- **Modularized components**: helps the development and the clear separation of concerns with great benefits for who wants to use mixed solutions.

- **Configurable computation precision**: helps to avoid the noise deriving from operation errors and default system precision limits with great improvement of the learning speed and performance stability.

- **Configuration checker**: helps to write less details about configuration and to keep compatibility with older version while the project evolves.

- TODO (Host) **Networks composition**: helps to create very effective architectures of multiple neural networks able to obtain advanced behaviours like in deep learning.

- TODO (Host) **Computation parallelization**: helps to improve the scalability of your whole system.

- TODO (Bios) **Sessions intercommunication**: helps to improve the scalability of your whole system.

var DN2A = require("dn2a");

// Instantiation

var neuralNetwork = new DN2A.NetworkAlpha();

// Training

var trainingPatterns = [

{

}

];

neuralNetwork.train(trainingPatterns);

// Querying

//

// The object passed to the callback function contains information about the querying process.

// Importation

// ...

// Instantiation

//

// The list of the properties is reported in the main README file.

var neuralNetwork = new DN2A.NetworkAlpha({

layerDimensions: [2, 4, 4, 1], // the default would be [2, 4, 1]

learningMode: "continuous",

},

numbersPrecision: 32

});

// Training

// ...

// Querying

// ...

// Importation

// ...

// Instantiation

// ...

// Training

//

// The object passed to the callback function contains information about the training process.

neuralNetwork.train(trainingPatterns, function(trainingStatus) {

console.log("Epoch: " + trainingStatus.elapsedEpochCounter);

});

// Querying

// ...

// Importation

// ...

// Instantiation

var cerebrum = new DN2A.Cerebrum({

minds: [

"firstNeuralNetwork"

]

});

// Training

//

// The name passed to the trainMind method specifies which specific mind to train

cerebrum.trainMind(trainingPatterns, function(trainingStatus) {

console.log("Epoch: " + trainingStatus.elapsedEpochCounter);

}, "firstNeuralNetwork");

// Querying

//

// The name passed to the queryMind method specifies which specific mind to query