Neural Network 편집하기 (부분)

==Types of Neural Networks==
Various types of neural networks are used for different tasks, each with unique architectures suited to specific data types:
*'''Feedforward Neural Network (FNN)''': The simplest type, where information flows in one direction from input to output. Used in basic classification and regression tasks.
*'''Convolutional Neural Network (CNN)''': Designed for image processing, CNNs use convolutional layers to detect spatial hierarchies in visual data, commonly applied in computer vision.
*'''Recurrent Neural Network (RNN)''': Tailored for sequential data, RNNs are used in language modeling, time series prediction, and speech recognition. Variants like LSTM and GRU improve long-term dependency learning.
*'''Autoencoder''': A neural network designed for unsupervised learning tasks like dimensionality reduction and anomaly detection by learning efficient representations of data.
*'''Transformer''': A modern architecture that processes sequences in parallel, highly effective in natural language processing tasks. Notable transformer models include BERT and GPT.