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Generative AI Foundations in Python: Discover key techniques and navigate modern challenges in LLMs
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Generative AI Foundations in Python: Discover key techniques and navigate modern challenges in LLMs

by Carlos Rodriguez

Artificial Intelligence

Book Details

Book Title

Generative AI Foundations in Python: Discover key techniques and navigate modern challenges in LLMs

Author

Carlos Rodriguez

Publisher

Packt Publishing

Publication Date

2024

ISBN

9781835460825

Number of Pages

190

Language

English

Format

PDF

File Size

3.9MB

Subject

Artificial Intelligence

Table of Contents

  • Title Page
  • Copyright and Credits
  • Dedications
  • Foreword
  • Contributors
  • Table of Contents
  • Preface
  • Part 1: Foundations of Generative AI and the Evolution of Large Language Models
  • Chapter 1: Understanding Generative AI: An Introduction
  • Generative AI
  • Distinguishing generative AI from other AI models
  • Looking back at the evolution of generative AI
  • Looking ahead at risks and implications
  • Introducing use cases of generative AI
  • The future of generative AI applications
  • Summary
  • References
  • Chapter 2: Surveying GenAI Types and Modes: An Overview of GANs, Diffusers, and Transformers
  • Understanding General Artificial Intelligence (GAI) Types
  • Deconstructing GAI methods
  • Applying GAI models
  • Summary
  • References
  • Chapter 3: Tracing the Foundations of Natural Language Processing and the Impact of the Transformer
  • Early approaches in NLP
  • The emergence of the Transformer in advanced language models
  • Evolving language models
  • Implementing the original Transformer
  • Summary
  • References
  • Chapter 4: Applying Pretrained Generative Models: From Prototype to Production
  • Prototyping environments
  • Transitioning to production
  • Mapping features to production setup
  • Setting up a production-ready environment
  • Local development setup
  • Model selection
  • Updating the prototyping environment
  • Quantitative metrics evaluation
  • Responsible AI considerations
  • Final deployment
  • Summary
  • Part 2: Practical Applications of Generative AI
  • Chapter 5: Fine-Tuning Generative Models for Specific Tasks
  • Foundation and relevance
  • PEFT
  • In-context learning
  • Fine-tuning versus in-context learning
  • Practice project: Fine-tuning for Q&A using PEFT
  • Summary
  • References
  • Chapter 6: Understanding Domain Adaptation for Large Language Models
  • Demystifying domain adaptation
  • Practice project: Transfer learning for the finance domain
  • Summary
  • References
  • Chapter 7: Mastering the Fundamentals of Prompt Engineering
  • The shift to prompt-based approaches
  • Basic prompting
  • Elevating prompts
  • Advanced prompting in action
  • Practice project: Implementing RAG with LlamaIndex using Python
  • Summary
  • References
  • Chapter 8: Addressing Ethical Considerations and Charting a Path Toward Trustworthy Generative AI
  • Ethical norms and values in the context of generative AI
  • Investigating and minimizing bias
  • Constrained generation and eliciting trustworthy outcomes
  • Understanding jailbreaking and harmful behaviors
  • Practice project: Minimizing harmful behaviors with filtering
  • Summary
  • References
  • Index
  • About Packt
  • Other Books You May Enjoy