Any neural network processing a framework has the following architecture:
There is a set of inputs, a processor, and a set of outputs. This layered approach is also followed in neural networks. The inputs form the input layer, the middle layer(s) which performs the processing is called the hidden layer(s), and the output(s) forms the output layer.
Our neural network architectures are also based on the same principle. The hidden layer has the magic to convert the input to the desired output. The understanding of the hidden layer requires knowledge of weights, bias, and activation functions, which is our next topic of discussion.
