Convolutional Neural Networks revolutionized computer vision and image processing. Convolution layers apply filters to detect features like edges and textures. Padding preserves spatial dimensions after convolution. Stride controls filter movement step size. Pooling layers reduce spatial dimensions and computation. Max pooling retains strongest features, average pooling smooths. Activation functions like ReLU introduce non-linearity. Dropout prevents overfitting by randomly disabling neurons. Batch normalization stabilizes training and accelerates convergence. Fully connected layers perform high-level reasoning. learning adapts pre-trained models like VGG and ResNet. Data augmentation expands training data with transformations. Image classification assigns images to predefined categories. Object detection with YOLO and SSD identifies object locations. Semantic segmentation classifies each pixel. U-Net architecture excels at medical image segmentation. Training requires GPUs for practical speed. Frameworks like TensorFlow and PyTorch simplify CNN implementation. Hyperparameter tuning optimizes learning rate, batch size, architecture. CNNs represent one of deep learning's greatest successes.

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