Neural Network Lab


Use Python with Your Neural Networks

A neural network implementation can be a nice addition to a Python programmer's skill set. If you're new to Python, examining a neural network implementation is a great way to learn the language.

Neural Network Training Using Simplex Optimization

Simplex optimization is one of the simplest algorithms available to train a neural network. Understanding how simplex optimization works, and how it compares to the more commonly used back-propagation algorithm, can be a valuable addition to your machine learning skill set.

Creating Neural Networks Using Azure Machine Learning Studio

Dr. McCaffrey walks you through how to use the Microsoft Azure Machine Learning Studio, a new front-end for Microsoft Azure Machine Learning, to get a neural prediction system up and running.

Understanding Neural Network Batch Training: A Tutorial

There are two different techniques for training a neural network: batch and online. Understanding their similarities and differences is important in order to be able to create accurate prediction systems.

Neural Network Weight Decay and Restriction

Weight decay and weight restriction are two closely related, optional techniques that can be used when training a neural network. This article explains exactly what weight decay and weight restriction are, and how to use them with an existing neural network application or implement them in a custom application.

Deep Neural Networks: A Getting Started Tutorial

Deep Neural Networks are the more computationally powerful cousins to regular neural networks. Learn exactly what DNNs are and why they are the hottest topic in machine learning research.

Neural Network Dropout Training

Dropout training is a relatively new algorithm which appears to be highly effective for improving the quality of neural network predictions. It's not yet widely implemented in neural network API libraries. Learn how to use dropout training if it's available in an existing system, or add dropout training to systems where it's not yet available.

Neural Network Cross Entropy Error

To train a neural network you need some measure of error between computed outputs and the desired target outputs of the training data. The most common measure of error is called mean squared error. However, there are some research results that suggest using a different measure, called cross entropy error, is sometimes preferable to using mean squared error.

Neural Network How-To: Code an Evolutionary Optimization Solution

Evolutionary optimization can be used to train a neural network. A virtual chromosome holds the neural network's weights and bias values, and the error term is the average of all errors between the network's computed outputs and the training data target outputs. Learn how to code the solution.

Learning to Use Genetic Algorithms and Evolutionary Optimization

Evolutionary optimization (EO) is a type of genetic algorithm that can help minimize the error between computed output values and training data target output values. Use this demo program to learn to the method.

How To Standardize Data for Neural Networks

Understanding data encoding and normalization is an absolutely essential skill when working with neural networks. James McCaffrey walks you through what you need to know to get started.

Neural Network Training Using Particle Swarm Optimization

Although mathematically elegant, back-propagation isn't perfect. Instead consider using particle swarm optimization (PSO) to train your neural network; here's how.

Particle Swarm Optimization Using C#

Particle swarm optimization isn't usually seen as the first-choice technique for training a neural network but, as James McCaffrey demonstrates, it's a useful alternative.

Understanding and Using K-Fold Cross-Validation for Neural Networks

James McCaffrey walks you through whys and hows of using k-fold cross-validation to gauge the quality of your neural network values.

Neural Network Training Using Back-Propagation

James McCaffrey explains the common neural network training technique known as the back-propagation algorithm.

Neural Network Back-Propagation Using C#

Understanding how back-propagation works will enable you to use neural network tools more effectively.

Neural Network Data Normalization and Encoding

James McCaffrey explains how to normalize and encode neural network data from a developer's point of view.

Neural Network Activation Functions in C#

James McCaffrey explains what neural network activation functions are and why they're necessary, and explores three common activation functions.

The Neural Network Input-Process-Output Mechanism

Understanding the feed-forward mechanism is required in order to create a neural network that solves difficult practical problems such as predicting the result of a football game or the movement of a stock price.

Classification Using Perceptrons

Learn how to create a perceptron that can categorize inputs consisting of two numeric values.

Modeling Neuron Behavior in C#

James McCaffrey presents one of the basic building blocks of a neural network.

Upcoming Events

.NET Insight

Sign up for our newsletter.

I agree to this site's Privacy Policy.