Advanced Algorithms Data

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1 - Chapter 1 Introducing data structures.mp4

09:08

2 - Part 1. Improving over basic data structures.mp4

02:13

3 - Chapter 1 Describing a data structure.mp4

06:15

4 - Chapter 1 Packing your knapsack - Data structures meet the real world.mp4

05:53

5 - Chapter 1 Algorithms to the rescue.mp4

09:54

6 - Chapter 2 Improving priority queues - d-way heaps.mp4

08:35

7 - Chapter 2 Solutions at hand - Keeping a sorted list.mp4

08:26

8 - Chapter 2 Concrete data structures.mp4

07:57

9 - Chapter 2 Priority, min-heap, and max-heap.mp4

05:54

10 - Chapter 2 How to implement a heap.mp4

08:13

11 - Chapter 2 PushDown.mp4

08:31

12 - Chapter 2 Top.mp4

09:27

13 - Chapter 2 Heapify.mp4

09:45

14 - Chapter 2 Use case - Find the k largest elements.mp4

05:35

15 - Chapter 2 More use cases.mp4

11:48

16 - Chapter 2 Analysis of branching factor.mp4

08:55

17 - Chapter 2 Performance analysis - Finding the best branching factor.mp4

08:53

18 - Chapter 2 Interpreting results.mp4

06:50

19 - Chapter 2 The mystery with heapify.mp4

06:24

20 - Chapter 3 Treaps - Using randomization to balance binary search trees.mp4

08:01

21 - Chapter 3 Treap.mp4

11:29

22 - Chapter 3 A few design questions.mp4

09:33

23 - Chapter 3 Delete.mp4

07:28

24 - Chapter 3 Applications - Randomized treaps.mp4

10:54

25 - Chapter 3 Performance analysis and profiling.mp4

07:31

26 - Chapter 3 Profiling height.mp4

11:56

27 - Chapter 3 Profiling memory usage.mp4

07:55

28 - Chapter 4 Bloom filters - Reducing the memory for tracking content.mp4

06:46

29 - Chapter 4 Alternatives to implementing a dictionary.mp4

05:09

30 - Chapter 4 Concrete data structures.mp4

08:12

31 - Chapter 4 Binary search tree - Every operation is logarithmic.mp4

09:56

32 - Chapter 4 Implementation.mp4

07:48

33 - Chapter 4 Constructor.mp4

08:38

34 - Chapter 4 Applications.mp4

08:42

35 - Chapter 4 Why Bloom filters work.mp4

08:23

36 - Chapter 4 Performance analysis.mp4

08:18

37 - Chapter 4 Explanation of the false-positive ratio formula.mp4

05:08

38 - Chapter 4 Improved variants.mp4

10:00

39 - Chapter 5 Disjoint sets - Sub-linear time processing.mp4

06:53

40 - Chapter 5 Reasoning on solutions.mp4

06:05

41 - Chapter 5 Naive solution.mp4

07:36

42 - Chapter 5 Using a tree-like structure.mp4

07:13

43 - Chapter 5 Heuristics to improve the running time.mp4

10:42

44 - Chapter 5 Applications.mp4

06:02

45 - Chapter 6 Trie, radix trie - Efficient string search.mp4

11:26

46 - Chapter 6 Trie.mp4

11:46

47 - Chapter 6 Search.mp4

09:23

48 - Chapter 6 Insert.mp4

09:53

49 - Chapter 6 Keys matching a prefix.mp4

08:41

50 - Chapter 6 Radix tries.mp4

10:20

51 - Chapter 6 Search.mp4

08:51

52 - Chapter 6 Applications.mp4

08:44

53 - Chapter 6 String sorting.mp4

08:58

54 - Chapter 7 Use case - LRU cache.mp4

10:44

55 - Chapter 7 First attempt - Remembering values.mp4

07:06

56 - Chapter 7 Handling asynchronous calls.mp4

05:34

57 - Chapter 7 Memory is not enough (literally).mp4

05:19

58 - Chapter 7 Getting rid of stale data - LRU cache.mp4

06:11

59 - Chapter 7 Temporal ordering.mp4

06:28

60 - Chapter 7 When fresher data is more valuable - LFU.mp4

10:25

61 - Chapter 7 How to use cache is just as important.mp4

08:56

62 - Chapter 7 Solving concurrency (in Java).mp4

10:31

63 - Chapter 7 Read locks.mp4

09:40

64 - Part 2. Multidimensional queries.mp4

03:15

65 - Chapter 8 Nearest neighbors search.mp4

07:29

66 - Chapter 8 Simplifying things to get a hint.mp4

08:25

67 - Chapter 8 Moving to k-dimensional spaces.mp4

08:23

68 - Chapter 9 K-d trees - Multidimensional data indexing.mp4

06:18

69 - Chapter 9 Constructing the BST.mp4

09:14

70 - Chapter 9 Methods.mp4

11:01

71 - Chapter 9 Balanced tree.mp4

07:24

72 - Chapter 9 Remove.mp4

12:55

73 - Chapter 9 Nearest neighbor.mp4

12:25

74 - Chapter 9 Region search.mp4

11:18

75 - Chapter 10 Similarity Search Trees - Approximate nearest neighbors search for image retrieval.mp4

08:17

76 - Chapter 10 R-tree.mp4

10:21

77 - Chapter 10 Inserting points in an R-tree.mp4

06:05

78 - Chapter 10 Similarity search tree.mp4

06:21

79 - Chapter 10 SS-tree search.mp4

06:44

80 - Chapter 10 Insert.mp4

12:09

81 - Chapter 10 Insertion - Split nodes.mp4

06:33

82 - Chapter 10 Delete.mp4

09:46

83 - Chapter 10 Similarity Search.mp4

05:29

84 - Chapter 10 Approximated similarity search.mp4

07:03

85 - Chapter 10 SS+-tree.mp4

09:21

86 - Chapter 10 Reducing overlap.mp4

06:35

87 - Chapter 11 Applications of nearest neighbor search.mp4

09:26

88 - Chapter 11.Centralized application.mp4

07:31

89 - Chapter 11 Moving to a distributed application.mp4

09:17

90 - Chapter 11 Other applications.mp4

08:19

91 - Chapter 11 Multidimensional DB queries optimization.mp4

06:02

92 - Chapter 12 Clustering.mp4

05:44

93 - Chapter 12 Types of learning.mp4

07:38

94 - Chapter 12 K-means.mp4

11:39

95 - Chapter 12 The curse of dimensionality strikes again.mp4

05:26

96 - Chapter 12 Boosting k-means with k-d trees.mp4

08:32

97 - Chapter 12 DBSCAN.mp4

05:33

98 - Chapter 12 From definitions to an algorithm.mp4

06:10

99 - Chapter 12 And finally, an implementation.mp4

07:30

100 - Chapter 12 OPTICS.mp4

11:49

101 - Chapter 12 From reachability distance to clustering.mp4

06:23

102 - Chapter 12 Hierarchical clustering.mp4

10:17

103 - Chapter 12. Evaluating clustering results - Evaluation metrics.mp4

11:09

104 - Chapter 13 Parallel clustering - MapReduce and canopy clustering.mp4

08:45

105 - Chapter 13 Canopy clustering.mp4

07:56

106 - Chapter 13 MapReduce.mp4

06:15

107 - Chapter 13 First map, then reduce.mp4

07:53

108 - Chapter 13 MapReduce k-means.mp4

07:09

109 - Chapter 13 Parallelizing canopy clustering.mp4

07:05

110 - Chapter 13 MapReduce canopy clustering.mp4

07:25

111 - Chapter 13 MapReduce DBSCAN - Part 1.mp4

08:46

112 - Chapter 13 MapReduce DBSCAN - Part 2.mp4

08:07

113 - Part 3. Planar graphs and minimum crossing number.mp4

02:21

114 - Chapter 14 An introduction to graphs - Finding paths of minimum distance.mp4

05:00

115 - Chapter 14 Implementing graphs.mp4

08:46

116 - Chapter 14 Graph properties.mp4

07:15

117 - Chapter 14 Graph traversal - BFS and DFS.mp4

10:24

118 - Chapter 14 Reconstructing the path to target.mp4

09:44

119 - Chapter 14 Shortest path in weighted graphs - Dijkstra.mp4

10:26

120 - Chapter 14 Beyond Dijkstras algorithm - A.mp4

05:06

121 - Chapter 14 How good is A search.mp4

07:56

122 - Chapter 14 Heuristics as a way to balance real-time data.mp4

05:05

123 - Chapter 15 Graph embeddings and planarity - Drawing graphs with minimal edge intersections.mp4

05:43

124 - Chapter 15 Some basic definitions.mp4

05:55

125 - Chapter 15 Planar graphs.mp4

05:21

126 - Chapter 15 Planarity testing.mp4

11:01

127 - Chapter 15 Improving performance.mp4

10:17

128 - Chapter 15 Non-planar graphs.mp4

07:43

129 - Chapter 15 Rectilinear crossing number.mp4

05:38

130 - Chapter 15 Edge intersections.mp4

06:52

131 - Chapter 15 Polylines.mp4

05:24

132 - Chapter 15 Intersections between quadratic Bezier curves.mp4

10:51

133 - Chapter 16 Gradient descent - Optimization problems (not just) on graphs.mp4

09:27

134 - Chapter 16 Did you just say heuristics.mp4

10:04

135 - Chapter 16 How optimization works.mp4

12:37

136 - Chapter 16 Gradient descent.mp4

09:16

137 - Chapter 16 When is gradient descent appliable.mp4

05:53

138 - Chapter 16 Applications of gradient descent.mp4

08:26

139 - Chapter 16 Gradient descent for graph embedding.mp4

10:31

140 - Chapter 17 Simulated annealing - Optimization beyond local minima.mp4

08:10

141 - Chapter 17 Sometimes you need to climb up to get to the bottom.mp4

05:17

142 - Chapter 17 Why simulated annealing works.mp4

06:53

143 - Chapter 17 Short-range vs long-range transitions.mp4

08:53

144 - Chapter 17 Simulated annealing + traveling salesman.mp4

05:23

145 - Chapter 17 Exact vs approximated solutions.mp4

07:20

146 - Chapter 17 State transitions.mp4

11:06

147 - Chapter 17 Simulated annealing and graph embedding.mp4

07:11

148 - Chapter 17 Force-directed drawing.mp4

10:31

149 - Chapter 18 Genetic algorithms - Biologically inspired, fast-converging optimization.mp4

06:30

150 - Chapter 18 Inspired by nature.mp4

09:56

151 - Chapter 18 Chromosomes.mp4

10:21

152 - Chapter 18 Natural selection.mp4

06:51

153 - Chapter 18 Selecting individuals for mating.mp4

13:01

154 - Chapter 18 Crossover.mp4

08:09

155 - Chapter 18 The genetic algorithm template.mp4

06:07

156 - Chapter 18 TSP.mp4

06:42

157 - Chapter 18 Results and parameters tuning.mp4

10:47

158 - Chapter 18 Minimum vertex cover.mp4

09:16

159 - Chapter 18 Other applications of the genetic algorithm.mp4

09:51

160 - Chapter 18 Beyond genetic algorithms.mp4

06:59

161 - Appendix A. A quick guide to pseudo-code.mp4

08:12

162 - Appendix A Conditional instructions.mp4

09:06

163 - Appendix A Blocks and indent.mp4

07:23

164 - Appendix B. Big-O notation.mp4

06:35

165 - Appendix B Notation.mp4

10:35

166 - Appendix C. Core data structures.mp4

10:22

167 - Appendix C Tree.mp4

08:41

168 - Appendix C Hash table.mp4

12:08

169 - Appendix D. Containers as priority queues.mp4

06:23

170 - Appendix E. Recursion.mp4

07:32

171 - Appendix E Tail recursion.mp4

05:24

172 - Appendix F. Classification problems and randomnized algorithm metrics.mp4

06:34

173 - Appendix F Classification metrics.mp4

07:06

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