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Artificial neural networks and related deep learning are conquering other areas of the industry.
It underpins most deep learning models. As a result, deep learning may sometimes be referred to as deep neural learning or deep neural networking. The use of networks built of artificial neurons allows to create software that imitates the work of the human brain, which translates into an increase in the efficiency of business processes and companies.

The Neural Network is constructed from 3 type of layers:

1. Input layer — initial data for the neural network.
2. Hidden layers — intermediate layer between input and output layer and place where all the computation is done.
3. Output layer — produce the result for given inputs.

The input layer is used to retrieve data and pass it on to the first hidden layer.
In hidden layers, calculations are performed, as well as the learning process itself.
The output layer calculates the output values ​​obtained from the entire network, and then sends the obtained results to the outside.

Each node has a weight and a threshold – when the threshold value exceeds the allowable value, it activates and sends data to the next layer. Neural networks need training data from which they learn to function properly. As they receive more data, they can improve their performance.

Neural networks come in several different forms, including recurrent neural networks, convolutional neural networks, artificial neural networks and feedforward neural networks, and each has benefits for specific use cases. However, they all function in somewhat similar ways — by feeding data in and letting the model figure out for itself whether it has made the right interpretation or decision about a given data element.

Neural networks involve a trial-and-error process, so they need massive amounts of data on which to train. It’s no coincidence neural networks became popular only after most enterprises embraced big data analytics and accumulated large stores of data. Because the model’s first few iterations involve somewhat educated guesses on the contents of an image or parts of speech, the data used during the training stage must be labeled so the model can see if its guess was accurate. This means, though many enterprises that use big data have large amounts of data, unstructured data is less helpful. Unstructured data can only be analyzed by a deep learning model once it has been trained and reaches an acceptable level of accuracy, but deep learning models can’t train on unstructured data.

Deep learning will be developed, and deep neural networks will find application in completely new areas. It is already predicted that they can be used in driving autonomous cars or in the entertainment sector to analyze the behavior of users of a streaming service, or add sound to silent movies.

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You can read more about Artificial Neural Network here.

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