Advanced Algorithms Data
Focused View
23:38:44
107 View
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
More details
User Reviews
Rating
average 0
Focused display
Category
PacktPub
View courses PacktPubPackt is a publishing company founded in 2003 headquartered in Birmingham, UK, with offices in Mumbai, India. Packt primarily publishes print and electronic books and videos relating to information technology, including programming, web design, data analysis and hardware.
- language english
- Training sessions 173
- duration 23:38:44
- Release Date 2023/05/15
