Assertion: neural network's weight | /r/HasProperty | adjusted
Facets:
value: each time
type: TEMPORAL
value: during training
type: TEMPORAL
value: continually
type: TEMPORAL
value: until training data
type: TEMPORAL
value: barely
type: DEGREE
value: due to extremely small gradients
type: CAUSE
value: accordingly
type: MANNER
value: gradually
type: DEGREE
Source sentences:
text: During training, the weights and thresholds are continually adjusted until training data with the same labels consistently yield similar outputs.
text: Suppose that the new weights associated with the node in consideration are w4, w5 and w6 (after Backpropagation and adjusting weights).
text: In this particular case, the slope we care about describes the relationship between the network’s error and a single weight; i.e. that is, how does the error vary as the weight is adjusted.
text: Once the output is generated from the final neural net layer, loss function (input vs output)is calculated and backpropagation is performed where the weights are adjusted to make the loss minimum.
text: Therefore, as the model is being trained, the weights of the interconnections of the neurons will gradually be adjusted until good predictions are obtained.
text: A key feature of neural networks is an iterative learning process in which records (rows) are presented to the network one at a time, and the weights associated with the input values are adjusted each time.
text: If this output is different from desired output, the weight is adjusted again and this process continuous till getting the desired output.
text: This error is noted and the weights are “adjusted” accordingly.
text: If we now input the same example to the network again, the network should perform better than before since the weights have now been adjusted to minimize the error in prediction.
text: A key feature of neural networks is an iterative learning process in which data cases (rows) are presented to the network one at a time, and the weights associated with the input values are adjusted each time.
text: If the perceptron uses an activation function other than the step function (e.g. the sigmoid function), then the weights and bias should be adjusted so that the output o
text: The weights are adjusted so as to minimize the cost function by back-propagating the errors through the layers.
text: The earlier layers fail to do any learning as the internal weights are barely being adjusted due to extremely small gradients.
text: During training, the weights and thresholds are continually adjusted until training data with the same labels consistently yield similar outputs.