The correct answer is D. To classify images based on features.
A convolutional neural network (CNN) is a type of deep learning model that is commonly used for image recognition and classification. CNNs are composed of multiple layers of artificial neurons, each of which is responsible for detecting specific features in the input image. The first layer of a CNN typically detects edges, while subsequent layers detect more complex features such as shapes and objects. The final layer of a CNN outputs a probability distribution over the possible classes of the input image.
CNNs have been shown to be very effective for image recognition and classification. They have achieved state-of-the-art results on a variety of benchmark datasets, including the ImageNet dataset. CNNs are also being used for other tasks such as object detection, semantic segmentation, and natural language processing.
Here is a brief explanation of each option:
- Option A: To perform dimensionality reduction. Dimensional reduction is a technique that is used to reduce the number of features in a dataset. This can be useful for improving the performance of machine learning models. However, CNNs are not typically used for dimensionality reduction.
- Option B: To model sequential data. Sequential data is data that is ordered in time. Examples of sequential data include time series data and text data. CNNs can be used to model sequential data, but they are not typically the best choice for this task. Recurrent neural networks (RNNs) are a type of neural network that is better suited for modeling sequential data.
- Option C: To process grid-like data structures. Grid-like data structures are data that is organized in a two-dimensional grid. Examples of grid-like data include images and maps. CNNs are well-suited for processing grid-like data structures. This is because CNNs have a convolution operation that is specifically designed for processing grid-like data.
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