How deeply does Grokking Machine Learning cover depth?

Explains the counting rule and distinguishes hidden, output, input, and bias nodes without deriving network internals.

Depth

As explained in Grokking Machine Learning

The number of neural-network layers excluding the input layer.

Depth counts a neural network’s layers, leaving out the input layer. The remaining layers include hidden layers and the output layer. section 10.1

Counting depth

A network with an input layer of size 4, hidden layers of sizes 5 and 3, and an output layer of size 1 has depth 3. Bias nodes do not increase layer size or depth. section 10.1

Use the architecture diagram to count depth:

Count every layer after the input layer, including the output layer. section 10.1
Remember that fully connected networks connect each layer to the next consecutive layer. section 10.1

How Grokking Machine Learning covers it

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Also explained in

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Deep Learning with PyTorch, Second EditionThe extent to which a neural network has many stacked layers or sequential operations, increasing representational complexity.
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Grokking AI Algorithms, Second EditionThe number of layers is the count of successive computational layers in a neural-network architecture.
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Graph Neural Networks in ActionThe number of computational layers in a neural network, especially the number of graph-processing layers in a graph neural network.

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Related concepts

neural networkconfigures · A neural network is a computational model of interconnected, parameterized units that learns transformations from inputs to outputs.
receptive fieldproduces · A receptive field is the set or region of input elements whose information can influence a particular neural-network unit, representation, or output.
over-smoothingproduces · Over-smoothing is a graph neural network failure mode in which repeated neighborhood aggregation or message passing makes node representations increasingly similar and difficult to distinguish.
hyperparameterkind of · A hyperparameter is a setting chosen outside ordinary parameter learning that controls a model, algorithm, or learning procedure.
neural network layermeasures · A neural network layer is a computational stage or component that transforms input representations into outputs within a neural network.
graph neural networkconfigures · A graph neural network is a neural-network model that learns representations or predictions from graph-structured data by combining information from connected entities.

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.