When analyzing a neural network, where does the majority of the parameters originate from?

The majority of the parameters in a neural network generally originate from dense layers, also known as fully connected layers. These layers are designed to connect every neuron from the previous layer to every neuron in the dense layer. This dense connectivity leads to a significant number of weights that need to be learned during the training process.

In contrast, while convolutional layers do have parameters, they typically have fewer compared to dense layers because they share weights across spatial dimensions, reducing the number of total parameters needed. Input layers do not contain any parameters as they simply pass input data to the next layer. Embedded layers, often used for categorical data representation, also contribute parameters but generally not to the extent seen in dense layers.

Understanding this distribution of parameters helps in designing more efficient neural networks and managing computational resources effectively.