Find out why backpropagation and gradient descent are key to prediction in machine learning, then get started with training a simple neural network using gradient descent and Java code.

The algorithms often rely on variants of steepest descent for their optimizers, for example stochastic gradient descent (SGD), which is essentially steepest descent performed multiple times from ...

Understand what is Linear Regression Gradient Descent in Machine Learning and how it is used. Linear Regression Gradient Descent is an algorithm we use to minimize the cost function value, so as ...

To increase beyond that, you can use techniques like stochastic gradient descent and update the machine learning model in mini-batches. That is, you can load a small portion of the dataset into ...

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 ...

This quantum BLAS (qBLAS) translates into quantum speedups for a variety of data analysis and machine learning algorithms including linear algebra, least-squares fitting, gradient descent, Newton ...

Finally, the computational effort of the SPGD algorithm was compared with the traditional gradient algorithm, GA and PSO algorithm when the optical matrix scale is expanded to 10×10, 16×16, 32×32.

In particular, distributed stochastic gradient descent intensively invokes all-reduce operations for gradient update, which dominates communication time during iterative training epochs. In this work, ...