To develop speaker adaptation algorithms for deep neural network (DNN) that are suitable for large-scale online deployment, it is desirable that the adaptation model be represented in a compact form ...

This project implements full-batch gradient descent (FBGD) for linear regression, comparing CPU serial and GPU implementations. The assignment demonstrates: assignment-5-linear-regression/ ├── ...

The goal of a machine learning regression problem is to predict a single numeric value. For example, you might want to predict an employee's bank account balance based on age, height, annual income, ...

In forecasting economic time series, statistical models often need to be complemented with a process to impose various constraints in a smooth manner. Systematically imposing constraints and retaining ...

ABSTRACT: Nowadays, understanding and predicting revenue trends is highly competitive, in the food and beverage industry. It can be difficult to determine which aspects of everyday operations have the ...

What is linear regression in machine learning ? Understanding Linear Regression in machine learning is considered as the basis or foundation in machine learning. In this video, we will learn what is ...

In the stock market, there may be a linear relationship between the stock price on the current day and stock prices in several previous trading days. According to this concept, we design the following ...

ProcessOptimizer is a Python package designed to provide easy access to advanced machine learning techniques, specifically Bayesian optimization using, e.g., Gaussian processes. Aimed at ...