Research on Computer Big Data Model Based on Deep Volume Learning Algorithm

Abstract

With the rapid development of information and communication technology, the amount of global data is increasing explosively. How to effectively analyze massive and complex data, mine and realize its potential value and make rational use of it is one of the important topics at present. An incomplete big data filling algorithm based on deep learning is proposed. Firstly, the filling automatic coder is established based on the automatic coder. On this basis, the depth filling network model is constructed, the depth characteristics of incomplete big data are analyzed, and the network parameters are calculated according to the layer by layer training idea and back propagation algorithm The target detection algorithm based on deep learning is far ahead of the traditional target detection algorithm in detection accuracy. Relying on big data to automatically learn and extract features, the effect is far better than that of manually designed features. Although big data has brought great potential to many fields such as industry, education and health care, it is a very arduous task to obtain valuable knowledge from big data. Learning the characteristics of big data and mining the information hidden in big data requires both advanced technology and interdisciplinary cooperation. Based on the deep convolution NN model of deep learning algorithm, the proposed field of computer vision has made remarkable achievements in recognition ability. This paper mainly discusses the application of deep convolution NN in computer vision.