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Natural Language Processing: Complete Technical Reference is a comprehensive, production-focused guide designed for AI engineers, machine learning practitioners, software developers, data scientists, and students who want to master modern NLP from first principles to real-world deployment.
Rather than focusing on isolated concepts, this book presents the complete NLP ecosystem-from classical text preprocessing and statistical techniques to Transformers, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), parameter-efficient fine-tuning, multimodal AI, multilingual models, and production-grade NLP systems.
Every chapter combines intuitive explanations with technical depth, practical Python examples, production insights, common implementation mistakes, interview-focused questions, and real-world engineering best practices.
Inside this book you'll learn:
Natural Language Processing fundamentals
Text preprocessing, tokenization, POS tagging, NER, and parsing
TF-IDF, Word2Vec, GloVe, FastText, and Sentence-BERT
RNNs, LSTMs, GRUs, Seq2Seq, Attention, and Transformers
BERT, GPT, T5, and modern Large Language Models
Retrieval-Augmented Generation (RAG) architecture
LoRA, QLoRA, PEFT, DPO, and LLM fine-tuning
Prompt Engineering and AI Agents
Production deployment, optimization, monitoring, and evaluation
Multimodal NLP and multilingual language models
NLP interview questions with detailed explanations
A complete end-to-end NLP project
200+ NLP and LLM glossary terms for quick reference
Whether you're preparing for technical interviews, building enterprise AI applications, or transitioning into AI engineering, this reference provides the knowledge needed to design, implement, deploy, and maintain modern Natural Language Processing systems.
If you're looking for a practical NLP handbook that bridges theory, implementation, and production engineering, this book belongs on your bookshelf.
