Different Types of Neural Networks and Applications: Evidence from Feedforward, Convolutional and Recurrent Neural Networks

Yumin Pan

doi:10.54097/6rn1wd81

Authors

Yumin Pan

DOI:

https://doi.org/10.54097/6rn1wd81

Keywords:

Computer science, Artificial Intelligence, Machine learning, Neural Network.

Abstract

Neural networks have achieved great process in the 90 years since they were officially introduced in 1943. Because of its wide application and huge research and development potential, this technology attracts more and more scientific and technological workers to the research of neural networks. Neural network technology is an essential component of AI development, and it is a significant indicator of a country's overall strength. In this paper, this study will demonstrate Feedforward Neural Network, Convolution Neural Network and Recurrent Neural networks and evaluate them through datasets from kaggle.com. and CSDN (China IT community). Through this paper, readers can have a better outlook and understanding of the operating principles of each type of neural network as well as their specific jobs (what kind of jobs they specialized in) and each application of these neural networks. So that this paper can promote readers' thoughts and help them start learning neural networks or be a supplement or reference for future scholars. In the end, this paper will present the outcome, which is the evaluation of the accuracy, loss curve, and accuracy curve of neural networks.

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