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Advanced algorithms and data structures / Marcello La Rocca

By: Material type: TextTextPublication details: Shelter Island, NY : Manning Publications, c2021Description: xxvii, 735.pages : illustrations ; 24 cmISBN:
  • 9781617295485
Subject(s): LOC classification:
  • QA 76.9 .R63 2021
Contents:
1 Introducing data structures -- PART 1 IMPROVING OVER BASIC DATA STRUCTURES -- 2 Improving priority queues: d-way heaps -- 3 Treaps: Using randomization to balance binary search trees -- 4 Bloom filters: Reducing the memory for tracking content -- 5 Disjoint sets: Sub-linear time processing -- 6 Trie, radix trie: Efficient string search -- 7 Use case: LRU cache -- PART 2 MULTIDEMENSIONAL QUERIES -- 8 Nearest neighbors search -- 9 K-d trees: Multidimensional data indexing -- 10 Similarity Search Trees: Approximate nearest neighbors search for image retrieval -- 11 Applications of nearest neighbor search -- 12 Clustering -- 13 Parallel clustering: MapReduce and canopy clustering -- PART 3 PLANAR GRAPHS AND MINIMUM CROSSING NUMBER -- 14 An introduction to graphs: Finding paths of minimum distance -- 15 Graph embeddings and planarity: Drawing graphs with minimal edge intersections -- 16 Gradient descent: Optimization problems (not just) on graphs -- 17 Simulated annealing: Optimization beyond local minima -- 18 Genetic algorithms: Biologically inspired, fast-converging optimization.
Summary: Advanced Algorithms and Data Structures introduces a collection of algorithms for complex programming challenges in data analysis, machine learning, and graph computing. You'll discover cutting-edge approaches to a variety of tricky scenarios. You'll even learn to design your own data structures for projects that require a custom solution
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Holdings
Item type Current library Home library Collection Call number Copy number Status Date due Barcode
Books Books National University - Manila LRC - Main General Circulation Digital Forensic GC QA 76.9 .R63 2021 (Browse shelf(Opens below)) c.1 Available NULIB000019528

Includes index.

1 Introducing data structures -- PART 1 IMPROVING OVER BASIC DATA STRUCTURES -- 2 Improving priority queues: d-way heaps -- 3 Treaps: Using randomization to balance binary search trees -- 4 Bloom filters: Reducing the memory for tracking content -- 5 Disjoint sets: Sub-linear time processing -- 6 Trie, radix trie: Efficient string search -- 7 Use case: LRU cache -- PART 2 MULTIDEMENSIONAL QUERIES -- 8 Nearest neighbors search -- 9 K-d trees: Multidimensional data indexing -- 10 Similarity Search Trees: Approximate nearest neighbors search for image retrieval -- 11 Applications of nearest neighbor search -- 12 Clustering -- 13 Parallel clustering: MapReduce and canopy clustering -- PART 3 PLANAR GRAPHS AND MINIMUM CROSSING NUMBER -- 14 An introduction to graphs: Finding paths of minimum distance -- 15 Graph embeddings and planarity: Drawing graphs with minimal edge intersections -- 16 Gradient descent: Optimization problems (not just) on graphs -- 17 Simulated annealing: Optimization beyond local minima -- 18 Genetic algorithms: Biologically inspired, fast-converging optimization.

Advanced Algorithms and Data Structures introduces a collection of algorithms for complex programming challenges in data analysis, machine learning, and graph computing. You'll discover cutting-edge approaches to a variety of tricky scenarios. You'll even learn to design your own data structures for projects that require a custom solution

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