Lior Gazit (32 results)

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Paperback or Softback. Condition: New. Mastering NLP from Foundations to LLMs: Apply advanced rule-based techniques to LLMs and solve real-world business problems using Python. Book.

Paperback or Softback. Condition: New. Mastering NLP From Foundations to Agents - Second Edition: Building AI Agents through Agentic Automation and RAG Workflows with Python. Book.

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PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

Paperback. Condition: New. 2nd. A fully updated second edition that bridges NLP fundamentals with the latest in AI. New chapters on LLMs, RAG, LangChain, and autonomous agents help you build advanced language solutions with hands-on Python examples.

PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

Paperback. Condition: New. 2nd. A fully updated second edition that bridges NLP fundamentals with the latest in AI. New chapters on LLMs, RAG, LangChain, and autonomous agents help you build advanced language solutions with hands-on Python examples.

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Condition: New. In English.

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Condition: New. pp. 387.

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Paperback. Condition: New. 2nd. A fully updated second edition that bridges NLP fundamentals with the latest in AI. New chapters on LLMs, RAG, LangChain, and autonomous agents help you build advanced language solutions with hands-on Python examples.

Paperback. Condition: New. 2nd. A fully updated second edition that bridges NLP fundamentals with the latest in AI. New chapters on LLMs, RAG, LangChain, and autonomous agents help you build advanced language solutions with hands-on Python examples.

Paperback. Condition: new. Paperback. 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 FeaturesEngineer NLP systems from ML foundations to LLM architecturesImplement RAG pipelines, routing layers, and agent workflowsFine-tune and align LLMs using LoRA, RLHF, and DPO methodsDesign production-grade AI systems with governance and safetyBook DescriptionNatural 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.Youll 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.*Email sign-up and proof of purchase requiredWhat you will learnBuild strong NLP foundations in math and MLEngineer text classification and NLP pipelinesTrain and fine-tune modern LLM architecturesImplement RAG systems with LangChainOrchestrate multiple AI agents and tools to solve complex tasksEvaluate NLP model performance and apply AI safety best practicesIntegrate external data and tools using Model Context Protocol (MCP)Fine-tune transformers with LoRA, QLoRA, and DPO techniquesWho this book is forThis 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. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

PAP. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condition: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Paperback / softback. Condition: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days.

Paperback / softback. Condition: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days.

Condition: New. Print on Demand pp. 387.

Condition: New. PRINT ON DEMAND pp. 387.

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Paperback. Condition: new. Paperback. 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 FeaturesEngineer NLP systems from ML foundations to LLM architecturesImplement RAG pipelines, routing layers, and agent workflowsFine-tune and align LLMs using LoRA, RLHF, and DPO methodsDesign production-grade AI systems with governance and safetyBook DescriptionNatural 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.Youll 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.*Email sign-up and proof of purchase requiredWhat you will learnBuild strong NLP foundations in math and MLEngineer text classification and NLP pipelinesTrain and fine-tune modern LLM architecturesImplement RAG systems with LangChainOrchestrate multiple AI agents and tools to solve complex tasksEvaluate NLP model performance and apply AI safety best practicesIntegrate external data and tools using Model Context Protocol (MCP)Fine-tune transformers with LoRA, QLoRA, and DPO techniquesWho this book is forThis 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Condition: New. PRINT ON DEMAND.

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Enhance your NLP proficiency with modern frameworks like LangChain, explore mathematical foundations and code samples, and gain expert insights into current and future trends Key FeaturesLearn how to build Python-driven solutions with a focus on NLP, LLMs, RAGs, and GPTMaster embedding techniques and machine learning principles for real-world applicationsUnderstand the mathematical foundations of NLP and deep learning designsPurchase of the print or Kindle book includes a free PDF Elektronisches BuchBook DescriptionDo you want to master Natural Language Processing (NLP) but don't know where to begin This book will give you the right head start. Written by leaders in machine learning and NLP, Mastering NLP from Foundations to LLMs provides an in-depth introduction to techniques. Starting with the mathematical foundations of machine learning (ML), you'll gradually progress to advanced NLP applications such as large language models (LLMs) and AI applications. You'll get to grips with linear algebra, optimization, probability, and statistics, which are essential for understanding and implementing machine learning and NLP algorithms. You'll also explore general machine learning techniques and find out how they relate to NLP. Next, you'll learn how to preprocess text data, explore methods for cleaning and preparing text for analysis, and understand how to do text classification. You'll get all of this and more along with complete Python code samples.By the end of the book, the advanced topics of LLMs' theory, design, and applications will be discussed along with the future trends in NLP, which will feature expert opinions. You'll also get to strengthen your practical skills by working on sample real-world NLP business problems and solutions.What you will learnMaster the mathematical foundations of machine learning and NLP Implement advanced techniques for preprocessing text data and analysis Design ML-NLP systems in PythonModel and classify text using traditional machine learning and deep learning methodsUnderstand the theory and design of LLMs and their implementation for various applications in AIExplore NLP insights, trends, and expert opinions on its future direction and potentialWho this book is forThis book is for deep learning and machine learning researchers, NLP practitioners, ML/NLP educators, and STEM students. Professionals working with text data as part of their projects will also find plenty of useful information in this book. Beginner-level familiarity with machine learning and a basic working knowledge of Python will help you get the best out of this book.Table of ContentsNavigating the NLP Landscape: A comprehensive introductionMastering Linear Algebra, Probability, and Statistics for Machine Learning and NLPUnleashing Machine Learning Potentials in NLPStreamlining Text Preprocessing Techniques for Optimal NLP PerformanceEmpowering Text Classification: Leveraging Traditional Machine Learning TechniquesText Classification Reimagined: Delving Deep into Deep Learning Language ModelsDemystifying Large Language Models: Theory, Design, and Langchain ImplementationAccessing the Power of Large Language Models: Advanced Setup and Integration with RAGExploring the Frontiers: Advanced Applications and Innovations Driven by LLMsRiding the Wave: Analyzing Past, Present, and Future Trends Shaped by LLMs and AIExclusive Industry Insights: Perspectives and Predictions from World Class Experts.…

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering.