Overview

""Building Small Language Models from Scratch"" is a comprehensive, hands-on guide designed for students, developers, and aspiring AI engineers who want to move beyond using pre-built models and learn to create their own. This book demystifies the complex world of language models by breaking it down into understandable, practical steps. Using the popular PyTorch framework, you will journey from the basic building blocks of neural networks to constructing and training a complete, functional Small Language Model (SLM). Key Features of the Book: 1. From-Scratch Approach: Learn by building every component of a language model, from the tokenizer to the final prediction head, for a deep, intuitive understanding. 2. Hands-On Learning: Packed with practical code examples, step-by-step tutorials, and end-of-chapter exercises to reinforce concepts. 3. Focus on PyTorch: Master the de-facto industry and research standard for deep learning to build flexible and powerful models. 4. NEP 2020 & AICTE Aligned: The curriculum is structured to promote skill-based, experiential learning with a focus on real-world problem-solving, perfectly aligning with modern educational frameworks. 5. Beginner to Advanced: The book starts with the basics and progressively builds to advanced topics, making it suitable for learners at all levels. 6. Capstone Project: A dedicated final chapter guides you through building a complete, real-world application-a domain-specific Question-Answering Bot-including full, commented code and deployment considerations. 7. Ethical AI Focus: A dedicated chapter on the ethical implications, biases, and societal impact of language models, fostering responsible innovation. 8. Clarity and Simplicity: Complex topics like the Transformer architecture and self-attention are broken down into simple, easy-to-understand explanations with clear diagrams and analogies. Who is this book for? 1. B.Tech/M.Tech Students: Computer Science, AI, and Data Science students looking for a textbook that bridges the gap between theory and practical application. 2. Aspiring AI/ML Engineers: Individuals who want to build a strong, foundational portfolio project and gain a deep understanding of the models they will work with. 3. Software Developers: Programmers who want to transition into AI/NLP and need a structured, hands-on learning path. 4. Researchers and Academics: Individuals who need a practical guide to quickly prototype and experiment with novel language model architectures.

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