANNER
Source sentences:
text: Once you finish the parameter update on the last training example, hopefully the network should be trained well enough so that the weights of the layers are tuned correctly.
text: As the neural network “learns” the data, the weights, or strength, of the connections between these neurons are “fine-tuned,” allowing the network to come up with accurate predictions.
text: In the case of supervised training, weights are derived by feeding sample inputs and outputs to the algorithm until the weights are tuned (that is, there’s a near-perfect match between inputs and outputs).
text: When the algorithms are presented with new-but-similar inputs and the algorithm produces new outputs that are similar to previous outputs, then the neurons’ weights have been tuned.
text: The weights of the inputs of each layer are tuned to minimize a cost or loss function, which is a task in mathematical optimization and machine learning.
text: In the last section, we discussed the problem of overfitting, where after training, the weights of the network are so tuned to the training examples they are given that the network doesn’t perform well when given new examples.