Neural networks come in various types tailored to specific tasks. Feedforward Neural Networks (FNNs) are the simplest, suitable for general regression or classification.
Convolutional Neural Networks (CNNs) excel in image processing, capturing spatial hierarchies through convolutional layers. Recurrent Neural Networks (RNNs), with their memory capabilities, handle sequential data like text or time series. Variants like LSTMs and GRUs address limitations in standard RNNs.
Other types include Generative Adversarial Networks (GANs) for generating new data, Autoencoders for dimensionality reduction, and Transformer models for NLP tasks. The choice depends on the problem's nature and data type.