Скачать или смотреть CNN vs RNN vs ANN vs GNN Using Python

CNN (Convolutional Neural Networks), RNN (Recurrent Neural Networks), ANN (Artificial Neural Networks), and GNN (Graph Neural Networks) are different types of neural networks used for various tasks. CNNs excel in image classification and object detection by processing spatial data, while RNNs specialize in sequential data, such as time series and text generation. ANNs are general-purpose networks commonly used in tasks like facial recognition and computer vision, relying on fully connected layers. GNNs are designed for graph-structured data, useful for applications like social network analysis and fraud detection, using message-passing mechanisms to capture node relationships. CNNs use convolutional layers with weight sharing across spatial regions, while RNNs use recurrent layers to capture temporal dependencies with weight sharing across time steps. ANNs have no weight sharing and use fully connected layers for general modeling. GNNs share weights across graph edges and are specialized for graph data. Pre-trained CNN models like MobileNetV2 are used to save time in image classification tasks, while RNNs are employed in tasks like sentiment analysis and text generation. GNNs can be applied in stock market analysis, where graph-based models help predict relationships between different stocks using financial data. These models, with their varying structures and strengths, are essential tools for deep learning in specialized fields.