Hidden layer

As explained in Natural Language Processing in Action, Second Edition

A hidden layer is an intermediate neural-network layer whose activations are not directly exposed as the model's input or final output.

A hidden layer turns each processed token into an internal activation pattern rather than exposing the original text directly. In an RNN, the tensor h or hidden stores these activations as an evolving encoding of the meaning gathered so far. section 8.1.2

A paint roller for meaning

Imagine rolling paint across a sentence: the letters become a compact, blurry smudge. Each new token overwrites the hidden activations, blending the latest information with what the RNN has already processed. section 8.1.2

Figure 8.4 Gathering up meaning into one spot

Use the figure to visualize how a sequence’s meaning is gathered into one hidden representation. section 8.1.2

How Natural Language Processing in Action, Second Edition covers it

How deep it goes

Explains the h tensor, activation updates, and compact encodings, but defers the underlying neuron mathematics.

Examples

Uses a paint-smudge analogy and visualizes gathering token meaning into one representation.

What you can do after

Evaluate/understand how an RNN preserves and updates a compact representation while reading a token sequence.

How it connects

Connects hidden layers with RNNs, embeddings, encoding vectors, tokens, and natural language processing pipelines.

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Related concepts

• neural network layerkind of · A neural network layer is a computational stage or component that transforms input representations into outputs within a neural network.
• deep learningpart of · Deep learning is a machine-learning approach that uses neural networks with multiple computational layers to learn representations from data.
• language modelpart of · A language model is a computational model that represents patterns in linguistic tokens to estimate sequence probabilities and predict or generate language from context.
• neural networkpart of · A neural network is a computational model of interconnected, parameterized units that learns transformations from inputs to outputs.
• recurrent neural networkpart of · A recurrent neural network is a neural network that processes sequences by updating an internal state across successive inputs, allowing earlier inputs to influence later computations.
• dropoutused by · Dropout is a neural-network regularization technique that randomly suppresses units, connections, or activations during training to reduce overfitting.

This explainer was generated by Manning's knowledge graph from the text of Natural Language Processing in Action, Second Edition. The section links point to the parts of the book it draws on.