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introduction
With the development of artificial intelligence technology, neural networks, as one of the core technologies of artificial intelligence, are widely used in image recognition, speech recognition, natural language processing and other fields. Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Deep Neural Network (DNN) and Spiking Neural Network (SNN) are currently the most popular and Efficient neural network architecture. These different types of neural networks have obvious differences in structure and algorithm, and each is suitable for different application scenarios. Therefore, an in-depth understanding of the differences and characteristics of these different types of neural networks is of great significance for selecting appropriate neural network models and applying them to practical problems.
This article aims to comprehensively compare the applications, advantages and disadvantages, and learning methods of four neural networks, CNN, RNN, DNN, and SNN, in different fields, in order to provide a reference for beginners. This article will introduce the structure and principles of each neural network, and illustrate its applications, advantages and disadvantages in different fields through rich examples. In addition, we will also delve into the methods and approaches of learning neural networks, as well as the related skills that need to be mastered.
1.1 Research background
Neural network, as an artificial intelligence technology that models by learning data, has been widely studied since the 1980s. As computer computing power and data volume continue to increase, the application fields of neural networks have also been greatly expanded. Four neural networks, CNN, RNN, DNN, and SNN, are widely used in image recognition, speech recognition, natural language processing and other fields, and have become the most popular ones at present.