Building Applications with Large Language Models : Techniques, Implementation, and Applications / by Bhawna Singh.

Format:

Book

Author/Creator:

Singh, Bhawna.

Series:

Professional and Applied Computing Series

Language:

English

Subjects (All):

Artificial intelligence.

Machine learning.

Python (Computer program language).

Natural language processing (Computer science).

Artificial Intelligence.

Machine Learning.

Python.

Natural Language Processing (NLP).

Local Subjects:

Artificial Intelligence.

Machine Learning.

Python.

Natural Language Processing (NLP).

Physical Description:

1 online resource (289 pages)

Edition:

1st ed. 2024.

Place of Publication:

Berkeley, CA : Apress : Imprint: Apress, 2024.

Summary:

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. 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 .

Contents:

Chapter 1: Introduction to Large Language Models

Chapter 2: Understanding Foundation Models

Chapter 3: Adapt with Fine-tuning

Chapter 4: The Magic of Prompt Engineering

Chapter 5: Stop Hallucination with RAG

Chapter 6: Evaluation of LLM

Chapter 7: Tools and Frameworks for Development

Chapter 8: Run in Production.-Chapter 9: The Ethical Dilemma

Chapter 10: Future of AI.

Notes:

Includes index.

Description based on publisher supplied metadata and other sources.

ISBN:

9798868805691

OCLC:

1478134556