Machine learning recognition method based on deep learning

Abstract

The invention discloses a deep learning-based machine learning identification method. The method comprises the following steps of S1, obtaining training parameter set related index data based on a BPneural network model according to an identification target parameter; S2, calling a corresponding data mining module and / or a Faster R-CNN model according to the acquired related index data to realizeautomatic detection and acquisition of a training parameter set; S3, calling a corresponding data preprocessing model to realize preprocessing of the training parameter set according to the identification target parameters and the obtained related index parameters, and obtaining training set data and test set data; and S4, inputting the training set data into a corresponding machine learning model for learning training, then updating parameters of the neural network according to forward propagation and reverse propagation until the model converges, and storing the trained model. The whole process is automatically completed by depending on different neural network models, so that the machine learning efficiency can be greatly improved.

Description

technical field [0001] The invention relates to the field of machine learning, in particular to a machine learning recognition method based on deep learning. Background technique [0002] With the advent of the era of big data, the classification and mining technology of massive data is particularly important. In massive data mining, how to use the information classified and mined from the existing data to guide the classification and mining of new data has become a new research hotspot. Especially when the number of samples for certain tasks is small, the use of multi-task learning can effectively reduce the time cost of massive data classification and mining and improve the accuracy of information acquisition. [0003] The method based on deep learning has been proved to be an effective and robust method for information classification in practice. Deep neural networks (such as deep convolutional neural networks) are the most representative machine learning methods. De...

AI Technical Summary

Problems solved by technology

This also leads to the training of machine learning models based on deep learning methods, which requires a large amount of training computing resources and a long training and learning time, which limits its convenience and versatility in practical applications.

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