Assertion: gradient descent | /r/CapableOf | compute the gradient
Facets:
value: for each update
type: TEMPORAL
value: for each training example
type: OTHER
value: for similar examples
type: PURPOSE
Source sentences:
text: Stochastic gradient descent (SGD) computes the gradient using a single sample.
text: Mini Batch Gradient Descent computes the gradient based on randomly selected data within the training set just like SGD but does not include the entire dataset when computing gradients, so
text: Batch gradient descent performs redundant computations for large datasets, as it recomputes gradients for similar examples before each parameter update.
text: Stochastic gradient descent (SGD) computes the gradient for each update using a single training data point x_i (chosen at random).
text: Batch gradient descent (BGD) computes the gradient using the whole dataset.
text: Stochastic gradient descent (SGD) computes the gradient using a single sample.
text: Unlike the batch gradient descent which computes the gradient using the whole dataset, because the SGD, also known as incremental gradient descent, tries to find minimums or maximums by iteration from a single randomly picked training example, the error is typically noisier than in gradient descent.
text: Stochastic gradient descent (SGD) computes the gradient for each update using a single training data point x_i (chosen at random).
text: Mini Batch Gradient Descent computes the gradient based on randomly selected data within the training set just like SGD but does not include the entire dataset when computing gradients, so
text: Stochastic gradient descent (SGD) computes the gradient for each training example x[i]. i.e. a single training data point is used for each update.
text: Note that stochastic gradient descent chooses a point from our data set uniformly at random, computes the gradient, and performs the update.