Deep neural networks, or so-called deep neural networks, are one of the top technologies in the field of machine learning and artificial intelligence. These networks model and recognize complex patterns in large and diverse data and are used in various fields such as image recognition, natural language processing, information retrieval, self-driving, and many other issues. In this article, we introduce and explain the principles of deep neural networks.
A deep neural network usually consists of several different layers. These layers include:
Input Layer: This layer receives input information from various sources such as images, texts or numerical data.
Hidden Layers: These layers extract latent and hidden features from the input data. Also, the network learns the parameters of the model by performing mathematical calculations in these layers.
Output Layer: This layer produces the final result of the model. Usually, in different problems, the output layer type is different. For example, in image classification problems, an output layer of different classes is used.
Deep neural networks can be designed in different ways. Some of the most famous types are:
Convolutional neural networks (CNN): They are used for image recognition and imaging.
Recurrent Neural Networks (RNN): They are used to process sequence data such as texts and speech.
Recurrent neural networks (LSTM و GRU): are a type of RNN used to model sequence data with the ability to retain past information.