Building LLMs for Production: Enhancing LLM Abilities and Reliability with Prompting, Fine-Tuning, and RAGКНИГИ » ПРОГРАММИНГ
Название: Building LLMs for Production: Enhancing LLM Abilities and Reliability with Prompting, Fine-Tuning, and RAG Автор: Louis-Francois Bouchard, Louie Peters Издательство: Towards AI, Inc. Год: 2024 Страниц: 475 Язык: английский Формат: epub (true) Размер: 11.1 MB
“This is the most comprehensive textbook to date on building LLM applications - all essential topics in an AI Engineer's toolkit." - Jerry Liu, Co-founder and CEO of LlamaIndex
With amazing feedback from industry leaders, this book is an end-to-end resource for anyone looking to enhance their skills or dive into the world of AI and develop their understanding of Generative AI and Large Language Models (LLMs). It explores various methods to adapt "foundational" LLMs to specific use cases with enhanced accuracy, reliability, and scalability. Written by over 10 people on our Team at Towards AI and curated by experts from Activeloop, LlamaIndex, Mila, and more, it is a roadmap to the tech stack of the future.
The book aims to guide developers through creating LLM products ready for production, leveraging the potential of AI across various industries. It is tailored for readers with an intermediate knowledge of Python.
What's Inside this 470-page Book?
Hands-on Guide on LLMs, Prompting, Retrieval Augmented Generation (RAG) & Fine-tuning Roadmap for Building Production-Ready Applications using LLMs Fundamentals of LLM Theory Simple-to-Advanced LLM Techniques & Frameworks Code Projects with Real-World Applications Colab Notebooks that you can run right away Community access and our own AI Tutor
What Experts Think About The Book:
"A truly wonderful resource that develops understanding of LLMs from the ground up, from theory to code and modern frameworks. Grounds your knowledge in research trends and frameworks that develop your intuition around what's coming. Highly recommend." - Pete Huang, Co-founder of The Neuron
“This book is filled with end-to-end explanations, examples, and comprehensive details. Louis and the Towards AI team have written an essential read for developers who want to expand their AI expertise and apply it to real-world challenges, making it a valuable addition to both personal and professional libraries.” - Alex Volkov, AI Evangelist at Weights & Biases and Host of ThursdAI news
"This book is the most thorough overview of LLMs I've come across. An excellent primer for newcomers and a valuable reference for experienced practitioners." - Shaw Talebi, Founder of The Data Entrepreneurs, AI Educator and Advisor
Whether you're looking to enhance your skills or dive into the world of AI for the first time as a programmer or software student, our book is for you. From the basics of LLMs to mastering fine-tuning and RAG for scalable, reliable AI applications, we guide you every step of the way.
Chapter I Introduction to Large Language Models Chapter II LLM Architectures & Landscape Chapter III LLMs in Practice Chapter IV Introduction to Prompting Chapter V Introduction to LangChain & LlamaIndex Chapter VI Prompting with LangChain Chapter VII Retrieval-Augmented Generation Chapter VIII Advanced RAG Chapter IX Agents Chapter X Fine-Tuning Chapter XI Deployment
Скачать Building LLMs for Production: Enhancing LLM Abilities and Reliability with Prompting, Fine-Tuning, and RAG
Mastering Large Language Models with Python Название: Mastering Large Language Models with Python: Unleash the Power of Advanced Natural Language Processing for Enterprise Innovation and...
LLM Adoption in the Enterprise Название: LLM Adoption in the Enterprise: A Guide to Building Meaningful Products with Generative AI Автор: Isabelle Nguyen Издательство: O’Reilly...
LLMs in Production (MEAP v1) Название: LLMs in Production (MEAP v1) Автор: Christopher Brousseau, Matthew Sharp Издательство: Manning Publications Год: 2023 Страниц: 152 Язык:...
Навигация
Вход на сайт
Реклама
Building LLMs for Production: Enhancing LLM Abilities and Reliability with Prompting, Fine-Tuning, and RAG 
