Hands-on Large Language Models from Scratch

"Hands-on Large Language Models from Scratch" is a practical guide designed to transition the reader from a theoretical understanding of AI to a functional capability in building and deploying Large Language Models. This book is engineered to be a comprehensive, step-by-step manual for students and developers.

Philosophy

The core philosophy of this book is learning by doing. I believe that true comprehension in a technical field is achieved not by passive reading but by active implementation. Every chapter is structured around a practical outcome, where theoretical concepts are introduced only to the extent necessary to understand the "why" behind the code. The emphasis is overwhelmingly on the "how"—how to design the architecture, how to process the data, how to write the training loop, how to fine-tune the model, and how to deploy the final application. I strip away unnecessary mathematical formalism and focus on intuitive explanations and functional code.


Key Features

1. Step-by-Step Implementation: Clear, numbered steps for building everything from a simple attention mechanism to a complete LLM-powered web application.

2. Simplified Code: All code is written in Python using the PyTorch framework, with a focus on readability and simplicity, making it accessible to beginners.

3. From Scratch to Deployment: Covers the entire lifecycle of an LLM project—data preparation, model building, pre-training, fine-tuning, and deployment using tools like FastAPI and Docker.

4. Focus on Practical Techniques: Deep dives into essential, modern techniques like transfer learning, fine-tuning, Retrieval-Augmented Generation (RAG), and Parameter-Efficient Fine-Tuning (PEFT).

5. DIY Capstone Project: A complete, end-to-end project in the final chapter with full, explained code to build a practical AI application.


To Whom This Book Is For

1. B.Tech/M.Tech Computer Science Students: An ideal textbook or supplementary resource for courses on AI, Machine Learning, or Natural Language Processing.

2. Aspiring AI/ML Engineers: A practical guide to acquiring the hands-on skills required for a career in the AI industry.

3. Software Developers: A clear and concise resource for upskilling and learning how to integrate LLM capabilities into existing or new applications.

4. Technology Enthusiasts and Hobbyists: Anyone with a programming background who curious about what goes on inside an LLM and wants to build their own.


Disclaimer: Earnest request from the Author.

Kindly go through the table of contents and refer kindle edition for a glance on the related contents.

Thank you for your kind consideration!