Assertion: gradient descent | /r/CapableOf | calculate the error
Facets:
value: for each example
type: OTHER
value: in the training dataset
type: LOCATION
value: within the training set
type: OTHER
value: basically
type: DEGREE
value: for each sample in the data
type: OTHER
value: for each observation in the dataset
type: OTHER
value: sequentially
type: MANNER
value: at each point
type: TEMPORAL
value: for each record
type: OTHER
Source sentences:
text: Batch gradient descent, also called vanilla gradient descent, calculates the error for each example within the training dataset, but only after all training examples have been evaluated does the model get updated.
text: Batch gradient descent (BGD): calculate the error for each example in the training dataset, but only updates the model after all training examples have been evaluated.
text: Stochastic gradient descent (SGD) : calculate the error and updates the model for each examplein the training dataset.
text: It calculates the error for each record and takes an average to determine the gradient.
text: By using that assumption, stochastic gradient descent is able to calculate the error at each point sequentially.
text: Batch Gradient Descent, aka Vanilla gradient descent, calculates the error for each observation in the dataset but performs an update only after all observations have been evaluated.
text: It basically calculates the error for each sample in the data and updates the model only after the training example has been calculated.
text: It calculates the error for each example within the training set.
text: It calculates the error for each example within the training set.