Assertion: gradient descent | /r/CapableOf | perform update
Facets:
value: on the parameters
type: OTHER
value: for each of these batches
type: TEMPORAL
value: for a batch of observations
type: PURPOSE
Source sentences:
text: Mini-batch gradient descent finally takes the best of both worlds and performs an update for every mini-batch of \(n\) training examples:
text: Note that stochastic gradient descent chooses a point from our data set uniformly at random, computes the gradient, and performs the update.
text: Standard gradient descent, also known as batch gradient descent, will calculate the gradient of the whole dataset but will perform only one update.
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: Mini-batch gradient descent performs an update for a batch of observations.
text: It simply separates the training set into small batches and performs an update for each of these batches.
text: It simply separates the training set into small batches and performs an update for each of these batches.
text: Batch Gradient Descent is a greedy approach, when we add all examples on each iteration when performing the updates to the parameters.
text: This means it only takes into account the first derivative when performing the updates on the parameters.
text: This means it only takes into account the first derivative when performing the updates on the parameters.
text: Batch Gradient Descent is a greedy approach, when we add all examples on each iteration when performing the updates to the parameters.
text: This means it only takes into account the first derivative when performing the updates on the parameters.