Dozens of machine learning algorithms require computing the inverse of a matrix. Computing a matrix inverse is conceptually easy, but implementation is one of the most difficult tasks in numerical ...

Dr. James McCaffrey of Microsoft Research guides you through a full-code, step-by-step tutorial on "one of the most important operations in machine learning." Computing the inverse of a matrix is one ...

Dr. James McCaffrey of Microsoft Research presents a full-code, step-by-step tutorial on an implementation of the technique that emphasizes simplicity and ease-of-modification over robustness and ...

Complex matrix algebra is of great importance to a wide variety of applications. One of the most important application areas is telecommunications. Matrix calculations are used in communications ...

For high data rate wireless communications they use Orthogonal Frequency Division Multiplexing (OFDM) due to its high spectral efficiency and low computational complexity. It gives the architecture of ...

With AlphaTensor, DeepMind Technologies has presented an AI system that is supposed to independently find novel, efficient and provably correct algorithms for complex mathematical tasks. AlphaTensor ...

The correlation matrix (denoted by R) plays an important role in many statistical models. Unfortunately, sampling the correlation matrix in Markov chain Monte Carlo (MCMC) algorithms can be ...