Convolutional Neural Networks (CNNs) are a type of artificial neural network (ANN) designed for image recognition tasks. They are characterized by their ability to automatically learn and extract features from images, making them highly effective for pattern recognition. Unlike traditional ANNs, CNNs use a specialized architecture that includes convolutional layers, pooling layers, and fully-connected layers. Convolutional layers apply filters to detect local features, pooling layers reduce spatial dimensions, and fully-connected layers perform classification. CNNs are particularly effective in image processing due to their ability to encode image-specific features into the architecture, making them more suitable for image-focused tasks. They reduce the number of parameters required to set up the model, which helps in managing computational complexity. However, traditional ANNs can struggle with the computational complexity of image data, especially for larger images. Overfitting is a significant issue in neural networks, where the model performs well on training data but poorly on new data. To mitigate overfitting, techniques such as reducing the number of parameters, using dropout, and employing regularization are used. CNNs are designed to reduce overfitting by using convolutional and pooling layers that help in learning more general features. The architecture of a CNN typically includes convolutional layers that apply filters to detect features, pooling layers that reduce spatial dimensions, and fully-connected layers that perform classification. The convolutional layers are responsible for learning spatial hierarchies of features, while pooling layers help in reducing the spatial dimensions and computational load. CNNs are widely used in various applications such as image classification, object detection, and image segmentation. They are particularly effective in tasks where the input is an image, and they have shown superior performance compared to traditional ANNs in many image recognition tasks. Despite their power, CNNs can be resource-intensive, requiring significant computational resources and memory. Techniques such as using smaller filters, adjusting the stride, and applying zero-padding can help in managing the computational load.Convolutional Neural Networks (CNNs) are a type of artificial neural network (ANN) designed for image recognition tasks. They are characterized by their ability to automatically learn and extract features from images, making them highly effective for pattern recognition. Unlike traditional ANNs, CNNs use a specialized architecture that includes convolutional layers, pooling layers, and fully-connected layers. Convolutional layers apply filters to detect local features, pooling layers reduce spatial dimensions, and fully-connected layers perform classification. CNNs are particularly effective in image processing due to their ability to encode image-specific features into the architecture, making them more suitable for image-focused tasks. They reduce the number of parameters required to set up the model, which helps in managing computational complexity. However, traditional ANNs can struggle with the computational complexity of image data, especially for larger images. Overfitting is a significant issue in neural networks, where the model performs well on training data but poorly on new data. To mitigate overfitting, techniques such as reducing the number of parameters, using dropout, and employing regularization are used. CNNs are designed to reduce overfitting by using convolutional and pooling layers that help in learning more general features. The architecture of a CNN typically includes convolutional layers that apply filters to detect features, pooling layers that reduce spatial dimensions, and fully-connected layers that perform classification. The convolutional layers are responsible for learning spatial hierarchies of features, while pooling layers help in reducing the spatial dimensions and computational load. CNNs are widely used in various applications such as image classification, object detection, and image segmentation. They are particularly effective in tasks where the input is an image, and they have shown superior performance compared to traditional ANNs in many image recognition tasks. Despite their power, CNNs can be resource-intensive, requiring significant computational resources and memory. Techniques such as using smaller filters, adjusting the stride, and applying zero-padding can help in managing the computational load.