LEARN NEURAL NETWORKS IN A DAY: ...and Deep Learning with C, Python, Java and R by Michael Redcar

Author:Michael Redcar
Language: eng
Format: azw3, epub
Published: 2017-05-09T07:00:00+00:00

Neural network in java

When it comes to artificial intelligence, the role of the neural networks is pretty important. Only with the help of neural networks, machine learning can be accomplished. Neural networks can be of plenty of different types like the feedforward,back propagation neural network. Moreover, you would be able to integrate the neural networks with a wide variety of programming languages. Today we would be speaking about neural networks working with java.

Construction of the neural network:

If you're looking at the construction of the neural network, you would realize that it has an input on one end and on the other hand, it is an output. In between, you would find hidden layers. The hidden layer can add to more processing power to the neural networks. Normally, the output layer would be just replicating the output of the hidden layer. As the size of the hidden layer increases, the complexity also increases.

Training the neural network:

It is important that the neural network is able to predict the right output. In order to help and also, 1st a standard input is given to the neural network. Then the output is measured against the expected output. Only when this measurement is done, it becomes possible for the user to determine whether the neural network is able to predict the right output or not. If the neural network is not able to predict the right output, the weight of the neural network is changed in such a way that the next time around, this particular input is provided to the neural network, it is able to produce the required and the correct output. The process which we described above is for a single number output. However, when you're feeding the data, the same technique is used repeatedly in order to generate the correct output which is needed.

Error detection:

Once error has been detected, as compared to the expected output, then the calculation would start regarding the quantum of the error. After that, the neural network is adjusted in such a way that the next time around, the accuracy would be better and the quantum of error would be on the lower side.

So, if you're looking at the functioning of the neural network in java, this is the way in which it would be able to predict the right output and increase its accuracy each time an input is fed to it.

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