Is it better to have more layers and nodes of neural networks in deep learning?

Zetao Zhang

doi:10.1117/12.3013350

29 November 2023 Is it better to have more layers and nodes of neural networks in deep learning?

Zetao Zhang

Proceedings Volume 12937, International Conference on Internet of Things and Machine Learning (IoTML 2023); 129371N (2023) https://doi.org/10.1117/12.3013350
Event: International Conference on Internet of Things and Machine Learning (IoTML 2023), 2023, Singapore, Singapore

Abstract

Deep learning is an essential part of machine learning, and neural network is an important technology in machine learning all the time. Neural networks can simulate the neural net and synaptic structure of the biological brain to complete data analysis and problem processing and can summarize the law from a time experience like human beings. For the human brain, the more layers and synapses the human brain neural network has, the deeper the level of abstraction of the input features established for some unknown things, so the deeper the accuracy of people's understanding is relative. Then, can the same logic be applied to neural networks in deep learning? Or, to put it differently, the deeper the neural network is and the more nodes it has, the better its performance must be.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zetao Zhang "Is it better to have more layers and nodes of neural networks in deep learning?", Proc. SPIE 12937, International Conference on Internet of Things and Machine Learning (IoTML 2023), 129371N (29 November 2023); https://doi.org/10.1117/12.3013350

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