Hidden layer

As explained in Grokking Machine Learning

A neural-network layer positioned between the input and output layers whose units transform intermediate representations.

A hidden layer sits between a neural network’s input and output layers. Its units form intermediate stages, and successive layers can combine simpler classifier boundaries into more complex ones. section 10.1

A two-layer classifier

For circular-data classification, one Keras model uses 128 and 64 units with ReLU activations, dropout between the hidden layers, and a two-unit softmax output. section 10.3

Figure 10.21 The architecture that we will use to classify our dataset. It contains two hidden layers: one of 128 and one of 64 nodes. The activation function between them is a ReLU, and the final activation function is a softmax.

View the architecture diagram showing the two hidden layers and their sizes. section 10.3

How Grokking Machine Learning covers it

How deep it goes

Defines layer placement and sizes, then illustrates how successive layers combine classifier boundaries into more complex ones.

Examples

Uses an alien-classification example and Keras models for circular data and handwritten-digit recognition.

What you can do after

Apply hidden layers via Keras Dense layers, choosing sizes and activations for a classifier architecture.

How it connects

Connects hidden layers to neural-network architecture, perceptrons, classifiers, activation functions, and dropout.

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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 Grokking Machine Learning. The section links point to the parts of the book it draws on.