Recurrent Neural Network (RNN)

What is a Recurrent Neural Network?

Neural networks can be feedback or recurrent. These are characterised by connections between neurons and layers. Whether these layers are on a previous layer or the same layer as the respective neuron is irrelevant here, both are possible. The human neocortex, for example, contains such interconnections of neuronal networks. In neural networks of an artificial nature, the already mentioned recurrent interconnection is used to read out information that is temporarily encoded in different data and data sets.

Areas of application

When it comes to problems in processing different sequences, sequences and processes, recurrent neural networks are used to solve them. In daily practice, these are found in the Speech or handwriting recognition and machine translation take place. There are many programme libraries where neural networks have been integrated.

Subdivision of neural networks

  • Direct feedback: = The own output becomes an additional input.
  • Indirect feedback: Establishes a connection between a neuron output and a neuron from the previous layer.
  • Lateral feedback: Establishes a new connection between the output of one neuron with another neuron of the same layer.
  • Full connection: Here, each output of neurons has an independent connection to every other neuron.

Train neural networks

Training through different ways and methods of so-called machine learning is possible here to a limited extent. Neural networks are thus only capable of training and learning to a limited extent. For this reason, a different approach is used. Here, it is not the neural network itself that is trained, but the reading out, i.e. the information output.

The neural network thus represents a memory or container here, which is seen as a large reservoir and represents the starting point for the training. Through the method "Backpropagation Through Time"This network is converted into a so-called feedforward network within the training units. In this process, the sequence length again plays a greater role.

The future of Recurrent Neural Network

Exploiting the feedback between neurons in a targeted way in order to draw on what has already been learned when making future decisions has been taking place for thousands of years, ever since mankind has existed. Profiting from experience is the most normal thing there is. The animal world has also benefited from this kind of conditioning since before mankind existed.

Researchers, scientists and IT experts have taken advantage of this fact to create and constantly expand machine learning. Recurrent neural networks are one of the most important components of many technologies and inventions that people use in everyday life today and take for granted. But the processes behind it are far more complex.

Constant improvements and updates, such as increasing the capacity of the storage locations, are important so that more and more experiences, contexts and data can be recorded and retrieved as needed. There is really no need to worry about the future of Recurrent Neural Network. Globalisation and technological progress allow for a steady expansion of Recurrent Neural Network worldwide.

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