Python GPT Cookbook

GPT has redefined the landscape of AI, enabling the creation of powerful language models capable of diverse applications. The objective of the Python GPT Cookbook is to equip readers with practical recipes and foundational knowledge to build business solutions using GPT and Python.

The book is divided into four parts. The first covers the basics, the second teaches the fundamentals of NLP, the third delves into applying GPT in various fields, and the fourth provides a conclusion. Each chapter includes recipes and practical insights to help readers deepen their understanding and apply the concepts presented. This cookbook approach delivers 78 practical recipes, including creating OpenAI accounts, utilizing playgrounds and API keys. You will learn text preprocessing, embeddings, fine-tuning, and GPT integration with Hugging Face. Learn to implement GPT using PyTorch and TensorFlow, convert models, and build authenticated actions. Applications include chatbots, email summarization, DBA copilots, and use cases in marketing, sales, IP, and manufacturing.

By the end of the book, readers will have a robust understanding of GPT models and how to use them for real-world NLP tasks, along with the skills to continue exploring this powerful technology independently.

WHAT YOU WILL LEARN
● Learn Python, OpenAI, TensorFlow, Hugging Face, and vector databases.
● Master Python for NLP applications and data manipulation.
● Understand and implement GPT models for various tasks.
● Integrate GPT with various architectural components, such as databases, third-party APIs, servers, and data pipelines
● Utilise NLTK, PyTorch, and TensorFlow for advanced NLP projects.
● Use Jupyter for interactive coding and data analysis.

WHO THIS BOOK IS FOR
The Python GPT Cookbook is for IT professionals and business innovators who already have basic Python skills. Data scientists, ML engineers, NLP engineers, and ML researchers will also find it useful.

1. Introduction to GPT
2. Crafting Your GPT Workspace
3. Pre-processing
4. Embeddings
5. Classifying Intent
6. Hugging Face and GPT
7. Vector Databases
8. GPT, PyTorch, and TensorFlow
9. Custom GPT Actions
10. Integrating GPT with the Enterprise
11. Marketing and Sales with GPT
12. Intellectual Property Management with GPT
13. GPT in Manufacturing
14. Scaling up
15. Emerging Trends and Future Directions

Dr. Neil Williams is a data scientist and AI strategist with a passion for transforming complex challenges into actionable insights. With a background spanning econometrics, machine learning, and Gen AI, he specializes in leveraging Python, Elastic, and LLMs to build scalable, high-impact solutions.

Neil has worked as a consultant for nearly 30 years; his client engagements include Volkswagen, National Grid, Royal Mail, Capgemini, The R&A, Man City FC, Elexon, UKRI, Electricity Company of Ghana, Duni, BIM Object, and many others.

From guiding enterprises through AI adoption to architecting intelligent risk management platforms, Neil combines deep technical expertise with sharp business acumen. His work spans industries (majoring in energy), where he has championed data-driven decision- making.

Whether pioneering new applications of GPT in enterprise environments or mentoring the next generation of data scientists, he thrives at the intersection of technology, strategy, and innovation.

In 1998, Neil was awarded a PhD for his research in computer vision applied to subsea robotics. His broad experience and academic rigour have given him a deep understanding of the technical aspects of AI and its application across different industries.