Depth
Neural-network depth is the number of successive computational layers in a neural-network architecture.
Grokking Machine Learning
The number of neural-network layers excluding the input layer.
- How it is used
- Defines a neural network’s architecture by reporting how many processing layers follow its input layer.
- How deep it goes
- Explains the counting rule and distinguishes hidden, output, input, and bias nodes without deriving network internals.
- Examples
- Uses a network sized 4-5-3-1 and diagrams showing increasingly complex classifier boundaries across hidden layers.
- What you can do after
- Evaluate neural-network diagrams by identifying their layers and counting depth correctly.
Deep Learning with PyTorch, Second Edition
The extent to which a neural network has many stacked layers or sequential operations, increasing representational complexity.
- How it is used
- Uses depth to increase representational complexity and process hierarchical visual information.
- How deep it goes
- Explains depth through intuition, sequential operations, gradient difficulties, and PyTorch architectures with up to 100 residual blocks.
- Examples
- Uses person-recognition hierarchies, three-layer NetDepth and NetRes models, residual blocks, and a 100-block network.
- What you can do after
- Build deeper convolutional networks by stacking layers or repeating residual blocks in PyTorch.
Read how Deep Learning with PyTorch, Second Edition explains depth →
Grokking AI Algorithms, Second Edition
The number of layers is the count of successive computational layers in a neural-network architecture.
- How it is used
- Uses depth to configure neural-network architectures and represent increasingly complex relationships or reasoning chains.
- How deep it goes
- Explains depth through hidden-layer abstractions and Transformer steps, but defers formal optimization or theoretical analysis.
- Examples
- Uses simple classification boundaries, multilayer ANN architectures, and toy-versus-real-world Transformer configurations.
- What you can do after
- Evaluate candidate layer counts through architecture design, experimentation, and iterative improvement.
Read how Grokking AI Algorithms, Second Edition explains depth →
Graph Neural Networks in Action
The number of computational layers in a neural network, especially the number of graph-processing layers in a graph neural network.
- How it is used
- Uses depth to tune GNN receptive fields and refine GraphSAGE model performance.
- How deep it goes
- Defines layers as graph hops, compares 2, 10, and 50 layers, and explains under-reaching, over-smoothing, overfitting, and complexity.
- Examples
- Compares 2-, 10-, and 50-layer training curves and implements two-layer GraphSAGE models with configurable aggregation and dropout.
- What you can do after
- Evaluate GNN depth by comparing training and validation behavior while configuring GraphSAGE layers for experiments.
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.
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