17. What is the difference between a fully connected layer and a convolutional layer in a neural network?
(A) Fully connected layers are only used in supervised learning, while convolutional layers are used in unsupervised learning.
(B) Fully connected layers require less computation than convolutional layers.
(C) Fully connected layers connect every input neuron to every output neuron, while convolutional layers only connect a subset of input neurons to a subset of output neurons.
(D) Convolutional layers are used for time-series data, while fully connected layers are used for image data.
(E) Convolutional layers are used for classification tasks, while fully connected layers are used for regression tasks.