LLM Design Patterns | A Practical Guide to Building Robust and Efficient AI Systems
Lingua: inglese
Editore: Packt Publishing, 2025
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- Nuovo
Da: preigu, Osnabrück, Germaniapreigu
Venditore AbeBooks dal 5 agosto 2024
Condizione: Nuovo
EUR 70,30
Quantità: 5 disponibili
Aggiungi al carrelloDescrizione dell’articolo da parte del venditore
LLM Design Patterns | A Practical Guide to Building Robust and Efficient AI Systems | Ken Huang | Taschenbuch | Englisch | 2025 | Packt Publishing | EAN 9781836207030 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.
Codice articolo 133481281
- Titolo
- LLM Design Patterns | A Practical Guide to Building Robust and Efficient AI Systems
- Autore
- Ken Huang
- Editore
- Packt Publishing
- Anno di pubblicazione
- 2025
- Condizione
- Neu
- Rilegatura
- Taschenbuch
- Lingua
- inglese
- ISBN 10
- 1836207034
- ISBN 13
- 9781836207030
- Peso dell'articolo
- 992 grammi
- Dimensioni
- 235 x 191 x 29 mm
- Cataloghi dei venditori
- Bücher
Explore reusable design patterns, including data-centric approaches, model development, model fine-tuning, and RAG for LLM application development and advanced prompting techniques
Free with your book: PDF Copy, AI Assistant, and Next-Gen Reader
Key Features
- Learn comprehensive LLM development, including data prep, training pipelines, and optimization
- Explore advanced prompting techniques, such as chain-of-thought, tree-of-thought, RAG, and AI agents
- Implement evaluation metrics, interpretability, and bias detection for fair, reliable models
Book Description
This practical guide for AI professionals enables you to build on the power of design patterns to develop robust, scalable, and efficient large language models (LLMs). Written by a global AI expert and popular author driving standards and innovation in Generative AI, security, and strategy, this book covers the end-to-end lifecycle of LLM development and introduces reusable architectural and engineering solutions to common challenges in data handling, model training, evaluation, and deployment.
You’ll learn to clean, augment, and annotate large-scale datasets, architect modular training pipelines, and optimize models using hyperparameter tuning, pruning, and quantization. The chapters help you explore regularization, checkpointing, fine-tuning, and advanced prompting methods, such as reason-and-act, as well as implement reflection, multi-step reasoning, and tool use for intelligent task completion. The book also highlights Retrieval-Augmented Generation (RAG), graph-based retrieval, interpretability, fairness, and RLHF, culminating in the creation of agentic LLM systems.
By the end of this book, you’ll be equipped with the knowledge and tools to build next-generation LLMs that are adaptable, efficient, safe, and aligned with human values.
What you will learn
- Implement efficient data prep techniques, including cleaning and augmentation
- Design scalable training pipelines with tuning, regularization, and checkpointing
- Optimize LLMs via pruning, quantization, and fine-tuning
- Evaluate models with metrics, cross-validation, and interpretability
- Understand fairness and detect bias in outputs
- Develop RLHF strategies to build secure, agentic AI systems
Who this book is for
This book is essential for AI engineers, architects, data scientists, and software engineers responsible for developing and deploying AI systems powered by large language models. A basic understanding of machine learning concepts and experience in Python programming is a must.
Table of Contents
- Introduction to LLM Design Patterns
- Data Cleaning for LLM Training
- Data Augmentation
- Handling Large Datasets for LLM Training
- Data Versioning
- Dataset Annotation and Labeling
- Training Pipeline
- Hyperparameter Tuning
- Regularization
- Checkpointing and Recovery
- Fine-Tuning
- Model Pruning
- Quantization
- Evaluation Metrics
- Cross-Validation
- Interpretability
- Fairness and Bias Detection
- Adversarial Robustness
- Reinforcement Learning from Human Feedback
- Chain-of-Thought Prompting
- Tree-of-Thoughts Prompting
- Reasoning and Acting
- Reasoning WithOut Observation
- Reflection Techniques
- Automatic Multi-Step Reasoning and Tool Use
- Retrieval-Augmented Generation
- Graph-Based RAG
- Advanced RAG
- Evaluating RAG Systems
- Agentic Patterns
"Riassunto" può appartenere a un’altra edizione di questo titolo.
preigu
Osnabrück, Germania
Venditore AbeBooks dal 5 agosto 2024
Tariffe di spedizione da Germania a U.S.A.
| Articolo | Da 60 a 60 giorni lavorativi | Da 60 a 60 giorni lavorativi |
|---|---|---|
| Primo articolo | EUR 70,00 | EUR 70,00 |
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