# HandsOnLLM/Hands-On-Large-Language-Models

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21,811 stars · 5,134 forks · Jupyter Notebook · apache-2.0

## Links

- GitHub: https://github.com/HandsOnLLM/Hands-On-Large-Language-Models
- Homepage: https://www.llm-book.com/
- awesome-repositories: https://awesome-repositories.com/repository/handsonllm-hands-on-large-language-models.md

## Topics

`artificial-intelligence` `book` `large-language-models` `llm` `llms` `oreilly` `oreilly-books`

## Description

This project is a comprehensive educational resource designed to help developers understand the fundamental concepts and architectural patterns behind transformer-based artificial intelligence systems. It serves as a technical reference for exploring the design principles, implementation details, and operational mechanics of large-scale neural networks used in generative tasks.

The repository provides structured documentation and visual guides that break down the internal structures of modern large language models. By examining the design choices and mathematical components of these systems, users can gain insight into how transformer-based models process data and generate sequences.

The content covers the core mechanics of sequence modeling, including self-attention mechanisms, multi-head attention, and vector-space semantic embeddings. It also addresses the training processes and optimization techniques required to build and analyze these complex machine learning structures. The material is presented through a collection of tutorials and visual resources intended to clarify the internal operations of generative systems.

## Tags

### Education & Learning Resources

- [Transformer Tutorials](https://awesome-repositories.com/f/education-learning-resources/technical-domain-education/ai-machine-learning-education/neural-network-architectures/recurrent-neural-network-tutorials/transformer-tutorials.md) — Provides comprehensive educational guides and visual documentation explaining the internal mechanics and architectural patterns of transformer-based generative models.
- [Large Language Model Tutorials](https://awesome-repositories.com/f/education-learning-resources/educational-resources/reference-and-media/tutorials-media-curated-lists/technical-tutorials/machine-learning-ai/large-language-model-tutorials.md) — Collects tutorials and visual resources to explain the operational mechanics of modern language models.
- [AI & Machine Learning Education](https://awesome-repositories.com/f/education-learning-resources/technical-domain-education/ai-machine-learning-education.md) — Serves as a comprehensive educational resource for understanding the foundations of generative AI.
- [Machine Learning Fundamentals](https://awesome-repositories.com/f/education-learning-resources/technical-domain-education/ai-machine-learning-education/machine-learning-fundamentals.md) — Provides a comprehensive guide to the fundamental concepts of generative artificial intelligence.

### Artificial Intelligence & ML

- [Generative AI Learning Resources](https://awesome-repositories.com/f/artificial-intelligence-ml/generative-ai-learning-resources.md) — Acts as a primary learning resource for developers studying transformer-based AI systems.
- [Neural Network Visualizers](https://awesome-repositories.com/f/artificial-intelligence-ml/neural-network-visualizers.md) — Offers conceptual breakdowns and visual guides for exploring transformer neural network architectures.
- [Architecture Visualizers](https://awesome-repositories.com/f/artificial-intelligence-ml/model-visualization-tools/architecture-visualizers.md) — Provides visual guides and diagrams to explain the internal architecture of large language models. ([source](https://cdn.jsdelivr.net/gh/HandsOnLLM/Hands-On-Large-Language-Models@main/README.md))
- [Transformer Language Models](https://awesome-repositories.com/f/artificial-intelligence-ml/transformer-language-models.md) — Covers the implementation and training processes required to build transformer-based sequence models.
- [Attention Mechanisms](https://awesome-repositories.com/f/artificial-intelligence-ml/attention-mechanisms.md) — Provides a core explanation of the self-attention mechanism and its role in contextual weighting.
- [Multi-Head Attention Mechanisms](https://awesome-repositories.com/f/artificial-intelligence-ml/multi-head-attention-mechanisms.md) — Explains how multi-head attention splits representations to capture diverse contextual relationships.
- [Attention Scoring Functions](https://awesome-repositories.com/f/artificial-intelligence-ml/attention-scoring-functions.md) — Covers the scoring functions used to calculate dynamic relevance weights between tokens.
- [Sequence Learning Models](https://awesome-repositories.com/f/artificial-intelligence-ml/sequence-learning-models.md) — Explains the use of transformer-based architectures for sequence modeling tasks.
- [Vector Embeddings](https://awesome-repositories.com/f/artificial-intelligence-ml/vector-embeddings.md) — Details the mapping of text tokens into high-dimensional semantic vector spaces.
- [Autoregressive Models](https://awesome-repositories.com/f/artificial-intelligence-ml/autoregressive-models.md) — Explains the autoregressive generation mechanism used to predict sequential tokens in transformer models.
- [Token Prediction](https://awesome-repositories.com/f/artificial-intelligence-ml/text-generation-strategies/token-prediction.md) — Details the iterative token prediction process based on probability distributions.

### Content Management & Publishing

- [Technical Documentation](https://awesome-repositories.com/f/content-management-publishing/documentation-knowledge-management/technical-documentation.md) — Provides structured technical documentation on the design principles of large-scale neural networks.

### Repository Format

- [Awesome List](https://awesome-repositories.com/f/repository-format/awesome-list.md) — A community-curated directory that catalogs and links out to other open-source projects, rather than a standalone tool you run yourself.

### System Administration & Monitoring

- [Neural Network Layer Stacks](https://awesome-repositories.com/f/system-administration-monitoring/diagnostic-tools/diagnostics/telemetry-and-log-collectors/output-capture-utilities/model-layer-capture-utilities/neural-network-layer-stacks.md) — Describes the hierarchical stacking of transformer layers to extract complex statistical patterns.