hidden layers in neural networks code examples tensorflow
In neural networks, hidden layers are intermediary layers situated between the input and output layers. These layers perform a key role in learning complex representations by applying non-linear transformations through activation functions. The number of neurons and layers directly affects the capacity of the network to capture intricate relationships in the input data. Neural networks…