Neural networks come in various forms, each with its unique characteristics and applications. Let's explore some common types:
Feedforward Neural Networks: Feedforward neural networks are the most basic type. They consist of an input layer, one or more hidden layers, and an output layer. These networks process data in a forward direction, from input to output, without any feedback loops. They are used for tasks like classification and regression.
Convolutional Neural Networks (CNNs): CNNs are specifically designed for tasks involving images and pattern recognition. They make use of convolutional layers to extract features from input images. CNNs have been remarkably successful in applications such as image classification, object detection, and image generation.
Recurrent Neural Networks (RNNs): RNNs are designed to handle sequential data, such as time series or text. They use recurrent connections that allow information to flow from previous steps to the current step. RNNs are widely used in tasks like language modeling, speech recognition, and machine translation.