Neural Network Lab

Parameter Sweeps, or How I Took My Neural Network for a Test Drive

The short definition of a parameter sweep is that it's the process of trying different training parameter values in order to find a good set of neural network weight values.

Step Up To Recurrent Neural Networks

Recurrent neural networks can solve some types of problems that regular feed-forward networks cannot handle.

How To Reuse Neural Network Models

Neural network models can be created, saved and reused. Here's how.

Neural Network Binary Classification

The differences between neural network binary classification and multinomial classification are surprisingly tricky. McCaffrey looks at two approaches to implement neural network binary classification.

Variation on Back-Propagation: Mini-Batch Neural Network Training

Let's explore mini-batch training, the third among a variety of back-propagation algorithms you can use for training a neural network.

Customize Neural Networks with Alternative Activation Functions

Here's how to use non-standard activation functions to customize your neural network system.

Neural Network Train-Validate-Test Stopping

The train-validate-test process is hard to sum up in a few words, but trust me that you'll want to know how it's done to avoid the issue of model overfitting when making predictions on new data.

Coding Neural Network Back-Propagation Using C#

Back-Propagation is the most common algorithm for training neural networks. Here's how to implement it in C#.

How To Use Resilient Back Propagation To Train Neural Networks

It's more complex than back propagation, but Rprop has advantages in training speed and efficiency.

Using Multi-Swarm Training on Your Neural Networks

Now that you know how to work with multi-swarm optimization, it's time to take it up a level and see how to train your network to use it.

Multi-Swarm Optimization for Neural Networks Using C#

Multi-swarm optimization (MSO) is a powerful variation of particle swarm optimization. Understanding how MSO works and how to implement it can be a valuable addition to your developer toolkit.

Neural Network Back-Propagation using Python

Python is James's preferred language for hybrid environments. Here's how to implement neural network back-propagation training using it.

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.

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