Components
Components of a Deep Learning Network
- The first layer, the input layer, receives the input data and passes it to the first hidden layer.
- The hidden layers now perform the calculations on the received data. The biggest challenge here in neural networks creation is to decide the number of neurons and optimal number of hidden layers.
- Finally, the output layer takes in the inputs that are passed in from the layers before it and performs the calculations via its neurons to compute the output.
Deep learning requires a large amount of data for best results, while processing the data, neural networks can classify data with labels received from the dataset involving highly complex mathematical calculations.
For example, in Facial Recognition, the model works by learning to detect and recognize edges and lines of the face, then to more significant features, and finally, to overall representations of the face.
While representing a neural network, every node is provided with information in the form of inputs. The node then multiplies the inputs with randomly initialized weights and adds a bias value to the result. At last, nonlinear activation functions are applied to determine which neuron to fire.