A Convolutional Neural Network (CNN) is a class of deep learning algorithms that is particularly effective for processing data with a grid-like topology, such as images. CNNs are widely used in computer vision tasks, including image classification, object detection, and segmentation, among others. ### Key Components of CNNs: 1. **Convolutional Layers**: - The core building block of a CNN.
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A convolutional neural network uses local connections and weight sharing across input positions. Each learned filter detects the same local feature at multiple positions; successive activation functions and layers combine those features. Shared filters impose translation equivariance away from boundary and sampling effects, while a pooling layer can reduce spatial size.
For a standard dense-channel two-dimensional convolution with a by kernel, input channels and output channels, the parameter count including one bias per output channel isIt is independent of the image's spatial dimensions. For example, a three-by-three convolution from three channels to sixteen channels has parameters. Grouped or depthwise convolutions use different connectivity and therefore different counts.
