Neural Network Lab


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.

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