Deep learning neural networks, also known as artificial neural networks, attempt to simulate the human brain by combining data inputs, weights, and bias. These components collaborate to accurately recognize, classify, and describe objects in data.

Deep neural networks are made up of multiple layers of interconnected nodes, with each layer improving and optimizing the prediction or categorization. Forward propagation refers to the movement of computations through a network. The visible layers of a deep neural network are the input and output layers. The deep learning model ingests data for processing in the input layer, and the final prediction or classification is made in the output layer.

Backpropagation is another process that uses algorithms such as gradient descent to calculate prediction errors and then adjusts the weights and biases of the function by moving backwards through the layers in order to train the model. Forward and backpropagation work together to allow a neural network to make predictions and correct for errors. The algorithm gradually improves in accuracy over time.