For example, gradient descent is often used in machine learning in ways that don’t require extreme precision. But a machine learning researcher might want to double the precision of an experiment. In ...

Gradient descent algorithms take the loss function and use partial derivatives to determine what each variable (weights and biases) in the network contributed to the loss value.

Algorithm: Gradient Descent Siraj Raval: “I believe the gradient descent algorithm has had the biggest impact in artificial intelligence .” As always Siraj is right on the money.

Optimization methods for machine learning, including neural networks, typically use some form of gradient descent algorithm to drive the back propagation, often with a mechanism to help avoid ...

However, the gradient descent algorithms need to update variables one by one to calculate the loss function with each iteration, which leads to a large amount of computation and a long training time.

Algorithm classification. Gradient: Gradient-based means that the search logic is based on a model of the slope of the surface.Steepest descent, successive quadratic, and Newton-type methods are of ...

Ben Grimmer showed that gradient descent algorithms can work faster by including unexpectedly large step sizes — the opposite of what researchers long believed. Will Kirk “It turns out that we did not ...