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What Is a CNN?
A neural network designed to understand images.
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AiTechWorlds
AiTechWorlds
Convolutional Neural Networks (CNNs) are deep learning models built for images. This visual guide explains convolutions, filters, feature maps, pooling, and how CNNs learn to recognize edges, shapes, and objects.
A neural network designed to understand images.
Images have too many pixels for dense layers alone.
Pixels become grids of values.
A filter slides across the image detecting features.
Small matrices that detect edges and textures.
Outputs showing where features appear.
Control how filters move and edges are handled.
Adds non-linearity after convolution.
Shrink maps while keeping key info.
Keep the strongest signal in each region.
Early layers find edges; deep layers find objects.
Combine features for the final prediction.
Backpropagation tunes the filters.
They learn features automatically from data.
LeNet, AlexNet, ResNet, and more.
Reuse pretrained CNNs for new tasks.
CNNs also work on audio and signals.
Need lots of data and compute.
Vision transformers now rival CNNs.
Train a small CNN on an image dataset.
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