With the advent of big data, deep learning technology has become an important research direction in the field of machine learning, which has been widely applied in the image processing, natural language processing, speech recognition and online advertising and so on. This paper introduces deep learning techniques from various aspects, including common models of deep learning and their optimization methods, commonly used open source frameworks, existing problems and future research directions. Firstly, we introduce the applications of deep learning; Secondly, we introduce several common models of deep learning and optimization methods; Thirdly, we describe several common frameworks and platforms of deep learning; Finally, we introduce the latest acceleration technology of deep learning and highlight the future work of deep learning.
The potential of using neural networks in TIG welding has been investigated. It has been successfully demonstrated by using a commercial package, BRAIN-MA KER, on an IBM PC that the feed forward multilayer neural network, trained by the back-propagation algorithm with suitable structures, can give satisfactory predictions for both manual and mechanised TIG Welding. A neural network has been trained to predict the welding conditions (procedure) necessary to produce a good quality weld. The types of defect arising by the use of other welding conditions are also predicted. The feasibility of incorporating a neural network into a welding control system is also discussed.
In order to solve the problems of relatively backward agricultural irrigation technology, low utilization rate of water resources and serious waste, with raspberry pie as the main control chip, combined with all kinds of intelligent sensors, solenoid valve external control equipment, big data analysis and intelligent algorithm, an intelligent water-saving irrigation control system based on BP neural network is designed. The system takes raspberry pie as the main control module, according to the real-time weather forecast data of the current area obtained through WI-FI, and combined with the real-time monitoring of soil moisture by the soil moisture information collection module, the BP neural network model is used to intelligently judge whether irrigation is needed and automatically output the amount of water needed for irrigation. The test results of the system show that the predicted value of crop water demand based on BP neural network almost coincides with the expected value, indicating that the system can effectively and accurately predict crop water demand, and all kinds of modules controlled by raspberry pie can be combined organically, and the system is easy to operate, stable and reliable, and can provide optimal precision irrigation for crops. The system ensures the best growth of crops with the minimum amount of irrigation water, provides targeting and refinement for modern agriculture, effectively improves the utilization rate of water resources, has strong practicability, and has strong application value in agricultural production.
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