Kernel methods represent a cornerstone in modern machine learning, enabling algorithms to efficiently derive non-linear patterns by implicitly mapping data into high‐dimensional feature spaces.

Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demonstration of Nadaraya-Watson kernel regression using the C# language. NW kernel regression is simple to implement and is ...

More recently, the kernel method has been introduced in quantum machine learning with great success.

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 ...

The addition of heterogenous memory management to the Linux kernel will unlock new ways to speed up GPUs, and potentially other kinds of machine learning hardware ...