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What are Convolutional Neural Networks (CNNs)?
Convolutional Neural Networks (CNNs) are a specialized type of neural network designed to process and analyze structured grid-like data, such as images or sequences. CNNs have revolutionized computer vision tasks, achieving remarkable performance in image classification, object detection, and image segmentation.
The key feature of CNNs is the use of convolutional layers. These layers apply filters, also known as kernels or feature detectors, to the input data. Each filter detects specific local patterns or features, such as edges, corners, or textures, by convolving across the input. Training a CNN involves showing it many labeled images and letting it learn from them. The network adjusts its internal settings (called weights) to get better at recognizing the right features. By using multiple layers of filters, CNNs can recognize more complex features. They start with simple features like edges and gradually learn more advanced ones like shapes and objects. CNNs are great at capturing spatial relationships in images. They can understand that certain features are usually found in specific areas of an image, making CNNs computationally efficient and capable of handling large-scale datasets.
CNNs have achieved remarkable success in various computer vision tasks. For example, in image classification, CNNs can accurately classify images into different classes, such as cats or dogs, based on learned features. In object detection, CNNs can detect and localize multiple objects within an image. In image segmentation, CNNs can classify and assign a label to each pixel, enabling precise delineation of object boundaries.
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