Convolutional Neural Networks Explained: How It Works and How Kernels Create Feature Maps
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YouTubeCode Monarch
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 ...
41.2K viewsSep 17, 2024
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Visualisation of how Convolutional Neural Network works. A Convolutional Neural Network (CNN) operates by applying filters to input data, extracting features through convolution and pooling layers. These filters systematically scan the input, detecting patterns like edges. The network learns these filters during training, enabling it to recognize complex features in images. By combining convolution and pooling layers, CNNs create feature maps that highlight detected features. This process allows
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