Hidden layer

As explained in Data Without Labels

A hidden layer is an intermediate neural-network layer whose activations are not directly observed as inputs or outputs.

A hidden layer is an intermediate neural-network layer whose activations are not directly observed as inputs or outputs. It receives signals from the preceding layer and passes processed signals to the next layer. section 8.2.3

Hidden layers perform the network’s internal transformation work:

• Process raw inputs through neurons, weights, bias terms, and activation functions. section 8.3.2
• Extract attributes and features that support later decision-making. section 8.2.3
• Appear in networks ranging from a few layers to hundreds, depending on the problem. section 8.2.3
• Pass information sequentially from the input layer toward the output layer during feed-forward propagation. section 8.3.2

Face identification

A face-recognition network receives images through its input layer. Hidden layers process facial attributes and features, then the output layer produces a probability that the face belongs to a particular person. section 8.3.2

Figure 8.4 A typical neural network with neurons and input, hidden, and output layers

Use the network diagram to trace connections among input, hidden, and output layers. section 8.2.3

How Data Without Labels covers it

How deep it goes

Explains layer position, sequential flow, activations, weights, biases, and feature extraction, but not specialized connection exceptions.

Examples

Uses image classification, object identification, face recognition, and RBM layers that capture increasingly abstract features.

What it assumes

Assumes familiarity with input data and predictions while introducing layers, neurons, weights, biases, activations, and feature extraction.

What you can do after

Evaluate/understand how neural networks transform inputs through intermediate layers before producing predictions.

How it connects

Connects hidden layers to neural networks, input and output layers, activation functions, weights, bias terms, feed-forward propagation, and feature extraction.

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  Inside Deep Learning: Math, Algorithms, ModelsA network layer between input and output that transforms representations before later computation, here before attention is applied.

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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 Data Without Labels. The section links point to the parts of the book it draws on.