When we combine multiple neurons together, it creates a vector of neurons called a layer.
When we combine multiple layers together, where all the neurons are interconnected to each other, this network of neurons is called, Artificial Neural Network or abbreviated as ANN.
There are three major type of layers in the ANN listed below
- The Input layer (interface to accept data): The data input is handled by a single input layer, which transmits it to the network. Keep in mind that this layer is only the passed data vector. It is only an interface to receive data for the hidden layers, which are the real neurons, and does not consist of an actual neuron layer.
- The Hidden layer(s) (Actual Neurons):An ANN may include one or more hidden layers. These are the real neurons that use the input data to calculate things. How many hidden levels has to be employed is inconclusive. It is quite difficult to determine the ideal number of hidden layers and the number of neurons in each layer, this is accomplished by looking at the end accuracy for different configurations.
- The Output layer (Actual Neurons to output the result):There is only one output layer and the number of neurons in it depends on the target variable.