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!

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