Data structures algorithms generative de liao david (4 resultados)

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

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

Condición: New. Print on Demand.

Paperback. Condición: new. Paperback. Learn the foundations of Data Structures and Algorithms while discovering how they power modern software and Generative AI systems.Data Structures and Algorithms in the Era of GenAI presents the essential topics of a traditional undergraduate DSA course through a practical, contemporary lens. Using Python, the book explains not only how common data structures and algorithms work, but also why they matter in applications involving large language models, retrieval systems, intelligent agents, caching, ranking, and data processing.The book begins with Python and object-oriented programming foundations, then connects Big-O analysis to real-world performance measures such as latency, throughput, memory use, and scalability. It develops the major structures and algorithms step by step, including: Arrays, lists, strings, and sequence-processing patternsLinked lists, stacks, queues, and dequesTrees, binary search trees, AVL trees, and Red-Black treesHeaps and priority queuesGraph representations, traversal, shortest paths, and spanning treesHash tables, dictionaries, sets, caches, and memoizationLinear search, binary search, and search variantsClassic sorting algorithms, Timsort, partial sorting, and Top-K selectionBridge Theory with Modern AI PracticeGenerative AI examples are used to clarify the computer science rather than replace it. Readers will see how strings support prompt and response processing, queues manage requests and streaming work, heaps select the best candidates, graphs model workflows and relationships, hash tables support caching and token mappings, and searching and sorting contribute to retrieval and ranking.Who This Book Is ForDesigned for undergraduate students, self-learners, instructors, and industry practitioners, this book requires no prior background in machine learning. Its goal is to help readers move beyond memorizing complexity tables and develop the judgment to choose the right structure for a problem, analyze its tradeoffs, evaluate AI-generated code, and build software that remains efficient as it scales.Whether you are preparing for a Data Structures course, strengthening your programming foundations, or building modern AI-enabled applications, this book provides the concepts and practical reasoning needed to design better systems. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…