Feature Maps
In the context of deep learning, feature maps refer to the output of a layer in a neural network that corresponds to a specific set of learned features. These features are learned through the process of training the neural network on a large dataset, and are used to identify patterns and structures in the input data. Each layer in a neural network can have multiple feature maps, each of which corresponds to a different set of learned features. Feature maps are typically visualized as a grid of values, where each value corresponds to the activation of a particular neuron in the layer. The values in the feature map are used as input to the next layer in the network, and the process of learning and refining the features continues through the network.
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