OpenNN C++ Tutorials
To start, please download OpenNN for C++ from GitHub
Getting started:
Start working with openNN with the following tutorials
Building OpenNN
Learn how to build the OpenNN library on any system using our project files for Qt creator. If working with another compiler is needed, you will learn how to create a project for it.
OpenNN in 6 steps
This tutorial shows the principal ingredients to build a neural network model in a few steps using OpenNN.
The software model
Discover the software model of OpenNN, carried out in the Unified Modeling Language (UML), which is used to specify, visualize, construct, and document the artefacts of a software system.
Machine learning examples:
Start working with openNN with the following tutorials
Approximation:
Airfoil self-noise prediction
The fundamental goal of this example is to predict the noise generated by an aircraft's airfoil blades.
Classification:
Breast cancer diagnosis
This tutorial shows the principal ingredients to build a neural network model in a few steps using OpenNN.
API comparison:
This tutorial compares the three ways to use the OpenNN library—the C++ API, the Command Line Interface, and the Graphical User Interface, to help users choose the best method for their neural network projects.
Main classes:
Start working with openNN with the following tutorials
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.
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.
The neural network class
Discover the class of neural networks implemented in OpenNN, based on the multilayer perceptron. That model is extended here to contain scaling, unscaling, bounding, probabilistic, and conditions layers.
The training strategy class
Learn how to use a training strategy in OpenNN: the procedure used to carry out the learning process in a neural network.
The model selection class
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 testing analysis class
Learn how to test the quality of a model for different types of problems and compare the outputs from the neural network against targets in an independent testing set.
Text Classification:
Amazon reviews classification
The main goal of this example is to classify amazon customer reviews into positive and negative.
Text Classification:
Amazon reviews classification
The main goal of this example is to classify amazon customer reviews into positive and negative.