Neural network consists of neurons (units) distributed among the layers. There are 3 types of units: input, output and hidden ones. Input units receive the information (training data), output units issue the received processed information. An artificial neuron receives signals from many inputs, processes them in a unified manner and transmits the result to many other artificial neurons, i.e. does the same thing as a biological neuron.
These units are connected with the hidden units on the different layers that transfer the information. The connection is represented by numbers called weights. So the unit of the first layer changes the unit of the next transferring the information having its own weight. The input information is processed and turned into a result and the training of the neural network is based on the experimental selection of such a weight coefficient for each synapse which leads to the desired result.
The number of layers illustrates how complex the neural network is. More layers result in more difficult tasks that it can deal with. This kind of neural network is able to learn deeply. Using the deep learning neural network Google improved the possibilities and quality of its Translator.