The purpose of this tutorial is to continue our exploration of regression by constructing linear models with two or more explanatory variables. This is an extension of Lesson 9. I will start with a ...

The purpose of this tutorial is to continue our exploration of multivariate statistics by conducting a simple (one explanatory variable) linear regression analysis. We will continue to use the ...

Get started with Machine Learning with Python - An introduction with Python programming examples - ml-MachineLearningWithPython/03 - Lesson - Linear Regression.ipynb at main · ...

The lesson data was chosen because it generates approximate linear relationships using real-world data. Define data modeling and how to apply a simple linear regression. Build a linear regression ...

A linear function approximator is a function y=f(x,w) that is linear in the weights, though not necessarily linear in the input x: Linear function approximators have several nice properties. For ...

Abstract: Deep Learning is attracting much attention in object recognition and speech processing. A benefit of using the deep learning is that it provides automatic pre-training. Several proposed ...

Often in economics a linear function cannot explain the relationship between variables. In such cases a non-linear function must be used. Non-linear means the graph is not a straight line. The graph ...

Abstract: The activation functions play increasingly important roles in deep convolutional neural networks. The traditional activation functions have some problems such as gradient disappearance, ...