Neural network

Neural network is an algorithmic mathematical model that simulates the working mode of the human brain's nervous system for distributed parallel information processing. Handwritten digit recognition typically uses neural networks as recognition models, and a classic neural network typically includes an input layer, convolutional layer, pooling layer, fully connected layer, and output layer. When the model needs to recognize new images, it extracts the features of the images and compares them with the features in the training set to obtain predicted numbers.

The unit is designed for unsupervised public use: controls are recessed or shielded, moving parts are enclosed, and the interface is operable by a child of about six with no instruction. Where a screen is used, the language can be switched without changing the hardware, and the content can be replaced on site.

Handwritten digit recognition is a technology that uses machine learning models to recognize human handwritten digits. It typically uses neural networks as recognition models to learn the mapping relationship between images and numbers by training on a large number of handwritten digit images. When the model needs to recognize new images, it extracts the features of the images and compares them with the features in the training set to obtain predicted numbers. Convolutional neural network is a deep learning model that is particularly suitable for classification and recognition tasks of signal data such as images, videos, and speech. Convolutional neural network is a type of feedforward neural network that includes convolutional computation and has deep structure, and is one of the representative algorithms of deep learning. Convolutional neural networks have representation learning ability and can perform translation invariant classification of input information according to their hierarchical structure, hence they are also known as "translation invariant artificial neural networks".