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| Both sides previous revisionPrevious revisionNext revision | Previous revision | ||
| data_mining:neural_network:autoencoder [2017/07/30 17:03] – [Autoencoder] phreazer | data_mining:neural_network:autoencoder [2017/07/30 18:02] (current) – [Autoencoder] phreazer | ||
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| ====== Autoencoder ====== | ====== Autoencoder ====== | ||
| - | Loss as evaluation metric. | + | * Unsupervised learning: Feature extraction, Generative models, Compression, |
| + | * Loss as evaluation metric | ||
| + | * Difference to RBM: Deterministic approach (not stochastic). | ||
| + | * Encoder compresses to few dimensions, Decoder maps back to full dimensionality | ||
| + | * Building block for deep belief networks | ||
| ===== Comparison with PCA ===== | ===== Comparison with PCA ===== | ||