Assertion: gradient descent | /r/HasProperty | an iterative method
Facets:
value: for minimising the cost function.cost function
type: PURPOSE
value: for solving “optimization problems”
type: PURPOSE
Source sentences:
text: Stochastic gradient descent (often abbreviated SGD) is an iterative method for optimizing an objective function with suitable smoothness properties (e.g. differentiable or subdifferentiable).
text: Gradient Descent is an iterative process that finds the minima of a function.
text: Gradient descent is an iterative algorithm which we will run many times.
text: Here gradient descent is an iterative technique for minimising the cost function.cost function is error in prediction(Y_Actual-Y_Predicted)
text: Gradient descent is an iterative method.
text: Gradient descent is an iterative method for solving “optimization problems”
text: Stochastic gradient descent (often shortened to SGD) is an iterative method for optimizing a differentiable objective function, a stochastic approximation of gradient descent optimization.
text: Gradient descent is an iterative method.
text: Gradient descent is an iterative method.
text: Since gradient descent is an iterative method, we also have to set manually the number of iterations.
text: Stochastic gradient descent is still an iterative process, identifying a direction and moving towards the minimum one step at a time.
text: Gradient Descent is an iterative process that finds the minima of a function.
text: After updating our weight matrix, we keep looping until the desired number of epochs has been met — gradient descent is thus an iterative algorithm.