Transformer architecture

As explained in Coding with AI

A Transformer is a neural-network architecture that processes sequence elements using attention and can process them in parallel.

A Transformer processes sequence elements with self-attention, examining relationships across an entire sequence in parallel. This architecture underpins large language models used for chatbots, translation, content generation, and coding tools. section 1.4

A Transformer turns a prompt into a representation through a few practical stages:

• Converts words into numerical identifiers that the model can process. section 1.4
• Tracks word positions so that sequence order contributes to meaning. section 1.4
• Uses self-attention to compare each word with the others and emphasize relevant relationships. section 1.4
• Processes the sequence in parallel across multiple layers, improving training efficiency and supporting very large models. section 1.4

Tracking relationships in a sentence

For “Jane helps Joe,” the Transformer represents the words, records that Jane comes first and Joe comes last, and evaluates how the words relate. The same relationship-focused processing helps interpret sentences such as “The dog chased the cat.” section 1.4

Figure 1.2 A transformer takes a sentence and analyzes word positions and relationships to try and extract meaning from the text it sees.

Use the figure to trace how word positions and relationships contribute to extracting meaning from a sentence. section 1.4

How Coding with AI covers it

How deep it goes

Explains input identifiers, positions, self-attention, parallel processing, and scaling, without detailing layer internals.

Examples

Uses “Jane helps Joe,” “The dog chased the cat,” and a figure showing positions and relationships.

What it assumes

Introduces word representation, position tracking, and attention while contrasting the architecture with earlier sequence models.

What you can do after

Evaluate/understand it by tracing how prompts become context-sensitive representations for language applications.

How it connects

Connects large language models, self-attention, sequence processing, recurrent neural networks, and long short-term memory networks.

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Also explained in

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  Deep Learning with Python, Third EditionA neural-network architecture based on attention mechanisms, used here as the core architecture for large language models.
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  Build a Text-to-Image Generator (from Scratch)A transformer architecture is a neural-network architecture that uses attention mechanisms to process relationships among elements in a sequence or other structured input.
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  Deep Learning with R, Third EditionA Transformer is a neural-network architecture designed to process sets or sequences using attention-based contextual representations.
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  Transformers in ActionA transformer is a neural network architecture that processes sequential and other structured data using attention mechanisms.
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  Deep Learning with PyTorch, Second EditionA neural network architecture for sequence modeling and content understanding based on attention rather than recurrence or convolution.
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  LLMs in ProductionA transformer architecture is a neural-network architecture that models sequence relationships primarily through attention rather than recurrence or convolution.
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  Grokking AI ApplicationsA transformer is a neural-network architecture for sequential data that uses attention to relate elements of an input sequence.
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  The AI Pocket BookA neural network architecture that contextualizes each token using surrounding tokens rather than compressing the whole prompt into one summary vector.
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  Time Series Forecasting Using Foundation ModelsA neural-network architecture built from stacked attention and feed-forward processing components, typically organized into encoder and decoder parts.
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  Machine Learning Algorithms in DepthA transformer model is a neural network architecture that uses attention-based mechanisms to model contextual relationships in structured sequences or other data.
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  Inside Deep Learning: Math, Algorithms, ModelsA sequence model built from positional encoding and multiheaded attention, used here as the standard original design for NLP and other tasks.
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  Domain-Specific Small Language ModelsA neural network architecture for sequence modeling built around self-attention and parallel processing, underlying both large and small language models.
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  Introduction to Generative AI, Second EditionA transformer is a neural-network architecture for sequence modeling that uses attention, especially self-attention, rather than recurrence.
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  Build AI-Enhanced Web AppsA transformer model is a neural-network architecture that uses attention mechanisms to model contextual relationships in sequence data.
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  Build a Large Language Model (From Scratch)a neural network architecture based on self-attention mechanisms that enables processing of sequential data and underpins many large language models.
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  Generative AI in ActionThe Transformer architecture is a neural-network architecture based primarily on attention mechanisms for processing sequences.
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Related concepts

• attentionuses · Attention is a neural-network mechanism that computes input-dependent weights to combine information according to its relevance to a computation.
• encoderhas part · An encoder is a component that transforms input data into an encoded representation for subsequent processing, reconstruction, comparison, or transmission.
• decodehas part · Decoding is the process of transforming an encoded or internal representation into an interpretable or target-form output.
• Vision Transformerincludes · A Vision Transformer is a Transformer-based neural-network architecture that processes images as sequences of tokenized regions using attention.
• model architecturekind of · The structural organization of a computational system’s components, relationships, and operations that determines how it processes information or behaves.
• GPT-2includes · GPT-2 is a family of autoregressive, decoder-only Transformer language models developed by OpenAI that generate text by predicting successive tokens from preceding context.

This explainer was generated by Manning's knowledge graph from the text of Coding with AI. The section links point to the parts of the book it draws on.