Simulation Consolidation Method for Deep Learning Based on Cloud Computing

Abstract

The invention discloses a cloud computing-based deep learning simulation consolidation method, which belongs to the field of deep learning. The cloud computing-based deep learning simulation consolidation method can improve the depth by setting an error model for the error between a data group similar to the simulation environment and ideal data. The accuracy and sensitivity of the actual operation of the learning object, in the actual fine-tuning and correction process, through the setting of the difference node and the mutual capacity correction circle outside the difference node, the determination range of the next operation data point can be continuously narrowed, and the next operation data can be effectively improved. At the same time, it can reduce the difficulty of judging the data points of the next operation, improve the overall operation efficiency of the deep learning object, and expand the depth of the simulation environment through the difference between the data group and the ideal data that are significantly different from the simulation environment. Narrow the difference between the simulation environment and the actual operating environment, and then improve the comprehensiveness of deep learning objects for deep learning.

Description

Cloud computing-based deep learning simulation consolidation method technical field The present invention relates to the field of deep learning, more specifically, to the deep learning simulation consolidation method based on cloud computing. Law. Background technique Deep learning (DL, Deep Learning) is a new field in the field of machine learning (ML, Machine Learning). research direction, it was introduced into machine learning to bring it closer to its original goal - artificial intelligence (AI, Artificial Intelligence). Intelligence). Deep learning is to learn the inherent laws and representation levels of sample data, and the information obtained in these learning processes Interpretation of data such as text, images and sounds helps a lot. Its ultimate goal is to enable machines to be human-like It has the ability to analyze and learn, and can recognize data such as text, images and sounds. Deep learning is a complex machine learning algorithm The eff...

AI Technical Summary

Problems solved by technology

[0005] The existing deep learning simulation is only to obtain relevant data in a simulated environment, and use the relevant data as the basis for the actual operation of the deep learning object in the later stage. However, the data obtained by deep learning is obtained under the ideal environment of the simulation, and the data sheet Ideally, when the deep learning object is operated out of the simulated environment, there will be some situations that do not appear in the simulated environment or are similar but different from the simulated environment, resulting in a certain gap between the final actual operating data and the ideal data. The error affects the subsequent actual operation efficiency, sensitivity and accuracy of deep learning objects

Images