Modern Large Language Models by Daniel R. Holt (.ePUB)+
File Size: 12.5 MB
Modern Large Language Models: A First-Principles Guide to Building and Understanding Transformer-Based Language Models by Daniel R. Holt
Requirements: .ePUB, .PDF reader, 12.5 MB
Overview: Large language models now sit at the core of modern software systems. They power search, recommendation engines, coding assistants, conversational interfaces, and autonomous agents. Yet for many engineers and practitioners, these models remain opaque—understood through fragments of code, borrowed recipes, or surface-level explanations. This book was written to change that. Modern Large Language Models is a clear, systems-level guide to understanding how transformer-based language models actually work—starting from first principles and building upward toward complete, modern LLM systems. Rather than treating large language models as black boxes, this book explains the fundamental ideas that make them possible: probabilistic language modeling, vector representations, attention mechanisms, optimization, and architectural composition. The examples use PyTorch for clarity, but the ideas are framework-agnostic and designed to remain relevant as tooling and architectures evolve. Clean diagrams, structured explanations, and carefully reasoned trade-offs replace hype and jargon.
Genre: Non-Fiction > Tech & Devices
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