Deep learning algorithms are a subset of machine learning algorithms that aim to discover multiple levels of distributed representations of input data. Recently, many deep learning algorithms have been proposed to solve traditional artificial intelligence problems. Now days, deep learning has been extensively studied in the field of computer vision and as such, a large number of related methods have arisen. Today, different algorithms and models of neural network–based research have made their place among the classification of images. The main purpose of these algorithms is to train the machine in artificial networks in a way that ultimately has a diagnosis close to the human brain. Among a variety of neural networks, CNN's channel neural networks usually offer good accuracy in the classification of images. In this article, in the first episode, we will discuss 4 deep learning methods: Convolutional neural network (CNN), Restricted Boltzmann Machines (RBMS), Autoencoders and Sparse coding, which after determining the necessary assumptions and applying a preliminary pre–training using the channel neural network algorithm we need Convolutional to perform a general preprocessing on the entered samples. Therefore, a preprocessing is performed on all data and preprocessed samples are stored in a separate location and then the rest of the processes are applied to these samples. Then, we use deep learning to identify faces and reveal them, and deep learning algorithms to reveal different subjects.