Limitations of Fully Connected Layers
Despite their benefits, FC layers have several drawbacks:
- High Computational Cost: The dense connections can lead to a large number of parameters, increasing both computational complexity and memory usage.
- Prone to Overfitting: Due to the high number of parameters, they can easily overfit on smaller datasets unless techniques like dropout or regularization are used.
- Inefficiency with Spatial Data: Unlike convolutional layers, FC layers do not exploit the spatial hierarchy of images or other structured data, which can lead to less effective learning.
What is Fully Connected Layer in Deep Learning?
Fully Connected (FC) layers, also known as dense layers, are a crucial component of neural networks, especially in the realms of deep learning. These layers are termed “fully connected” because each neuron in one layer is connected to every neuron in the preceding layer, creating a highly interconnected network.
This article explores the structure, role, and applications of FC layers, along with their advantages and limitations.
Table of Content
- Structure of Fully Connected Layers
- Working and Structure of Fully Connected Layers in Neural Networks
- Key Role of Fully Connected Layers in Neural Networks
- Advantages of Fully Connected Layers
- Limitations of Fully Connected Layers
- Conclusion
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