Deep Learning with Yacine on MSN
How to implement linear regression in C++ step by step
Learn how to build a simple linear regression model in C++ using the least squares method. This step-by-step tutorial walks ...
Introduction: The learning process is characterized by its variability rather than linearity, as individuals differ in how they receive, process, and store information. In traditional learning, taking ...
Abstract: Mixed linear regression (MLR) models nonlinear data as a mixture of linear components. When noise is Gaussian, the Expectation-Maximization (EM) algorithm is commonly used for maximum ...
This C library provides efficient implementations of linear regression algorithms, including support for stochastic gradient descent (SGD) and data normalization techniques. It is designed for easy ...
Ever wondered how social media platforms decide how to fill our feeds? They use algorithms, of course, but how do these algorithms work? A series of corporate leaks over the past few years provides a ...
1 Shangwan Coal Mine, Ejin Horo Banner, Ordos, China 2 CCTEG Xi’an Research Institute Co. Ltd., Xi’an, China This study introduces an XGBoost-MICE (Multiple Imputation by Chained Equations) method for ...
Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demonstration of the AdaBoost.R2 algorithm for regression problems (where the goal is to predict a single numeric value). The ...
Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demonstration of k-nearest neighbors regression to predict a single numeric value. Compared to other machine learning ...
1 Tata Consultancy Services, Charlotte, NC, USA. 2 Mitaja Corportaion, Woodlawn, MD, USA. 3 Adobe, Seattle, WA, USA. 4 Microsoft, Charlotte, NC, USA. 5 Ally Financial ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results
Feedback