NLP Technical Reference
Language: English
Published by Independently Published Jul 2026, 2026
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
Condition: New
US$ 53.28
Quantity: 2 available
Add to basketItem description from seller
Neuware - 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 fundamentalsText preprocessing, tokenization, POS tagging, NER, and parsingTF-IDF, Word2Vec, GloVe, FastText, and Sentence-BERTRNNs, LSTMs, GRUs, Seq2Seq, Attention, and TransformersBERT, GPT, T5, and modern Large Language ModelsRetrieval-Augmented Generation (RAG) architectureLoRA, QLoRA, PEFT, DPO, and LLM fine-tuningPrompt Engineering and AI AgentsProduction deployment, optimization, monitoring, and evaluationMultimodal NLP and multilingual language modelsNLP interview questions with detailed explanationsA complete end-to-end NLP project200+ NLP and LLM glossary terms for quick referenceWhether 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. …
Seller Inventory # 9798188521158
- Title
- NLP Technical Reference
- Author
- Rashmi Patel
- Publisher
- Independently Published Jul 2026
- Publication year
- 2026
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 13
- 9798188521158
- Item weight
- 354 grams
- Dimensions
- 229x152x14 mm
- Series
- Book 10 of 22: AI and ML Reference handbooks
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.
"Synopsis" may belong to another edition of this title.
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