Название: Building Applications with Large Language Models: Techniques, Implementation, and ApplicationsАвтор: Bhawna Singh
Издательство: Apress
Год: 2024
Страниц: 289
Язык: английский
Формат: pdf
Размер: 16.5 MB
This book delves into a broad spectrum of topics, covering the foundational aspects of Large Language Models (LLMs) such as PaLM, LLaMA, BERT, and GPT, among others.
The book takes you through the complexities involved in creating and deploying applications based on LLMs, providing you with an in-depth understanding of the model architecture. You will explore techniques such as fine-tuning, prompt engineering, and retrieval augmented generation (RAG). The book also addresses different ways to evaluate LLM outputs and discusses the benefits and limitations of large models. The book focuses on the tools, techniques, and methods essential for developing Large Language Models. It includes hands-on examples and tips to guide you in building applications using the latest technology in Natural Language Processing (NLP). It presents a roadmap to assist you in navigating challenges related to constructing and deploying LLM-based applications.
By the end of the book, you will understand LLMs and build applications with use cases that align with emerging business needs and address various problems in the realm of language processing.
As a beginner, it can be difficult to understand the technical jargon, complex architecture, and sheer size of these models. Playing around new AI-based tools is fun, but how can you build a tool of your own? How can the businesses harness the power of this technology to build and deploy a real-world application? What is the other side of the technology that extends beyond the technicalities of these models?
This book is your guide in understanding different ways in which Large Language Models, like GPT, BERT, Claude, LLaMA, etc., can be utilized for building something useful. It takes you on a journey starting from very basic, like understanding the basic models in NLP, to complex techniques, like PEFT, RAG, Prompt Engineering, etc. Throughout the book, you will find several examples and code snippets which will help you appreciate the state-of-the-art NLP models. Whether you’re a student trying to get hold of the new technology, a data scientist transitioning to the field of NLP, or simply someone who is inquisitive about Large Language Models (LLMs), this book will build your concepts and equip you with the knowledge required to start building your own applications using LLMs.
What You Will Learn:
Be able to answer the question: What are Large Language Models?
Understand techniques such as prompt engineering, fine-tuning, RAG, and vector databases
Know the best practices for effective implementation
Know the metrics and frameworks essential for evaluating the performance of Large Language Models
Who This Book Is For:
An essential resource for AI-ML developers and enthusiasts eager to acquire practical, hands-on experience in this domain; also applies to individuals seeking a technical understanding of Large Language Models (LLMs) and those aiming to build applications using LLMs.
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