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CNNs power tasks like image classification and face recognition by extracting features through convolution and pooling layers. –
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RNNs process sequences by using hidden states to retain information from past inputs, ideal for time series and language data.–
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LSTMs overcome long-term dependency issues, making them perfect for speech recognition and sequential data tasks.
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GANs generate realistic images and audio by training a generator and discriminator in a competitive setup.
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Transformers process sequences efficiently with self-attention, enabling advanced natural language understanding.
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Autoencoders compress and reconstruct data, useful for denoising and feature extraction in unsupervised learning.
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DBNs extract features and reduce dimensionality through layer-by-layer training with Restricted Boltzmann Machines.