There are several learning methods used in neural networks, including:
Supervised learning: This is the most common learning method in which the network is trained using input-output pairs. The network is provided with input data, and the desired output for that data is also provided. The weights of the network are adjusted based on the error between the desired output and the actual output of the network.
Unsupervised learning: In unsupervised learning, the network is not provided with any output data. The network is trained on input data only, and the weights of the network are adjusted based on the input data distribution.
Reinforcement learning: This learning method involves providing the network with a reward or punishment for its actions. The network learns to perform actions that maximize the reward and avoid actions that lead to punishment.
Semi-supervised learning: This method is a combination of supervised and unsupervised learning. It is used when only a small amount of labeled data is available, and the network is trained using both labeled and unlabeled data.
Online learning: In online learning, the network is trained on new data as it becomes available. The weights of the network are adjusted continuously based on the new data.
Batch learning: In batch learning, the network is trained on a fixed set of data. The weights of the network are adjusted only after the network has processed all the data in the batch.
Each learning method has its advantages and disadvantages, and the choice of method depends on the problem at hand and the available data.
