Advanced Algorithms and Data Structures
- 16h 25m
- Marcello La Rocca
- Manning Publications
- 2021
Advanced Algorithms and Data Structures introduces a collection of algorithms for complex programming challenges in data analysis, machine learning, and graph computing.
Summary
As a software engineer, you’ll encounter countless programming challenges that initially seem confusing, difficult, or even impossible. Don’t despair! Many of these “new” problems already have well-established solutions. Advanced Algorithms and Data Structures teaches you powerful approaches to a wide range of tricky coding challenges that you can adapt and apply to your own applications. Providing a balanced blend of classic, advanced, and new algorithms, this practical guide upgrades your programming toolbox with new perspectives and hands-on techniques.
Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
About the technology
Can you improve the speed and efficiency of your applications without investing in new hardware? Well, yes, you can: Innovations in algorithms and data structures have led to huge advances in application performance. Pick up this book to discover a collection of advanced algorithms that will make you a more effective developer.
About the book
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.
What's inside
- Build on basic data structures you already know
- Profile your algorithms to speed up application
- Store and query strings efficiently
- Distribute clustering algorithms with MapReduce
- Solve logistics problems using graphs and optimization algorithms
In this Book
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Foreword
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About This Book
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Introducing Data Structures
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Improving Priority Queues—D-Way Heaps
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Treaps—Using Randomization to Balance Binary Search Trees
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Bloom Filters—Reducing the Memory for Tracking Content
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Disjoint Sets—Sub-Linear Time Processing
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Trie, Radix Trie—Efficient String Search
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Use Case—LRU Cache
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Nearest Neighbors Search
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K-d Trees—Multidimensional Data Indexing
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Similarity Search Trees—Approximate Nearest Neighbors Search for Image Retrieval
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Applications of Nearest Neighbor Search
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Clustering
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Parallel Clustering—MapReduce and Canopy Clustering
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An Introduction to Graphs—Finding Paths of Minimum Distance
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Graph Embeddings and Planarity—Drawing Graphs with Minimal Edge Intersections
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Gradient Descent—Optimization Problems (Not Just) on Graphs
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Simulated Annealing—Optimization Beyond Local Minima
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Genetic Algorithms—Biologically Inspired, Fast-Converging Optimization
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