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Developing LLMs [#1016839]
Developing LLMs by Ajit Singh
English | August 19, 2025 | ISBN: N/A | ASIN: B0FN2JSYYG | 278 pages | EPUB | 0.28 Mb
This book, "Developing LLMs" (A Crucial Step in the End-to-End LLM Development Lifecycle,)" is born out of a need for a structured, practical, and principles-first guide. While many resources focus on either the high-level, abstract theory or the simplistic use of APIs, there exists a significant gap for a text that walks the learner through the crucial development phase - the journey from understanding the foundational Transformer architecture to fine-tuning, adapting, and deploying a model for a real-world problem.
Key Features:
1. NEP 2020 & AICTE Compliant: The content is structured to promote critical thinking, project-based learning, and skill development, aligning perfectly with modern educational frameworks.
2. Beginner to Advanced Progression: The book starts with the absolute basics of NLP and systematically builds up to advanced, M.Tech-level topics like PEFT, RAG, and model evaluation.
3. Hands-On Practical Approach: Packed with code examples, practical exercises, and Jupyter Notebook-friendly snippets using popular libraries like Hugging Face and PyTorch.
4. End-to-End Lifecycle Focus: While covering the entire lifecycle conceptually, the book focuses deeply on the development phase-the most relevant part for most AI engineers.
5. Real-World Use Cases: Every concept is illustrated with examples that students can relate to, from building a code generator to a chatbot that can "read" documents.
6. In-Depth Capstone Project: A full chapter is dedicated to a live, working project that integrates fine-tuning, RAG, and UI development, complete with a step-by-step code walkthrough.
7. Focus on Responsible AI: A dedicated chapter on ethics, bias, and safety ensures that students learn to build AI systems that are not only powerful but also responsible and fair.
8. Updated and Relevant: The book covers the latest, state-of-the-art techniques and tools that are being used in the industry right now, ensuring students learn current best practices.
Who Should Read This Book?
1. B.Tech/B.E. Students: Undergraduates in Computer Science, IT, and AI who want a solid, practical foundation in LLMs.
2. M.Tech/M.S. Students: Graduate students specializing in AI, Machine Learning, or NLP who need to deepen their expertise in advanced LLM techniques.
3. Aspiring AI/ML Engineers: Professionals looking to transition into the field of generative AI and need a structured learning path.
4. University Faculty and Educators: Instructors seeking a textbook that is aligned with modern curricula and promotes hands-on learning.
By the end of this book, you will not only understand how LLMs work but will also possess the practical skills to develop, adapt, and deploy them to create innovative and impactful solutions.
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