Carter Zhang (25 results)

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Paperback. Condition: new. Paperback. Knowledge Graphs for AI Engineers is an end-to-end technical guide for building and operating knowledge graphs that power modern AI systems. Starting with ontology and schema design, this book provides concrete blueprints for modeling domain knowledge, representing triples, and handling taxonomies and hierarchical relationships. You'll find pragmatic guidance on selecting graph stores (Neo4j, JanusGraph, ArangoDB, RDF triple stores), designing ETL and ingestion processes, and implementing entity resolution and canonicalization pipelines.A central theme is converting graph semantics into numerical representations: learn KG embedding algorithms, vectorization strategies, and how to integrate KG embeddings into hybrid retrieval and RAG flows. The book covers query languages (SPARQL and Cypher), rule-based reasoning, and integrating inference engines with LLMs to create grounded, explainable responses. Operational topics include governance, schema evolution, privacy, security, visualization, and scaling patterns. Case studies illustrate clinical knowledge graphs, enterprise product catalogs, and customer-support knowledge bases.What's inside: Ontology and schema blueprints for common domains.ETL patterns and pipeline templates for reliable KG ingestion.Entity linking, canonicalization, and de-duplication best practices.KG embeddings, graph neural net overviews, and conversion to vector indexes.Query examples in SPARQL and Cypher tuned for retrieval and analytics.Integration patterns for combining graph queries with vector search in RAG stacks.Reasoning and rule-engine examples for inference and business rules.Visualization and analytics workflows for KG insights and reporting.Governance: access control, data provenance, compliance, and lifecycle management.Scaling, replication, and operational playbooks for production KGs.Who this book is for: Knowledge engineers, data engineers, ML engineers, and technical product teams building graph-backed AI.Enterprises needing explainability, versioning, and governance in their AI pipelines.Research teams and tool-builders interested in KG embeddings and hybrid retrieval. 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.

Paperback. Condition: new. Paperback. Context Engineering for LLMs is the operational handbook for anyone who wants LLMs to behave predictably, efficiently, and responsibly in production. The book reframes prompt engineering as a systems discipline context pipelines that include chunking, compression, vectorization, retrieval orchestration, and memory layers. You'll learn token-economics for large context windows, practical prompt architectures, system and user message patterns, and templates for common tasks.The middle sections cover embedding strategies, vector-store patterns, and RAG designs that ensure relevance, freshness, and cost control. Memory system chapters describe how to design short-term and long-term memory, when to use episodic memory, and how to index and expire context. Security and reliability are core themes: learn prompt injection defenses, context validation, and audit trails. The book closes with evaluation, A/B testing, CI pipelines for prompt changes, and operational patterns for continuous improvement.What's inside: Practical prompt templates and system-message strategies for structured outputs.Token cost modeling and context-window optimization techniques.Chunking, semantic compression, and fragment re-assembly recipes.Embedding strategies, batching, and hybrid index maintenance.Memory architectures: ephemeral, episodic, and persistent memory designs.RAG workflows and retrieval orchestration best practices.Prompt-injection defenses, content filtering, and context validation checks.Evaluation frameworks, metrics, and test suites for context quality.CI/CD for prompts and context pipelines, plus A/B testing patterns.Operational playbooks for latency, scaling, and cost tradeoffs.Who this book is for: Prompt engineers, ML engineers, SREs, and product teams shipping LLM features.Teams that require predictable, auditable, and cost-effective LLM behavior. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. …

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Paperback. Condition: new. Paperback. LangGraph for Knowledge-Driven LLMs shows how to combine graph-structured knowledge with large language models to produce more accurate, explainable, and maintainable AI systems. The book introduces LangGraph concepts, data models, and connectors, and walks through full ingestion pipelines that convert raw documents into triples, entities, and canonical nodes. Learn entity resolution and linking techniques that reduce ambiguity, maintain provenance, and make knowledge updates straightforward.A major focus is on converting graph structure into vector representations and building hybrid retrieval flows that combine graph queries with vector similarity search. You'll learn how to craft graph-aware context assembly and prompting strategies so LLMs can reason with structured knowledge and return traceable answers. The book also covers graph embeddings, graph neural nets, explainability patterns, and operational best practices for indexing, monitoring, and schema evolution. Real-world case studies demonstrate customer-support assistants, domain expert systems, and product catalogs that use LangGraph for domain grounding and faster iteration.What's inside: LangGraph architecture explained with connector and transform examples.Pipelines from documents to triples, to graph stores, to vector indexes.Entity linking, canonicalization, deduplication, and schema evolution patterns.Graph vector conversion: embedding strategies, batching, and incremental updates.Hybrid retrieval recipes: combining SPARQL/Cypher-like graph constraints with vector similarity.Prompting patterns that leverage graph provenance and traceability.Agents that consult LangGraph for planning, grounding, and action execution.Monitoring, explainability, and provenance tooling for regulated domains.Integration examples with Neo4j, ArangoDB, and common vector DBs.Performance tuning, consistency approaches, and operational checklists.Who this book is for: Data engineers, knowledge engineers, and ML engineers building knowledge-first LLM applications.Teams seeking explainability, auditability, and updatability in AI systems.Product managers and architects planning hybrid retrieval or graph-backed assistants. 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. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Condition: New. Print on Demand.

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.

Condition: New. Print on Demand.

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.

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. Condition: New. Paperback. Pub Date: 2015-09-01 Pages: 200 Language: English. Chinese Publisher: Foreign Language Teaching and Research Press Big Cat English Graded Reading Level 3 1 includes Around the World. Light. Brand New Kite. Where is My School?? The Adventures of Little Egg. Dancing to the Rhythm. Trick or Treating Big Cat Sam. Party Guide and Percy and the Bunny 9 English stories. a supporting book Family Reading Instruction Manual. and an MP3 CD.?These 9 English books include fictional stories and. …

Pas de couverture. Condition: Assez bon. - Rare & exceptional - Large poster signed in person at the 2006 Cannes Film Festival by most of the jury and Sidney Poitier: Wong Kar-wai, Monica Bellucci, Helena Bonham Carter, Samuel L. Jackson, Tim Roth, Zhang Ziyi, and Patrice Leconte. The poster and the COA will be shipped in a cardboard tube. Size : 80x60 cm. Condition : signs of moisture, slightly wrinkled, worn edges, please see scans. Certificate of Authenticity and lifetime guarantee. Signé par l'auteur.…

Paperback. Condition: new. Paperback. Knowledge Graphs for AI Engineers is an end-to-end technical guide for building and operating knowledge graphs that power modern AI systems. Starting with ontology and schema design, this book provides concrete blueprints for modeling domain knowledge, representing triples, and handling taxonomies and hierarchical relationships. You'll find pragmatic guidance on selecting graph stores (Neo4j, JanusGraph, ArangoDB, RDF triple stores), designing ETL and ingestion processes, and implementing entity resolution and canonicalization pipelines.A central theme is converting graph semantics into numerical representations: learn KG embedding algorithms, vectorization strategies, and how to integrate KG embeddings into hybrid retrieval and RAG flows. The book covers query languages (SPARQL and Cypher), rule-based reasoning, and integrating inference engines with LLMs to create grounded, explainable responses. Operational topics include governance, schema evolution, privacy, security, visualization, and scaling patterns. Case studies illustrate clinical knowledge graphs, enterprise product catalogs, and customer-support knowledge bases.What's inside: Ontology and schema blueprints for common domains.ETL patterns and pipeline templates for reliable KG ingestion.Entity linking, canonicalization, and de-duplication best practices.KG embeddings, graph neural net overviews, and conversion to vector indexes.Query examples in SPARQL and Cypher tuned for retrieval and analytics.Integration patterns for combining graph queries with vector search in RAG stacks.Reasoning and rule-engine examples for inference and business rules.Visualization and analytics workflows for KG insights and reporting.Governance: access control, data provenance, compliance, and lifecycle management.Scaling, replication, and operational playbooks for production KGs.Who this book is for: Knowledge engineers, data engineers, ML engineers, and technical product teams building graph-backed AI.Enterprises needing explainability, versioning, and governance in their AI pipelines.Research teams and tool-builders interested in KG embeddings and hybrid retrieval. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. …

Paperback. Condition: new. Paperback. Context Engineering for LLMs is the operational handbook for anyone who wants LLMs to behave predictably, efficiently, and responsibly in production. The book reframes prompt engineering as a systems discipline context pipelines that include chunking, compression, vectorization, retrieval orchestration, and memory layers. You'll learn token-economics for large context windows, practical prompt architectures, system and user message patterns, and templates for common tasks.The middle sections cover embedding strategies, vector-store patterns, and RAG designs that ensure relevance, freshness, and cost control. Memory system chapters describe how to design short-term and long-term memory, when to use episodic memory, and how to index and expire context. Security and reliability are core themes: learn prompt injection defenses, context validation, and audit trails. The book closes with evaluation, A/B testing, CI pipelines for prompt changes, and operational patterns for continuous improvement.What's inside: Practical prompt templates and system-message strategies for structured outputs.Token cost modeling and context-window optimization techniques.Chunking, semantic compression, and fragment re-assembly recipes.Embedding strategies, batching, and hybrid index maintenance.Memory architectures: ephemeral, episodic, and persistent memory designs.RAG workflows and retrieval orchestration best practices.Prompt-injection defenses, content filtering, and context validation checks.Evaluation frameworks, metrics, and test suites for context quality.CI/CD for prompts and context pipelines, plus A/B testing patterns.Operational playbooks for latency, scaling, and cost tradeoffs.Who this book is for: Prompt engineers, ML engineers, SREs, and product teams shipping LLM features.Teams that require predictable, auditable, and cost-effective LLM behavior. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. …

Paperback. Condition: new. Paperback. LangGraph for Knowledge-Driven LLMs shows how to combine graph-structured knowledge with large language models to produce more accurate, explainable, and maintainable AI systems. The book introduces LangGraph concepts, data models, and connectors, and walks through full ingestion pipelines that convert raw documents into triples, entities, and canonical nodes. Learn entity resolution and linking techniques that reduce ambiguity, maintain provenance, and make knowledge updates straightforward.A major focus is on converting graph structure into vector representations and building hybrid retrieval flows that combine graph queries with vector similarity search. You'll learn how to craft graph-aware context assembly and prompting strategies so LLMs can reason with structured knowledge and return traceable answers. The book also covers graph embeddings, graph neural nets, explainability patterns, and operational best practices for indexing, monitoring, and schema evolution. Real-world case studies demonstrate customer-support assistants, domain expert systems, and product catalogs that use LangGraph for domain grounding and faster iteration.What's inside: LangGraph architecture explained with connector and transform examples.Pipelines from documents to triples, to graph stores, to vector indexes.Entity linking, canonicalization, deduplication, and schema evolution patterns.Graph vector conversion: embedding strategies, batching, and incremental updates.Hybrid retrieval recipes: combining SPARQL/Cypher-like graph constraints with vector similarity.Prompting patterns that leverage graph provenance and traceability.Agents that consult LangGraph for planning, grounding, and action execution.Monitoring, explainability, and provenance tooling for regulated domains.Integration examples with Neo4j, ArangoDB, and common vector DBs.Performance tuning, consistency approaches, and operational checklists.Who this book is for: Data engineers, knowledge engineers, and ML engineers building knowledge-first LLM applications.Teams seeking explainability, auditability, and updatability in AI systems.Product managers and architects planning hybrid retrieval or graph-backed assistants. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…