A convolutional neural network (CNN, or ConvNet) is a type of deep learning neural network that is primarily used for image and video recognition tasks. CNNs are designed to process and analyze data that has a grid-like structure, such as an image. The design of CNNs is inspired by the organization of the animal visual cortex, which is responsible for processing visual information in the brain.
The key feature of a CNN is its use of convolutional layers, which are designed to automatically and adaptively learn spatial hierarchies of features from input images. These layers apply a set of learnable filters to the input image, which scan the image and detect features such as edges, shapes, and textures. The resulting feature maps are then processed by pooling layers, which reduce the spatial dimensions of the feature maps while retaining the most important information.
In addition to the convolutional and pooling layers, CNNs also typically include fully connected layers, which are used to make a final prediction or classification based on the features extracted by the convolutional layers.
CNNs have been successfully applied to a wide range of computer vision tasks, such as object detection, image segmentation, and facial recognition. They have also been used in other fields such as natural language processing, speech recognition and medical imaging.
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