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Deep learning is a subset of machine learning that uses multi-layer neural networks to learn hierarchical representations from data. It enables state-of-the-art performance in vision, language and speech by training large models on massive datasets. Practical use requires careful model design, data engineering and substantial compute resources.
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Deep learning uses multi-layer neural networks that learn hierarchical features from examples and produce predictions.
It grew from neural networks as data, hardware, and training made deeper models practical. Goodfellow, Bengio, and Courville consolidate theory; Wikipedia and PyTorch show context and implementation.
Early layers detect simple patterns and later layers combine them. Training sends error backward and adjusts weights; data quality and regularization shape generalization.
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Its value depends on applying it to an observable problem and checking the result.
Deep learning is strong for images, speech, and text. It needs data and compute; performance guarantees neither explainability nor robustness.
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