Gradient Descent finds the minima of cost function, by using the derivative of the cost function w.r.t parameters. Without applying Gradient Descent, we cannot train any model in Machine Learning.

Dr. James McCaffrey presents a complete end-to-end demonstration of the kernel ridge regression technique to predict a single numeric value. The demo uses stochastic gradient descent, one of two ...

Dr. James McCaffrey from Microsoft Research presents a complete end-to-end program that explains how to perform binary classification (predicting a variable with two possible discrete values) using ...

In the '80s, navigating that gradient was derided by MIT scientist Marvin Minsky as mere "hill climbing." (The inverse of gradient descent is like ascending to a summit of highest accuracy.) ...

Function estimation/approximation is viewed from the perspective of numerical optimization in function space, rather than parameter space. A connection is made between stagewise additive expansions ...

S. Manthira Moorthi, D. Dhar, R. Sivakumar, Co-registration of LISS-4 multispectral band data using mutual information-based stochastic gradient descent optimization, Current Science, Vol. 113, No. 5 ...

Summary Researchers developed an AI-powered protein design method using Alphafold2 and gradient descent optimization. The approach allows precise tailoring of large proteins with desired properties, ...