How deeply does Grokking AI Algorithms, Second Edition cover depth?

Explains depth through hidden-layer abstractions and Transformer steps, but defers formal optimization or theoretical analysis.

Depth

As explained in Grokking AI Algorithms, Second Edition

The number of layers is the count of successive computational layers in a neural-network architecture.

Depth is the number of successive computational layers in a neural-network architecture. In an artificial neural network, adding hidden layers lets the model build increasingly high-level abstractions and represent more complex classification boundaries. section 9.13 section 11.11

Depth matters because each layer adds another transformation:

Shallow networks can represent simple relationships, such as a fairly simple nonlinear boundary. section 9.13
Deeper networks can combine earlier outputs into higher-level abstractions for complex functions across many dimensions. section 9.13
Choosing depth usually requires experimentation and iterative improvement rather than a fixed universal value. section 9.13
Architecture setup defines the hidden-layer count alongside nodes, activation functions, inputs, outputs, and initial weights. section 9.6

Depth in a toy language model

A Transformer with two layers performs two successive processing steps in its reasoning chain. A larger model with twelve layers can perform a longer sequence of such steps. Depth is only one architectural choice: context window, embedding width, attention heads, and feed-forward size also affect the design. section 11.11

Table 11.7 The hyperparameters for the Transformer framework

Hyperparameter	Rule of thumb	Toy LLM example	Real-world LLM
Vocabulary size (V)	Based on training data	34 sentence pieces	50,000 (more than 250,000 sentence pieces
Context window (T)	Longest prompt you need, rounded to power of 2, that still fits batch on a GPU	32 tokens	1,024 (more than 32,000 tokens)
Width (d)	Start near 4√Vocabulary size	4 channels	768 (more than 20,000 channels)
Depth (L)	1–2 layers per 100,000 characters of training text	2 layers	12 (more than 120 layers
Attention heads (h)	h = Width ÷ 64 (round to even)	1 head	12 (more than 128 heads
Feed-forward size (d_n)	d_n = 4 × Width is a safe default	Layer 1: 8 neurons Layer 2: 4 neurons	3,027 (more than 80,000 neurons)

Compare toy and real-world Transformer depths alongside context windows, embedding widths, attention heads, and feed-forward sizes. section 11.11

How Grokking AI Algorithms, Second Edition covers it

Cover of Grokking AI Algorithms, Second Edition

liveBook manning.com

Read Grokking AI Algorithms, Second Edition on liveBook Get the book at manning.com

Also explained in

liveBook manning.com

Grokking Machine LearningThe number of neural-network layers excluding the input layer.
liveBook manning.com

Deep Learning with PyTorch, Second EditionThe extent to which a neural network has many stacked layers or sequential operations, increasing representational complexity.
liveBook manning.com

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.

Compare how 4 books explain depth →

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 AI Algorithms, Second Edition. The section links point to the parts of the book it draws on.