Ever wondered how large language models (LLMs) work and how they're shaping the future of artificial intelligence? Decoding Large Language Models will guide you through understanding how they work, how they're built, and how people use them in real life.
You’ll start by learning about their structure, how they're developed, and how to get hands-on with using them in various ways. Next, you’ll discover strategies for improving these models and testing them to ensure effective deployment. Packed with real-life examples, this book also covers ethical considerations, offering a balanced perspective on their societal impact. Detailed explanations will empower you to leverage and fine-tune LLMs for optimal performance. By mastering techniques in training, deploying, and scaling models, you’ll be able to tackle complex data challenges with confidence and precision. This book will eventually prepare you for future challenges in the evolving AI and NLP fields.
By the end of this book, you’ll have a solid understanding of the architecture, development, applications, and ethical use of LLMs and be up to date with emerging trends, such as GPT-5.

Osoby które kupowały "Decoding Large Language Models. A comprehensive guide to understanding, implementing, and optimizing LLMs for NLP applications", wybierały także:

• Scala for Machine Learning - Second Edition 186,88 zÅ‚, (29,90 zÅ‚ -84%)
• Data Analysis with IBM SPSS Statistics 186,88 zÅ‚, (29,90 zÅ‚ -84%)
• QlikView for Developers 186,88 zÅ‚, (29,90 zÅ‚ -84%)
• Practical Machine Learning Cookbook 186,88 zÅ‚, (29,90 zÅ‚ -84%)
• Mastering Hadoop 3 175,88 zÅ‚, (29,90 zÅ‚ -83%)

Spis treści

Decoding Large Language Models. An exhaustive guide to understanding, implementing, and optimizing LLMs for NLP applications eBook -- spis treści

• 1. LLM Architecture
• 2. How LLMs Make Decisions
• 3. The Mechanics of Training LLMs
• 4. Advanced Training Strategies
• 5. Fine-Tuning LLMs for Specific Applications
• 6. Testing and Evaluating LLMs
• 7. Deploying LLMs in Production
• 8. Strategies for Integrating LLMs
• 9. Optimization Techniques for Performance
• 10. Advanced Optimization and Efficiency
• 11. LLM Vulnerabilities, Biases, and Legal Implications
• 12. Case Studies – Business Applications and ROI
• 13. The Ecosystem of LLM Tools and Frameworks
• 14. Preparing for GPT-5 and Beyond
• 15. Conclusion and Looking Forward