Abstract
Currently, deep learning of neural networks is one of the most popular methods for speech recognition, natural language processing, and computer vision. The article reviews the history of deep learning of neural networks and the current state in General. We consider algorithms for training neural networks used for deep training of neural networks, followed by fine-tuning using the method of back propagation of errors. Neural networks with large numbers of hidden layers, frequently occurring and disappearing gradients are very difficult to train. In this paper, we consider methods that successfully implement training of neural networks with large numbers of layers (more than one hundred) and vanishing gradients. A review of well-known libraries used for successful deep learning of neural networks is conducted.
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