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Autoencoders and Principal Component Analysis (PCA) are both techniques for dimensionality reduction, with autoencoders being nonlinear and capable of capturing complex relationships, while PCA is linear and interpretable. Autoencoders consist of an encoder and decoder that compress and reconstruct data, whereas PCA transforms data into orthogonal axes to maximize variance. Each method has its advantages and limitations, with PCA being suitable for simple, linear data and autoencoders excelling in complex,

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