Könyv Mastering Large Language Models with Python Raj Arun R

Mastering Large Language Models with Python

Szerző: Raj Arun R
Nyelv: Angol
Kötés: Puha kötésű
Elérhetőség: Beszállítói készleten
Küldés 9-15 napon belül
16 309 Ft
A Comprehensive Guide to Leverage Generative AI in the Modern EnterpriseBook Description"Mastering L...

Információk a könyvről

Szerző
Nyelv
Angol
Kötés
Könyv - Puha kötésű
Kiadva
2024
oldal
554
EAN
9788197081828
ISBN
8197081824
Enbook ID
45889840
Súly
1021
Méretek
191 x 235 x 30

Teljes leírás

A Comprehensive Guide to Leverage Generative AI in the Modern Enterprise


Book Description

"Mastering Large Language Models with Python" is an indispensable resource that offers a comprehensive exploration of Large Language Models (LLMs), providing the essential knowledge to leverage these transformative AI models effectively. From unraveling the intricacies of LLM architecture to practical applications like code generation and AI-driven recommendation systems, readers will gain valuable insights into implementing LLMs in diverse projects.


Covering both open-source and proprietary LLMs, the book delves into foundational concepts and advanced techniques, empowering professionals to harness the full potential of these models. Detailed discussions on quantization techniques for efficient deployment, operational strategies with LLMOps, and ethical considerations ensure a well-rounded understanding of LLM implementation.


Through real-world case studies, code snippets, and practical examples, readers will navigate the complexities of LLMs with confidence, paving the way for innovative solutions and organizational growth. Whether you seek to deepen your understanding, drive impactful applications, or lead AI-driven initiatives, this book equips you with the tools and insights needed to excel in the dynamic landscape of artificial intelligence.


Table of Contents

1. The Basics of Large Language Models and Their Applications

2. Demystifying Open-Source Large Language Models

3. Closed-Source Large Language Models

4. LLM APIs for Various Large Language Model Tasks

5. Integrating Cohere API in Google Sheets

6. Dynamic Movie Recommendation Engine Using LLMs

7. Document-and Web-based QA Bots with Large Language Models

8. LLM Quantization Techniques and Implementation

9. Fine-tuning and Evaluation of LLMs

10. Recipes for Fine-Tuning and Evaluating LLMs

11. LLMOps - Operationalizing LLMs at Scale

12. Implementing LLMOps in Practice Using MLflow on Databricks

13. Mastering the Art of Prompt Engineering

14. Prompt Engineering Essentials and Design Patterns

15. Ethical Considerations and Regulatory Frameworks for LLMs

16. Towards Trustworthy Generative AI (A Novel Framework Inspired by Symbolic Reasoning)

      Index

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