Dr. James McCaffrey presents a complete end-to-end demonstration of linear regression using pseudo-inverse training. Compared to other training techniques, such as stochastic gradient descent, ...
Dr. James McCaffrey presents a complete end-to-end demonstration of decision tree regression from scratch using the C# language. The goal of decision tree regression is to predict a single numeric ...
In a recent write-up, [David Delony] explains how he built a Wolfram Mathematica-like engine with Python. Core to the system is SymPy for symbolic math support. [David] said being able to work with ...
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 ...
Abstract: The purpose of this work is to improve the detection of fraud websites using Novel Linear Regression Algorithm and Recurrent Neural Network Algorithm. Materials and Methods: Novel Linear ...
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 ...
ABSTRACT: Burundi faces major agricultural constraints, including land fragmentation, soil erosion, limited access to inputs, inadequate infrastructure and demographic pressures that exacerbate food ...
In this excellent tutorial video presentation below, Magnus Erik Hvass Pedersen demonstrates the basic workflow of using TensorFlow with a simple linear model. After loading the so-called MNIST ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results