Convolutional Neural Networks Explained: How It Works and How Kernels Create Feature Maps
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Convolutional Neural Networks Explained: How It Works and How Kernels Create Feature Maps
Ever wondered how Convolutional Neural Networks (CNNs) process data and generate feature maps? In this video, we dive into the workings of CNNs, focusing on how kernels (or filters) slide across the input layer, perform element-wise multiplications, and create feature maps. Discover how strides influence the size of these maps and follow the ...
28.9K viewsSep 17, 2024
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Artificial Intelligence | AI on Instagram: "Follow for more @getintoai Convolutions are a key operation in Convolutional Neural Networks (CNNs), designed for tasks like image processing. A convolution slides a small matrix, called a kernel or filter, over an input (like an image) to extract features like edges or textures. The kernel performs element wise multiplication with portions of input, and the results are summed to produce a feature map. CNNs stack multiple layers of these convolutions,
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