什么是机器学习中的卷积神经网络（CNN）？

Convolutional Neural Networks (CNNs) are a type of deep learning model widely used in image processing, computer vision, and natural language processing. By mimicking the human visual system, CNNs can automatically extract features from images, reducing the need for manual feature engineering. The core architecture consists of convolutional layers, pooling layers, and fully connected layers. Convolutional layers are responsible for extracting local features, while pooling layers reduce the dimensionality of features, enhancing computational efficiency and the model's generalization ability. CNNs excel in tasks such as image classification, object detection, and image generation, making them a crucial component of deep learning. As technology advances, various modifications and improvements to CNNs continue to emerge, driving progress in artificial intelligence.