Understanding Fully Connected Layers in Deep Learning
A Fully Connected layer is a type of neural network layer where every neuron in the layer is connected to every neuron in the previous and subsequent layers. The “fully connected” descriptor comes from the fact that each of the neurons in these layers is connected to every activation in the previous layer.
- In CNNs, fully connected layers often follow convolutional and pooling layers, serving to interpret the feature maps generated by these layers into the final output categories or predictions.
- In fully connected feedforward networks, these layers are the main building blocks that directly process the input data into outputs.
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