دانلود کتاب استاد شدن در پردازش زبان طبیعی (NLP) از مبانی تا عامل‌ها

Natural Language Processing (NLP) has evolved beyond rule-based systems and classical machine learning. This second edition guides you through that transformation from mathematical and ML foundations to large language models, retrieval pipelines, agentic automation, and AI-native system design. It strengthens core NLP concepts while expanding into modern architectures such as transformers, parameter-efficient fine-tuning such as LoRA and QLoRA, and alignment methods like RLHF and DPO. You’ll begin with essential linear algebra, probability, and machine learning principles before moving into text preprocessing, feature engineering, classification pipelines, and deep learning architectures. From there, the focus shifts to system design: building Retrieval- Augmented Generation (RAG) pipelines, implementing model routing strategies that balance cost and performance, and orchestrating structured multi-agent workflows. You’ll also learn about structured interoperability patterns, including the Model Context Protocol (MCP). Governance and safety will be treated as architectural concerns, demonstrating how policy and compliance can be integrated directly into AI systems. By the end, you will have the tools to implement NLP techniques and be equipped to design, govern, and deploy intelligent systems built on them.