In deep learning, a computer model learns to perform tasks by considering examples rather than being explicitly programmed. The term "deep" refers to the number of layers in the network — the more layers, the deeper the network.
Deep learning is based on artificial neural networks (ANNs). These are networks of simple nodes, or neurons, that are interconnected and can learn to recognize patterns of input. ANNs are similar to the brain in that they are composed of many interconnected processing nodes, or neurons. Each node is connected to several other nodes and has a weight that determines the strength of the connection.
Layer-wise, the first layer of a neural network extracts low-level features from the data, such as edges and shapes. The second layer combines these features into more complex patterns, and so on until the final layer (the output layer) produces the desired result. Each successive layer extracts more complex features from the previous one until the final output is produced.
This process is also known as forward propagation. Forward propagation can be used to calculate the outputs of deep neural networks for given inputs. It can also be used to train a neural network by back-propagating errors from known outputs.
Backpropagation is a supervised learning algorithm, which means it requires a dataset with known correct outputs. Backpropagation works by comparing the network's output with the correct output and then adjusting the weights in the network accordingly. This process repeats until the network converges on the correct output. Backpropagation is an important part of deep learning because it allows for complex models to be trained quickly and accurately.
This process of forward and backward propagation is repeated until the error is minimized and the network has learned the desired pattern.