About the Author
Jay Alammar
ISBN 9789355425522
Rs. 3,640
O'Reilly Media
Hands-On Large Language Models
Who it's for: Developers and AI practitioners seeking practical LLM skills
This book offers a practical guide to large language models (LLMs), focusing on building, fine-tuning, and deploying these advanced AI systems. It covers foundational natural language processing (NLP) concepts and explores transformer-based architectures like GPT, BERT, and T5. Through hands-on examples, readers will learn to train, optimize, and integrate LLMs into real-world applications, emphasizing efficiency, scalability, and responsible AI practices.Why You Should Read? Master the fundamentals of LLMs, including transformers and attention mechanisms. Gain practical experience with cutting-edge models like GPT, BERT, and T5. Learn fine-tuning, inference optimization, and deployment strategies for LLMs. Understand ethical considerations and best practices for responsible AI development.
Highlights
- Covers foundational NLP concepts and transformer architectures like GPT, BERT, and T5.
- Hands-on examples for training, optimizing, and integrating LLMs.
- Emphasizes efficiency, scalability, and responsible AI practices.
- Includes fine-tuning, inference optimization, and deployment strategies.
Themes
Available formats
1 formatPaperback
Physical
All 77 Districts COD
Nationwide cash on delivery
7-day exchange
Damage or wrong edition
Genuine print
Direct from publishers
Specifications
10 detailsAbout the author
Community Reviews
Sign in to Write a ReviewWrong edition? Want a different title?
Tell us about a book, edition or format you'd like us to stock — or flag anything off about this page.
Sign in to send us a message - it lets us reply and keeps out spam.
Sign inQuestions about this book
What does this book cover?
It covers building, fine-tuning, and deploying large language models, including foundational NLP concepts and transformer architectures like GPT, BERT, and T5.
Is this book suitable for beginners?
It is a practical guide that introduces fundamentals, making it suitable for those new to LLMs but with some programming background.
Does it include hands-on examples?
Yes, it provides hands-on examples for training, optimizing, and integrating LLMs into real-world applications.
What are the key topics?
Key topics include transformers, attention mechanisms, fine-tuning, inference optimization, deployment, and responsible AI practices.