OpenNN C++ Tutorials

Getting started

OpenNN has been written in ANSI C++. This means that the library can be built on any system with little effort. OpenNN includes project files for Qt Creator. When working with another compiler is needed, a project for it must be created. In this tutorial, you'll learn how to do that.

This tutorial shows the principal ingredients to build a neural network model in a few steps using OpenNN.

In this tutorial, we present the software model of OpenNN. The whole process is carried out in the Unified Modeling Language (UML). The Unified Modeling Language (UML) is a general-purpose visual modelling language used to specify, visualize, construct, and document the artefacts of a software system.

Machine learning examples

The fundamental goal of this example is to predict the noise generated by an aircraft's airfoil blades.

This example aims to assess whether a lump in a breast could be malignant (cancerous) or benign (non-cancerous) from digitized images of a fine-needle aspiration biopsy.

In this example, we will forecast airline passengers number from past years' data.

Main classes

In this tutorial, we will learn about the Vector, Matrix, and Tensor templates and how OpenNN allows you to easily work with them.

The data set contains the information needed to construct the predictive model. In this tutorial, we will see how to use that concept within OpenNN.

This tutorial will show that the class of neural networks implemented in OpenNN is based on the multilayer perceptron. That model is extended here to contain scaling, unscaling, bounding, probabilistic, and conditions layers. A set of independent parameters associated with the neural network is also included here for convenience.

The procedure used to carry out the learning process in a neural network is called the training strategy. In this tutorial, you will learn about how to use a training strategy in OpenNN.

To obtain the best model, we have to optimize the architecture of the neural network. This tutorial shows the different types of model optimization and the algorithms contained in OpenNN.

The purpose of testing is to compare the outputs from the neural network against targets in an independent testing set. Here you will learn how to test the quality of a model for different types of problems.