Advanced algorithms and data structures 1. Introducing 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 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 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. Can you improve the speed and efficiency of your applications without investing in new hardware Well yest you an: 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. Advanced algorithms and data structures introduces a collection of algorithms for complex programming challenges in data analysis machine learning and graph computing. Youll discover cutting edge approaches to a variety of tricky scenarios. Youll even learn to design your own data structures for projects that require a custom solution