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Mastering Large Language Models from First Principles: A Practical Guide to Building Transformers, Attention Mechanisms, Tokenizers, and Intelligent A
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ISBN13: 9798190792409
Publisher: Independently Published
Pages: 190
Weight: 0.75
Height: 0.40 Width: 7.00 Depth: 10.00
Language: English
Publisher: Independently Published
Pages: 190
Weight: 0.75
Height: 0.40 Width: 7.00 Depth: 10.00
Language: English
Large Language Models (LLMs) are transforming how software is built, how businesses automate workflows, and how people interact with artificial intelligence. From intelligent chatbots and coding assistants to content generation, search, and reasoning systems, LLMs have become the foundation of modern AI. Yet many developers use these powerful models without truly understanding what happens behind the scenes.
Mastering Large Language Models from First Principles takes you beyond APIs and prebuilt frameworks to reveal the engineering concepts that power today's transformer-based language models. Through a practical, hands-on approach, you'll learn how every major component of an LLM works from tokenization and embeddings to self-attention, transformer architectures, pretraining, fine-tuning, evaluation, and deployment.
Rather than treating LLMs as black boxes, this book guides you through the underlying mathematics, algorithms, and implementation techniques that make them possible. You'll build essential model components step by step using Python and PyTorch, gaining the confidence to understand, customize, optimize, and extend modern language models for your own applications.
As you progress, you'll explore not only the fundamentals but also the techniques used in real-world AI systems, including efficient training strategies, parameter-efficient fine-tuning, retrieval-augmented generation (RAG), model optimization, inference acceleration, and production deployment. Each chapter combines clear explanations with practical coding examples, diagrams, and hands-on projects that reinforce your understanding and help you develop production-ready skills.
By the end of this book, you won't just know how to use Large Language Models you'll understand how they are built, how they learn, why they work, and how to adapt them to solve real-world problems across a wide range of industries.
What You'll Learn
Rather than treating LLMs as black boxes, this book guides you through the underlying mathematics, algorithms, and implementation techniques that make them possible. You'll build essential model components step by step using Python and PyTorch, gaining the confidence to understand, customize, optimize, and extend modern language models for your own applications.
As you progress, you'll explore not only the fundamentals but also the techniques used in real-world AI systems, including efficient training strategies, parameter-efficient fine-tuning, retrieval-augmented generation (RAG), model optimization, inference acceleration, and production deployment. Each chapter combines clear explanations with practical coding examples, diagrams, and hands-on projects that reinforce your understanding and help you develop production-ready skills.
By the end of this book, you won't just know how to use Large Language Models you'll understand how they are built, how they learn, why they work, and how to adapt them to solve real-world problems across a wide range of industries.
What You'll Learn
- Understand the mathematics and engineering principles behind Large Language Models.
- Build transformer architectures from first principles using Python and PyTorch.
- Implement tokenization, embeddings, positional encoding, and self-attention mechanisms.
- Design, train, and evaluate transformer-based language models.
- Fine-tune pretrained LLMs for classification, instruction following, and domain-specific tasks.
- Apply parameter-efficient fine-tuning techniques such as LoRA and QLoRA.
- Build Retrieval-Augmented Generation (RAG) systems that combine language models with external knowledge.
- Optimize models through quantization, efficient inference, and performance tuning.
- Deploy LLM-powered applications for real-world use cases.
- Gain the practical skills needed to confidently work with modern AI technologies and future transformer-based architectures.
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