Many aspects of modern applied research rely on a crucial algorithm called gradient descent. This is a procedure generally used for finding the largest or smallest values of a particular ...

Computer Scientists Discover Limits of Major Research Algorithm The most widely used technique for finding the largest or smallest values of a math function turns out to be a fundamentally difficult ...

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.

Unlike the metaphorical mountaineer, optimization researchers can program their gradient descent algorithms to take steps of any size. Giant leaps are tempting but also risky, as they could overshoot ...

Otherwise, it is easily optimized using gradient descent (see below). The assumption of linear regression is that the objective function is linearly correlated with the independent variables.

Dr. James McCaffrey of Microsoft Research explains stochastic gradient descent (SGD) neural network training, specifically implementing a bio-inspired optimization technique called differential ...

Modeled on the human brain, neural networks are one of the most common styles of machine learning. Get started with the basic design and concepts of artificial neural networks.