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How many layers are typically found in a neural network?
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In neural networks, the number of layers plays a crucial role in determining the complexity, depth, and capacity of the model to learn from data. The layers in a neural network are composed of interconnected nodes, or neurons, that process inputs and pass the output to the next layer. A neural network generally consists of three types of layers:
Input Layer: This is the first layer in the network that receives the raw data. The number of neurons in the input layer depends on the number of features or attributes in the input data. For instance, in image processing, if the input is a 28x28 pixel grayscale image, the input layer would have 784 neurons (28*28).
Hidden Layers: Hidden layers are the intermediate layers between the input and output layers. These layers perform the actual computations by applying weights and activation functions to the inputs. They are called “hidden” because their outputs are not directly observed. The number of…
