OpenNN C++ Tutorials

Download OpenNN for C++ from GitHub and SourceForge

Getting started

Building OpenNN

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.

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OpenNN in 6 steps

In this tutorial we can see the principal ingredients to build a neural network model in a few steps using OpenNN.

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The software model of 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 modeling language that is used to specify, visualize, construct, and document the artifacts of a software system.

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Main classes

The Vector, Matrix and Tensor templates

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

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The data set class

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.

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The neural network class

In this tutorial, we will see that the class of neural network 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 to the neural network is also included here for convenience.

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The training strategy class

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 training strategy in OpenNN.

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The model selection class

In order 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.

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The testing analysis class

The purpose of testing is to compare the outputs from the neural network against targets in an independent testing set. In this tutorial, you will learn about the test for the quality of the model for the diffentts types of problems.

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