Mastering Large Language Models with Python: Unleash the Power of Advanced Natural Language Processing for Enterprise Innovation and Efficiency Using Large Language Models (LLMs) with Python. E-book. Formato EPUB - 9788197081804

Mastering Large Language Models with Python: Unleash the Power of Advanced Natural Language Processing for Enterprise Innovation and Efficiency Using Large Language Models (LLMs) with Python. E-book. Formato EPUB. <div><b>A Comprehensive Guide to Leverage Generative AI in the Modern Enterprise</b></div><div><br><b style="font-size: 0.9rem;">Key Features</b></div><div>? Gain a comprehensive understanding of LLMs within the framework of Generative AI, from foundational concepts to advanced applications.<br></div><div>? Dive into practical exercises and real-world applications, accompanied by detailed code walkthroughs in Python.<br></div><div>? Explore LLMOps with a dedicated focus on ensuring trustworthy AI and best practices for deploying, managing, and maintaining LLMs in enterprise settings.<br></div><div><br></div><div></div><div><b>Book Description</b><br></div><div>“<b>Mastering Large Language Models with Python</b>” 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.<br><br></div><div></div><div>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.</div><div><br></div><div></div><div><b>What you will learn</b><br></div><div>? In-depth study of LLM architecture and its versatile applications across industries. <br></div><div>? Harness open-source and proprietary LLMs to craft innovative solutions. <br></div><div>? Implement LLM APIs for a wide range of tasks spanning natural language processing, audio analysis, and visual recognition. <br></div><div>? Optimize LLM deployment through techniques such as quantization and operational strategies like LLMOps, ensuring efficient and scalable model usage. </div><div><br></div><div></div><div><b>Table of Contents</b><br></div><div>1. The Basics of Large Language Models and Their Applications<br></div><div>2. Demystifying Open-Source Large Language Models<br></div><div>3. Closed-Source Large Language Models<br></div><div>4. LLM APIs for Various Large Language Model Tasks<br></div><div>5. Integrating Cohere API in Google Sheets<br></div><div>6. Dynamic Movie Recommendation Engine Using LLMs<br></div><div>7. Document-and Web-based QA Bots with Large Language Models<br></div><div>8. LLM Quantization Techniques and Implementation<br></div><div>9. Fine-tuning and Evaluation of LLMs<br></div><div>10. Recipes for Fine-Tuning and Evaluating LLMs<br></div><div>11. LLMOps - Operationalizing LLMs at Scale<br></div><div>12. Implementing LLMOps in Practice Using MLflow on Databricks<br></div><div>13. Mastering the Art of Prompt Engineering<br></div><div>14. Prompt Engineering Essentials and Design Patterns<br></div><div>15. Ethical Considerations and Regulatory Frameworks for LLMs<br></div><div>16. Towards Trustworthy Generative AI (A Novel Framework Inspired by Symbolic Reasoning)<br></div><div>&nbsp; &nbsp; &nbsp; &nbsp;&nbsp;<b>Index</b></div><div></div><div></div>