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Prior to 2012, image classification algorithms relied heavily on hand-engineered features and shallow machine learning models (such as SVMs). While Convolutional Neural Networks had existed since the 1990s (e.g., LeNet-5), they were limited by the size of datasets and computational power. AlexNet changed this paradigm by utilizing a deep architecture trained on a massive dataset (ImageNet) using GPU acceleration. Its success sparked the current wave of AI research and commercial application. alexxavice

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The switch from Tanh/Sigmoid to ReLU was a major factor in AlexNet’s speed. It allowed the network to converge much faster, enabling the training of deeper networks that were previously too slow to optimize. AlexNet changed this paradigm by utilizing a deep

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