There are many algorithms involved, such as back propagation algorithm, forward propagation algorithm, convolution algorithm, matrix far point algorithm, gradient optimization algorithm, evaluation algorithm and so on. It is too general to describe the neural network simply by using algorithms, and the programming process of neural network is generally described directly by corresponding mathematical principles and formulas.

There are three common deep learning algorithms: deconvolution neural network, cyclic neural network and generating countermeasure network.

There are three commonly used algorithms for deep learning: convolutional neural network, cyclic neural network and generative confrontation network. Convolutive neural networks (CNN) is a feedforward neural network with deep structure, which includes convolution calculation and is one of the representative algorithms of deep learning.

BP algorithm is by far the most successful neural network learning algorithm. When neural networks are used in practical tasks, most of them are trained by BP algorithm [2], including convolutional neural networks (CNN) under the popular concept of recent deep learning.

In fact, neural network is also called artificial neural network, which is simply ANN. The algorithm was very popular in machine learning in the 1980s, but it declined in the 1990s. Now, with the development of deep learning, neural network has once again appeared in everyone's field of vision and become one of the most powerful machine learning algorithms.

This model is generally expressed intuitively by graph model in computer science. The "depth" of deep learning refers to the number of layers of graph model and the number of nodes in each layer, which is greatly improved compared with the previous neural network.