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Generative AI Foundations in Python

You're reading from   Generative AI Foundations in Python Discover key techniques and navigate modern challenges in LLMs

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Product type Paperback
Published in Jul 2024
Publisher Packt
ISBN-13 9781835460825
Length 190 pages
Edition 1st Edition
Languages
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Author (1):
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Carlos Rodriguez Carlos Rodriguez
Author Profile Icon Carlos Rodriguez
Carlos Rodriguez
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Table of Contents (13) Chapters Close

Preface 1. Part 1: Foundations of Generative AI and the Evolution of Large Language Models FREE CHAPTER
2. Chapter 1: Understanding Generative AI: An Introduction 3. Chapter 2: Surveying GenAI Types and Modes: An Overview of GANs, Diffusers, and Transformers 4. Chapter 3: Tracing the Foundations of Natural Language Processing and the Impact of the Transformer 5. Chapter 4: Applying Pretrained Generative Models: From Prototype to Production 6. Part 2: Practical Applications of Generative AI
7. Chapter 5: Fine-Tuning Generative Models for Specific Tasks 8. Chapter 6: Understanding Domain Adaptation for Large Language Models 9. Chapter 7: Mastering the Fundamentals of Prompt Engineering 10. Chapter 8: Addressing Ethical Considerations and Charting a Path Toward Trustworthy Generative AI 11. Index 12. Other Books You May Enjoy

References

This reference section serves as a repository of sources referenced within this book; you can explore these resources to further enhance your understanding and knowledge of the subject matter:

  • Gururangan, S., Marasović, A., Swayamdipta, S., Lo, K., Beltagy, I., Downey, D., & Smith, N. A. (2020). Don’t stop pretraining: Adapt language models to domains and tasks. In arXiv [cs.CL]. http://arxiv.org/abs/2004.10964/.
  • Pruksachatkun, Y., Phang, J., Liu, H., Htut, P. M., Zhang, X., Pang, R. Y., Vania, C., Kann, K., & Bowman, S. R. (2020a). Intermediate-task transfer learning with pretrained language models: When and why does it work? Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics.
  • Xie, Q., Dai, Z., Hovy, E., Luong, M.-T., & Le, Q. V. (n.d.). Unsupervised Data Augmentation for Consistency Training. Arxiv.org. Retrieved March 16, 2024, from http://arxiv.org/abs/1904.12848.
  • Anaby-Tavor, A., Carmeli...
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