In this video, I show how to create a simple Convolutional Neural Network for image classification using Python and PyTorch. We start by loading the CIFAR-10 dataset, which contains common image categories such as cars, birds, cats, dogs, ships, and trucks. Then we prepare the images with transformations, create data loaders, and define the CNN architecture step by step. The model uses convolutional layers to detect visual patterns, activation functions to learn non-linear features, pooling layers to reduce image size, and fully connected layers to make the final classification. This is a beginner-friendly introduction to building a CNN for computer vision and deep learning projects.
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