bratanic tomaz (52 resultados)

Paperback. Condición: Fair. No Jacket. Former library book; Readable copy. Pages may have considerable notes/highlighting. ~ ThriftBooks: Read More, Spend Less.

Condición: As New. Unread copy in mint condition.

Condición: New. Brand New.

Condición: As New. Unread copy in mint condition.

Paperback or Softback. Condición: New. Essential Graphrag: Knowledge Graph-Enhanced Rag. Book.

Condición: New.

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

Paperback. Condición: New. 1st. Your LLM keeps hallucinating, and clients are beginning to lose trust. Generative AI can amaze users one moment and confuse them the next when answers are based on guesswork rather than verified facts. What if you could design systems that deliver accurate, traceable, and relevant information every time? By combining knowledge graphs with retrieval-augmented generation, you can build solutions that power GenAI models with structured, reliable data and keep stakeholders confident in every interaction. Knowledge graph basics: Model context data for instant, precise retrieval. Vector similarity search toolkit: Surface only the most relevant passages, cut noise. Agentic RAG workflow: Orchestrate multi-step reasoning that scales to production. Cypher and Python templates: Drop-in code accelerates prototypes to deployable services. Evaluation framework: Measure accuracy, latency, and traceability with confidence. Hybrid structured plus unstructured guidance: Integrate PDFs, databases, and APIs into one coherent knowledge base. Essential GraphRAG by graph experts Tomaz Bratanic and Oskar Hane arrives to show data teams exactly how to hard-wire reliability into GenAI projects. Through concise explanations and fully worked examples, the authors guide you from raw text to a Neo4j-backed knowledge graph powering Retrieval Augmented Generation. Each chapter pairs theory with runnable notebooks, so you see instant results. Finish the book able to architect, build, and benchmark a production-ready RAG pipeline that your stakeholders can audit and trust. The techniques transfer to any domain and future model. For data scientists and Python developers with basic Neo4j skills who want bulletproof GenAI, this is your next step.

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

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

Paperback. Condición: new. Paperback. Your LLM keeps hallucinating, and clients are beginning to lose trust. Generative AI can amaze users one moment and confuse them the next when answers are based on guesswork rather than verified facts. What if you could design systems that deliver accurate, traceable, and relevant information every time? By combining knowledge graphs with retrieval-augmented generation, you can build solutions that power GenAI models with structured, reliable data and keep stakeholders confident in every interaction. Knowledge graph basics: Model context data for instant, precise retrieval. Vector similarity search toolkit: Surface only the most relevant passages, cut noise. Agentic RAG workflow: Orchestrate multi-step reasoning that scales to production. Cypher and Python templates: Drop-in code accelerates prototypes to deployable services. Evaluation framework: Measure accuracy, latency, and traceability with confidence. Hybrid structured plus unstructured guidance: Integrate PDFs, databases, and APIs into one coherent knowledge base. Essential GraphRAG by graph experts Tomaz Bratanic and Oskar Hane arrives to show data teams exactly how to hard-wire reliability into GenAI projects. Through concise explanations and fully worked examples, the authors guide you from raw text to a Neo4j-backed knowledge graph powering Retrieval Augmented Generation. Each chapter pairs theory with runnable notebooks, so you see instant results. Finish the book able to architect, build, and benchmark a production-ready RAG pipeline that your stakeholders can audit and trust. The techniques transfer to any domain and future model. For data scientists and Python developers with basic Neo4j skills who want bulletproof GenAI, this is your next step. Upgrade your RAG applications with the power of knowledge graphs. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

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

Paperback. Condición: New. Graphs are the natural way to understand connected data. This book explores the most important algorithms and techniques for graphs in data science, with practical examples and concrete advice on implementation and deployment. In   Graph Algorithms for Data Science  you will learn: Labeled-property graph modelingConstructing a graph from structured data such as CSV or SQLNLP techniques to construct a graph from unstructured dataCypher query language syntax to manipulate data and extract insightsSocial network analysis algorithms like PageRank and community detectionHow to translate graph structure to a ML model input with node embedding modelsUsing graph features in node classification and link prediction workflows Graph Algorithms for Data Science  is a hands-on guide to working with graph-based data in applications like machine learning, fraud detection, and business data analysis. It's filled with fascinating and fun projects, demonstrating the ins-and-outs of graphs. You'll gain practical skills by analyzing Twitter, building graphs with NLP techniques, and much more. You don't need any graph experience to start benefiting from this insightful guide. These powerful graph algorithms are explained in clear, jargon-free text and illustrations that makes them easy to apply to your own projects. about the technology Graphs reveal the relationships in your data. Tracking these interlinking connections reveals new insights and influences and lets you analyze each data point as part of a larger whole. This interconnected data is perfect for machine learning, as well as analyzing social networks, communities, and even product recommendations. about the book Graph Algorithms for Data Science  teaches you how to construct graphs from both structured and unstructured data. You'll learn how the flexible Cypher query language can be used to easily manipulate graph structures, and extract amazing insights. The book explores common and useful graph algorithms like PageRank and community detection/clustering algorithms. Each new algorithm you learn is instantly put into action to complete a hands-on data project, including modeling a social network! Finally, you'll learn how to utilize graphs to upgrade your machine learning, including utilizing node embedding models and graph neural networks.

Paperback. Condición: New. 1st. Your LLM keeps hallucinating, and clients are beginning to lose trust. Generative AI can amaze users one moment and confuse them the next when answers are based on guesswork rather than verified facts. What if you could design systems that deliver accurate, traceable, and relevant information every time? By combining knowledge graphs with retrieval-augmented generation, you can build solutions that power GenAI models with structured, reliable data and keep stakeholders confident in every interaction. Knowledge graph basics: Model context data for instant, precise retrieval. Vector similarity search toolkit: Surface only the most relevant passages, cut noise. Agentic RAG workflow: Orchestrate multi-step reasoning that scales to production. Cypher and Python templates: Drop-in code accelerates prototypes to deployable services. Evaluation framework: Measure accuracy, latency, and traceability with confidence. Hybrid structured plus unstructured guidance: Integrate PDFs, databases, and APIs into one coherent knowledge base. Essential GraphRAG by graph experts Tomaz Bratanic and Oskar Hane arrives to show data teams exactly how to hard-wire reliability into GenAI projects. Through concise explanations and fully worked examples, the authors guide you from raw text to a Neo4j-backed knowledge graph powering Retrieval Augmented Generation. Each chapter pairs theory with runnable notebooks, so you see instant results. Finish the book able to architect, build, and benchmark a production-ready RAG pipeline that your stakeholders can audit and trust. The techniques transfer to any domain and future model. For data scientists and Python developers with basic Neo4j skills who want bulletproof GenAI, this is your next step.

paperback. Condición: New.

paperback. Condición: New.

paperback. Condición: New. Special order direct from the distributor.

Paperback. Condición: new. Paperback. Graphs are the natural way to understand connected data. This book explores the most important algorithms and techniques for graphs in data science, with practical examples and concrete advice on implementation and deployment. In Graph Algorithms for Data Science you will learn: Labeled-property graph modelingConstructing a graph from structured data such as CSV or SQLNLP techniques to construct a graph from unstructured dataCypher query language syntax to manipulate data and extract insightsSocial network analysis algorithms like PageRank and community detectionHow to translate graph structure to a ML model input with node embedding modelsUsing graph features in node classification and link prediction workflows Graph Algorithms for Data Science is a hands-on guide to working with graph-based data in applications like machine learning, fraud detection, and business data analysis. It's filled with fascinating and fun projects, demonstrating the ins-and-outs of graphs. You'll gain practical skills by analyzing Twitter, building graphs with NLP techniques, and much more. You don't need any graph experience to start benefiting from this insightful guide. These powerful graph algorithms are explained in clear, jargon-free text and illustrations that makes them easy to apply to your own projects. about the technology Graphs reveal the relationships in your data. Tracking these interlinking connections reveals new insights and influences and lets you analyze each data point as part of a larger whole. This interconnected data is perfect for machine learning, as well as analyzing social networks, communities, and even product recommendations. about the book Graph Algorithms for Data Science teaches you how to construct graphs from both structured and unstructured data. You'll learn how the flexible Cypher query language can be used to easily manipulate graph structures, and extract amazing insights. The book explores common and useful graph algorithms like PageRank and community detection/clustering algorithms. Each new algorithm you learn is instantly put into action to complete a hands-on data project, including modeling a social network! Finally, you'll learn how to utilize graphs to upgrade your machine learning, including utilizing node embedding models and graph neural networks. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Condición: New.

paperback. Condición: New.

Condición: New.

Condición: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.

Paperback / softback. Condición: New. New copy - Usually dispatched within 2 working days.

Condición: new.

Condición: New.

Condición: New. Brand New Original US Edition. Customer service! Satisfaction Guaranteed.

Paperback / softback. Condición: New. New copy - Usually dispatched within 4 working days.

paperback. Condición: New.

Condición: New.

Paperback. Condición: New. Graphs are the natural way to understand connected data. This book explores the most important algorithms and techniques for graphs in data science, with practical examples and concrete advice on implementation and deployment. In   Graph Algorithms for Data Science  you will learn: Labeled-property graph modelingConstructing a graph from structured data such as CSV or SQLNLP techniques to construct a graph from unstructured dataCypher query language syntax to manipulate data and extract insightsSocial network analysis algorithms like PageRank and community detectionHow to translate graph structure to a ML model input with node embedding modelsUsing graph features in node classification and link prediction workflows Graph Algorithms for Data Science  is a hands-on guide to working with graph-based data in applications like machine learning, fraud detection, and business data analysis. It's filled with fascinating and fun projects, demonstrating the ins-and-outs of graphs. You'll gain practical skills by analyzing Twitter, building graphs with NLP techniques, and much more. You don't need any graph experience to start benefiting from this insightful guide. These powerful graph algorithms are explained in clear, jargon-free text and illustrations that makes them easy to apply to your own projects. about the technology Graphs reveal the relationships in your data. Tracking these interlinking connections reveals new insights and influences and lets you analyze each data point as part of a larger whole. This interconnected data is perfect for machine learning, as well as analyzing social networks, communities, and even product recommendations. about the book Graph Algorithms for Data Science  teaches you how to construct graphs from both structured and unstructured data. You'll learn how the flexible Cypher query language can be used to easily manipulate graph structures, and extract amazing insights. The book explores common and useful graph algorithms like PageRank and community detection/clustering algorithms. Each new algorithm you learn is instantly put into action to complete a hands-on data project, including modeling a social network! Finally, you'll learn how to utilize graphs to upgrade your machine learning, including utilizing node embedding models and graph neural networks.