Transformer architecture

As explained in Grokking AI Applications

A transformer is a neural-network architecture for sequential data that uses attention to relate elements of an input sequence.

A transformer is a neural-network architecture for sequential data, especially text. It uses attention to relate elements of an input sequence, helping the model focus on the most relevant words while accounting for their positions. section 1.6

A useful mental model is a layered sequence-processing system:

• Represent the input as tokens and embeddings. section 1.7
• Process those representations through layers of interconnected neural-network nodes. section 1.6
• Use attention to emphasize relationships between the most relevant parts of the sequence. section 1.6
• Generate text one token at a time after large-scale pretraining. section 1.7

Predicting a capital

For “What is the capital of Italy?”, attention can emphasize “Italy” rather than words such as “what” or “is”, supporting the prediction “Rome.” section 1.6

Figure 1.11 How a GPT architecture generates sentences

Use the architecture diagram to trace how a GPT system generates sentences from its processed sequence. section 1.6

How Grokking AI Applications covers it

How deep it goes

Defines attention and layered processing with an example, but stops short of mathematical or implementation-level internals.

Examples

Uses GPT-3, the question about Italy's capital, token-by-token generation, and diagrams of GPT architecture and training.

What it assumes

Introduces neural networks, deep learning, tokens, embeddings, and attention as the foundation for understanding transformers.

What you can do after

Understand how attention and layered neural networks support GPT-style sequence processing and text generation.

How it connects

Connects transformers with neural networks, deep learning, attention, embeddings, pretraining, fine-tuning, and context windows.

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Also explained in

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  Learn Generative AI with PyTorchThe internal design and structural organization of a Transformer model built around decoder blocks or self-attention components.
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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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  How Large Language Models WorkA transformer is a neural-network architecture that uses attention mechanisms to process relationships among elements in a sequence.
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  AI-Powered SearchA transformer is a neural-network architecture that models relationships among sequence elements using attention and can encode and decode 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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  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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  Grokking AI Algorithms, Second EditionThe Transformer is a neural-network architecture based primarily on attention mechanisms for processing sequences and modeling context.
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  Hugging Face in ActionThe Transformer architecture is a neural-network architecture based on attention mechanisms for processing sequences and other structured inputs.
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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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  Build a Website with ChatGPTA transformer is a neural-network architecture that processes sequences using attention mechanisms to model relationships among their components.
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  Data Analysis with LLMsThe Transformer is a neural-network architecture designed to model relationships in sequences, especially sequences with variable-length inputs or outputs.
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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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  Coding with AIA Transformer is a neural-network architecture that processes sequence elements using attention and can process them in parallel.
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  Optimization Algorithms: AI techniques for design, planning, and control problemsA transformer architecture is a neural-network architecture that processes sequences primarily through attention mechanisms rather than recurrence.
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  Essential GraphRAGA transformer architecture is a neural-network architecture that processes sequences using attention-based representations and supports efficient language modeling.

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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 Grokking AI Applications. The section links point to the parts of the book it draws on.