Mastering NLP From Foundations to Agents - Second Edition | Building AI Agents through Agentic Automation and RAG Workflows with Python
Idioma: inglés
Editorial: Packt Publishing, 2026
- Tapa blanda
- Nuevo
Librería: preigu, Osnabrück, Alemaniapreigu
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Mastering NLP From Foundations to Agents - Second Edition | Building AI Agents through Agentic Automation and RAG Workflows with Python | Lior Gazit (u. a.) | Taschenbuch | Englisch | 2026 | Packt Publishing | EAN 9781806106134 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.
N° de ref. del artículo 134650787
- Título
- Mastering NLP From Foundations to Agents - Second Edition | Building AI Agents through Agentic Automation and RAG Workflows with Python
- Autor
- Lior Gazit (u. a.)
- Editorial
- Packt Publishing
- Año de publicación
- 2026
- Estado
- Neu
- Encuadernación
- Taschenbuch
- Idioma
- inglés
- ISBN 10
- 1806106132
- ISBN 13
- 9781806106134
- Edición
- 2ª Edición
- Peso del artículo
- 1273 gramos
- Dimensiones
- 235 x 191 x 37 mm
- Catálogos de vendedores
- Bücher
This second edition spans NLP foundations to LLMs, RAG, & agentic systems, teaching you to design and fine-tune production-ready AI solutions in Python.
Free with your book: DRM-free PDF version + access to Packt's next-gen Reader*
Key Features
- Engineer NLP systems from ML foundations to LLM architectures
- Implement RAG pipelines, routing layers, and agent workflows
- Fine-tune and align LLMs using LoRA, RLHF, and DPO methods
- Design production-grade AI systems with governance and safety
Book Description
Natural Language Processing has evolved beyond rule-based systems and classical machine learning (ML). 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 (LoRA and QLoRA), and alignment methods like RLHF and DPO.
You’ll begin with essential linear algebra, probability, and ML 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 introduce 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.
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What you will learn
- Build strong NLP foundations in math and ML
- Engineer text classification and NLP pipelines
- Train and fine-tune modern LLM architectures
- Implement RAG systems with LangChain
- Orchestrate multiple AI agents and tools to solve complex tasks
- Evaluate NLP model performance and apply AI safety best practices
- Integrate external data and tools using Model Context Protocol (MCP)
- Fine-tune transformers with LoRA, QLoRA, and DPO techniques
Who this book is for
This book is for machine learning engineers, data scientists, and NLP practitioners looking to deepen their expertise and build advanced AI solutions. It also benefits professionals and researchers who want to apply the latest NLP and LLM techniques in real-world projects. Software engineers entering the AI field and tech enthusiasts keen on modern NLP advancements will find it valuable. A solid understanding of Python and basic Machine Learning concepts is assumed.
Table of Contents
- An Introduction to the NLP Landscape
- Mathematical Foundations for Machine Learning in NLP
- Unleashing Machine Learning Potential in NLP
- Streamlining Text Preprocessing Techniques for NLP
- Text Classification Using Traditional ML Techniques
- Text Classification Part 2 - Using Deep Learning Language Models
- Demystifying LLM Theory, Design, and Implementation
- Parameter-Efficient Fine-Tuning and Reasoning in LLMs
- Advanced Setup and Integration with RAG and MCP
- Advanced LLM Practices Using RAG and LangChain
- Multi-Agent Solutions and Advanced Agent Frameworks
- Technical Guardrails of AI Safety and Responsible Implementation
- Designing and Managing AI-Native Products
“Sinopsis” puede pertenecer a otra edición de este título.
Acerca del autor
Lior Gazit is a highly skilled Machine Learning professional with a proven track record of success in building and leading teams drive business growth. He is an expert in Natural Language Processing and has successfully developed innovative Machine Learning pipelines and products. He holds a Master degree and has published in peer-reviewed journals and conferences. As a Senior Director of the Machine Learning group in the Financial sector, and a Principal Machine Learning Advisor at an emerging startup, Lior is a respected leader in the industry, with a wealth of knowledge and experience to share. With much passion and inspiration, Lior is dedicated to using Machine Learning to drive positive change and growth in his organizations.
Meysam Ghaffari is a Senior Data Scientist with a strong background in Natural Language Processing and Deep Learning. Currently working at MSKCC, where he specialize in developing and improving Machine Learning and NLP models for healthcare problems. He has over 9 years of experience in Machine Learning and over 4 years of experience in NLP and Deep Learning. He received his Ph.D. in Computer Science from Florida State University, His MS in Computer Science - Artificial Intelligence from Isfahan University of Technology and his B.S. in Computer Science at Iran University of Science and Technology. He also worked as a post doctoral research associate at University of Wisconsin-Madison before joining MSKCC.
“Acerca de” puede pertenecer a otra edición de este título.
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