Tomaz Bratanic (52 results)
- Softcover
Seller: ThriftBooks-Atlanta, AUSTELL, GA, U.S.A.ThriftBooks-Atlanta
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US$ 16.08
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Paperback. Condition: Fair. No Jacket. Former library book; Readable copy. Pages may have considerable notes/highlighting. ~ ThriftBooks: Read More, Spend Less.
- Softcover
Seller: INDOO, Avenel, NJ, U.S.A.INDOO
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US$ 43.62
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Condition: As New. Unread copy in mint condition.
- Softcover
Seller: INDOO, Avenel, NJ, U.S.A.INDOO
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- Softcover
Seller: INDOO, Avenel, NJ, U.S.A.INDOO
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US$ 51.03
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Condition: As New. Unread copy in mint condition.
- Softcover
Seller: BargainBookStores, Grand Rapids, MI, U.S.A.BargainBookStores
Contact seller5-star sellerCondition: New
US$ 53.36
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Paperback or Softback. Condition: New. Essential Graphrag: Knowledge Graph-Enhanced Rag. Book.
- Softcover
Seller: California Books, Miami, FL, U.S.A.California Books
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- Softcover
Seller: PBShop.store US, Wood Dale, IL, U.S.A.PBShop.store US
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US$ 55.45
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PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.
More images- Softcover
- First Edition
Seller: Rarewaves.com USA, London, LONDO, United KingdomRarewaves.com USA
Contact seller5-star sellerCondition: New
US$ 55.91
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Paperback. Condition: 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 ever…y 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.
- Softcover
Seller: PBShop.store US, Wood Dale, IL, U.S.A.PBShop.store US
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PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.
- Softcover
Seller: PBShop.store UK, Fairford, GLOS, United KingdomPBShop.store UK
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US$ 53.50
US$ 5.51 shippingShips from United Kingdom to U.S.A.Quantity: 15 available
PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.
- Softcover
Seller: Grand Eagle Retail, Bensenville, IL, U.S.A.Grand Eagle Retail
Contact seller5-star sellerCondition: New
US$ 60.32
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Paperback. Condition: 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 informatio…n 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.
- Softcover
Seller: PBShop.store UK, Fairford, GLOS, United KingdomPBShop.store UK
Contact seller5-star sellerCondition: New
US$ 55.12
US$ 6.65 shippingShips from United Kingdom to U.S.A.Quantity: 6 available
PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.
More images- Softcover
Seller: Rarewaves.com USA, London, LONDO, United KingdomRarewaves.com USA
Contact seller5-star sellerCondition: New
US$ 61.13
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Paperback. Condition: 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 gra…ph 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.
- Softcover
- First Edition
Seller: Rarewaves USA, OSWEGO, IL, U.S.A.Rarewaves USA
Contact seller5-star sellerCondition: New
US$ 61.39
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Paperback. Condition: 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 ever…y 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.
- Softcover
Seller: Chiron Media, Wallingford, , United KingdomChiron Media
Contact seller5-star sellerCondition: New
US$ 46.58
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paperback. Condition: New.
- Softcover
Seller: Chiron Media, Wallingford, , United KingdomChiron Media
Contact seller5-star sellerCondition: New
US$ 46.42
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paperback. Condition: New.
- Softcover
Seller: Russell Books, Victoria, BC, CanadaRussell Books
Contact seller5-star sellerCondition: New
Original price: US$ 59.98; 20% offCurrent price: US$ 47.99
US$ 19.99 shippingShips from Canada to U.S.A.Quantity: 11 available
paperback. Condition: New. Special order direct from the distributor.
- Softcover
Seller: Grand Eagle Retail, Bensenville, IL, U.S.A.Grand Eagle Retail
Contact seller5-star sellerCondition: New
US$ 67.98
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Paperback. Condition: 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-prop…erty 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.
- Softcover
Seller: Books Puddle, New York, NY, U.S.A.Books Puddle
Contact seller4-star sellerCondition: New
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Condition: New.
- Softcover
Seller: Chiron Media, Wallingford, , United KingdomChiron Media
Contact seller5-star sellerCondition: New
US$ 51.19
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paperback. Condition: New.
- Softcover
Seller: Majestic Books, Hounslow, , United KingdomMajestic Books
Contact seller4-star sellerCondition: New
US$ 62.49
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Condition: New.
- Softcover
Seller: Romtrade Corp., STERLING HEIGHTS, MI, U.S.A.Romtrade Corp.
Contact seller5-star sellerCondition: New
US$ 72.75
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Condition: 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.
- Softcover
Seller: THE SAINT BOOKSTORE, Southport, , United KingdomTHE SAINT BOOKSTORE
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US$ 53.88
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Paperback / softback. Condition: New. New copy - Usually dispatched within 2 working days.
- Softcover
Seller: Brook Bookstore On Demand, Napoli, NA, ItalyBrook Bookstore On Demand
Contact seller3-star sellerCondition: New
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Condition: new.
- Softcover
Seller: Kennys Bookshop and Art Galleries Ltd., Galway, GY, IrelandKennys Bookshop and Art Galleries Ltd.
Contact seller5-star sellerCondition: New
US$ 64.17
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Condition: New.
- Softcover
Seller: SMASS Sellers, IRVING, TX, U.S.A.SMASS Sellers
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US$ 75.73
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Condition: New. Brand New Original US Edition. Customer service! Satisfaction Guaranteed.
- Softcover
Seller: THE SAINT BOOKSTORE, Southport, , United KingdomTHE SAINT BOOKSTORE
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US$ 53.88
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Paperback / softback. Condition: New. New copy - Usually dispatched within 4 working days.
- Softcover
Seller: Chiron Media, Wallingford, , United KingdomChiron Media
Contact seller5-star sellerCondition: New
US$ 57.03
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paperback. Condition: New.
- Softcover
Seller: Biblios, frankfurt am main, HESSE, GermanyBiblios
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US$ 66.47
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Condition: New.
- Softcover
Seller: Rarewaves USA, OSWEGO, IL, U.S.A.Rarewaves USA
Contact seller5-star sellerCondition: New
US$ 77.85
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Paperback. Condition: 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 gra…ph 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.
