Our data science expert continues his exploration of neural network programming, explaining how regularization addresses the problem of model overfitting, caused by network overtraining. Neural ...

Data analysis with neural networks often involves quite a bit of trial and error, including experimenting with different combinations of activation functions. There are many other activation functions ...

The field of artificial neural networks is extremely complicated and readily evolving. In order to understand neural networks and how they process information, it is critical to examine how these ...

Modeled on the human brain, neural networks are one of the most common styles of machine learning. Get started with the basic design and concepts of artificial neural networks. Artificial ...

We saw in the previous lecture that perceptrons have limited scope in the type of concepts they can learn - they can only learn linearly separable functions. However, we can think of constructing ...

A neural network is a graph of nodes called neurons. The neuron is the basic unit of computation. It receives inputs and processes them using a weight-per-input, bias-per-node, and final function ...

By replacing the step function with a continuous function ... By choosing the weights that minimize the total error, one can obtain the neural network that best solves the problem at hand.

Mohamad Hassoun, author of Fundamentals of Artificial Neural Networks (MIT Press, 1995) and a professor of electrical and computer engineering at Wayne State University, adapts an introductory ...