GitHub

Learn machine learning by building all the fundamentals from scratch - with Python

Learn machine learning from the ground up - using Python and a handful of fundamental tools. This repository contains a range of resources associated with the 2nd edition of the university textbook ...
Visual Studio Magazine

Kernel Ridge Regression with Stochastic Gradient Descent Training Using JavaScript

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 ...
Visual Studio Magazine

Kernel Ridge Regression with Stochastic Gradient Descent Training Using C#

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 ...
Frontiers

Expectation-maximization alternating least squares for tensor network logistic regression

In recent years, a learning method for classifiers using tensor networks (TNs) has attracted attention. When constructing a classification function for high-dimensional data using a basis function ...
Scientific Research Publishing

Stock Type Prediction Based on Multiple Machine Learning Methods ()

Stocks in the Chinese stock market can be divided into ST stocks and normal stocks, so to prevent investors from buying potential ST stocks, this paper first performs SMOTEENN oversampling data ...
PNAS

The inverse variance–flatness relation in stochastic gradient descent is critical for ...

One key ingredient in deep learning is the stochastic gradient descent (SGD) algorithm, which allows neural nets to find generalizable solutions at flat minima of the high-dimensional loss function.