Autoencoder

As explained in Delivering Machine Learning Projects: From design to deployment

An autoencoder is a neural network that learns to encode inputs into a lower-dimensional representation and reconstruct the original inputs.

How Delivering Machine Learning Projects: From design to deployment covers it

How deep it goes

Explains the operational use of reconstruction errors but does not detail architecture, training procedure, or tuning.

Examples

Uses historical regional news articles and per-article reconstruction errors as the concrete detection example.

What you can do after

Evaluate anomalous articles by measuring their reconstruction errors against a learned regional baseline.

How it connects

Connects anomaly detection with reconstruction error, feature engineering, model selection, and regional training pipelines.

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Also explained in

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  Inside Deep Learning: Math, Algorithms, ModelsA neural network trained to reconstruct its input, typically by compressing it into a lower-dimensional latent representation and decoding it back.
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  Outlier Detection in PythonAn autoencoder is a neural network trained to encode an input into a representation and reconstruct the original input.
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  Math and Architectures of Deep LearningA neural network model trained to compress inputs into a latent representation and reconstruct the original inputs from that representation.
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  Data Without LabelsAn autoencoder is a neural-network model that learns to encode data into a lower-dimensional representation and reconstruct the original input from it.
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  Evolutionary Deep Learning: Genetic algorithms and neural networksAn autoencoder is a neural network that learns to encode inputs into a representation and decode that representation to reconstruct the inputs.
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  Learn Generative AI with PyTorchA neural network that learns to compress inputs into a latent representation and reconstruct them, used here for digit-image generation and reconstruction.
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  Graph Neural Networks in ActionAn autoencoder is a neural network that encodes inputs into latent representations and decodes those representations to reconstruct the inputs.
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  Financial AI in PracticeAn autoencoder is a neural network trained to reconstruct its input, typically by encoding it into a lower-dimensional representation and decoding it.
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  Deep Learning with R, Third EditionAn autoencoder is a neural-network model that learns to encode inputs into representations and decode those representations back into approximations of the inputs.
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  Deep Learning with Python, Third EditionA neural network trained to reconstruct its input, often by compressing and then decoding it, used here for denoising images.
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  liveBookmanning.com
  Introduction to Generative AI, Second EditionAn autoencoder is a neural network that learns to encode data into a compressed representation and reconstruct the original data from it.

Compare how 12 books explain autoencoder →

Related concepts

• encoderhas part · An encoder is a component that transforms input data into an encoded representation for subsequent processing, reconstruction, comparison, or transmission.
• decoderhas part · A decoder is a component that maps encoded data or representations to decoded values, reconstructed data, or generated output.
• variational autoencoderincludes · A variational autoencoder is a generative neural network that learns a probabilistic latent representation of data and decodes samples from it to reconstruct or generate data.
• deep learningkind of · Deep learning is a machine-learning approach that uses neural networks with multiple computational layers to learn representations from data.
• neural networkkind of · A neural network is a computational model of interconnected, parameterized units that learns transformations from inputs to outputs.
• Kerasuses · Keras is an open-source, high-level deep-learning API for building, training, evaluating, and deploying neural-network models.

This explainer was generated by Manning's knowledge graph from the text of Delivering Machine Learning Projects: From design to deployment. The section links point to the parts of the book it draws on.