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Researchers from the National Research Nuclear University MEPhI's Institute of Cyber Intelligence Systems have recently developed a new learning model for the restricted Boltzmann machine (a neural network) which helps optimize the processes of semantic encoding, visualization and data recognition.
Today, deep neural networks with different architectures, such as convolutional, recurrent and autoencoder networks, are becoming an increasingly popular area of research. A number of high-tech companies, including Microsoft and Google, are using deep neural networks to design various intelligent systems. Together with deep neural networks, the term "deep" learning has gained currency.
In deep learning systems, the processes of feature selection and configuration are automated, which means that
the networks can choose between the most effective algorithms for hierarchal feature extraction on their own. Deep learning is characterized by learning with the help of large samples using a single optimization algorithm. Typical optimization algorithms configure the parameters of all operations simultaneously and effectively estimate every neural network parameter's effect on error with the help of the so-called backpropagation method.
"The neural networks' ability to learn on their own is one of their most intriguing properties," explained Vladimir Golovko, Professor at the MEPhI Institute of Cyber Intelligence Systems. "Just like biological systems, neural networks can model themselves, seeking to develop the best possible model of behavior."
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