This is the code repository for Generative AI Foundations in Python, published by Packt.
Discover key techniques and navigate modern challenges in LLMs
This guide equips you with the skills needed to implement generative AI in your applications. It covers the foundational elements of transformer-based LLMs and diffusion models by combining theoretical knowledge with practical application.
This book covers the following exciting features:
- Discover the fundamentals of GenAI and its foundations in NLP
- Dissect foundational generative architectures including GANs, transformers, and diffusion models
- Find out how to fine-tune LLMs for specific NLP tasks
- Understand transfer learning and fine-tuning to facilitate domain adaptation, including fields such as finance
- Explore prompt engineering, including in-context learning, templatization, and rationalization through chain-of-thought and RAG
- Implement responsible practices with generative LLMs to minimize bias, toxicity, and other harmful outputs
If you feel this book is for you, get your copy today!
All of the code is organized into folders.
The code will look like the following:
# Get the start and end positions
answer_start_scores = outputs.start_logits
answer_end_scores = outputs.end_logitsFollowing is what you need for this book: This book is for developers, data scientists, and machine learning engineers embarking on projects driven by generative AI. A general understanding of machine learning and deep learning, as well as some proficiency with Python, is expected.
With the following software and hardware list you can run all code files present in the book (Chapter 1-8).
| Chapter | Software required | OS required |
|---|---|---|
| 1-8 | Python 3 | GPU-enabled Windows, macOS, or Linux |
Carlos Rodriguez is the Director of AI Risk for a major financial institution, with an extensive career spanning over 20 years, focused on emerging technologies. Carlos led the development of a first-generation, finance-specific natural language platform and pioneered enterprise machine-learning workflows. He later transitioned to enterprise risk, guiding the AI risk discipline. Carlos holds degrees in data science and machine learning, and is a tireless proponent of responsible and ethical AI.