What is fully connected layer?
A layer in a neural network where every input is connected to every output
fully connected layer explained in plain English
Analogy
A fully connected layer is like a big meeting room where every person (input) is talking to every other person (output) at the same time, allowing them to discuss and decide on the best course of action together
Example
Image classification models often use fully connected layers to take the features extracted from an image and output a probability distribution over different classes, such as 'cat' or 'dog'
How is fully connected layer used?
Fully connected layers are often used in the final stages of a neural network to make predictions or classify inputs, as they can learn to recognize complex patterns in the data
Common misconceptions about fully connected layer
One common misconception is that fully connected layers are always necessary, but in fact, they can be computationally expensive and may not be needed for simpler tasks
History
Fully connected layers have been a part of neural networks since their inception, and were widely used in early models such as the perceptron
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