Fully connected neural network
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Fully connected neural networks (FCNNs) are a type of artificial neural network where the architecture is such that all the nodes, or neurons, in one layer are connected to the neurons in the next layer.
While this type of algorithm is commonly applied to some types of data, in practice this type of network has some issues in terms of image recognition and classification. Such networks are computationally intense and may be prone to overfitting. When such networks are also 'deep' (meaning there are many layers of nodes or neurons) they can be particularly hard for humans to understand.
While some convolutional neural networks may contain some fully connected layers, they do not have the same architecture as a fully connected neural network.
- 1. Alom, Md Zahangir, Taha, Tarek M., Yakopcic, Chris, Westberg, Stefan, Sidike, Paheding, Nasrin, Mst Shamima, Hasan, Mahmudul, Van Essen, Brian C., Awwal, Abdul A. S., Asari, Vijayan K.. A State-of-the-Art Survey on Deep Learning Theory and Architectures. (2019) Electronics. 8 (3): 292. doi:10.3390/electronics8030292 - Pubmed