Scientists improve deep learning method for neural networks

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 intelligent systems. 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.