You construct a generalized linear model by deciding on response and explanatory variables for your data and choosing an appropriate link function and response probability distribution. Some examples ...

Long wires or distant wireless communications always garble the transmission. How can an engineer cure that distortion? Turns out, a lot of math and a little experience can work wonders. This author ...

Proper handling of continuous variables is crucial in healthcare research, for example, within regression modelling for descriptive, explanatory, or predictive purposes. However, inadequate methods ...

While I do spend lots of time pushing the idea of linear approximations on my students, we do need nonlinear functions to describe most interesting physical phenomena.

We demonstrate the implementation of a variety of non-linear models that are more appropriate for modeling plant growth than are the traditional, linear, models.Ecological inference is frequently ...

However, this cannot easily be achieved whilst retaining the conventional non-linear transfer functions, known as gamma curves, which were designed for CRT displays.

Christophe P. Basso has written a new book: Linear Circuit Transfer Functions—An Introduction to Fast Analytical Techniques.

For this, you also need non-linear thresholding functions that adjust the weights between the layers of neurons.

In this video, we will learn what is linear regression in machine learning along with examples to make the concept crystal clear.