TorBT - Torrents and Magnet Links Search Engine
Latest Searches: walking+dead, after+everything+2023, covers, %e5%9b%bd%e4%ba%a7, %d0%b4%d0%be%d0%bc%d0%b0%d1%88%d0%bd%d0%b5%d0%b5, insertion, vr, no+limits, boku+dake, %d0%b2%d1%8b%d1%81%d0%be%d1%86%d0%ba%d0%b8%d0%b9

[FreeCoursesOnline.io] MANNING - Graph-Powered Machine Learning [Video Edition]

Magnet Download Download Torrent
File Name
Size
0. Websites you may like/1. Get Free Premium Accounts Daily On Our Discord Server!.txt
1.3 kB
0. Websites you may like/2. OneHack.us Premium Cracked Accounts-Tutorials-Guides-Articles Community Based Forum.url
377 B
0. Websites you may like/3. FTUApps.com Download Cracked Developers Applications For Free.url
239 B
0. Websites you may like/4. FreeCoursesOnline.io Download Udacity, Masterclass, Lynda, PHLearn, etc Free.url
290 B
01-Part 1 Introduction.mp4
21 MB
02-Chapter 1 Machine learning and graphs - An introduction.mp4
70 MB
03-Chapter 1 Business understanding.mp4
39 MB
04-Chapter 1 Machine learning challenges.mp4
50 MB
05-Chapter 1 Performance.mp4
53 MB
06-Chapter 1 Graphs.mp4
33 MB
07-Chapter 1 Graphs as models of networks.mp4
71 MB
08-Chapter 1 The role of graphs in machine learning.mp4
74 MB
09-Chapter 2 Graph data engineering.mp4
82 MB
10-Chapter 2 Velocity.mp4
51 MB
11-Chapter 2 Graphs in the big data platform.mp4
49 MB
12-Chapter 2 Graphs are valuable for big data.mp4
43 MB
13-Chapter 2 Graphs are valuable for master data management.mp4
76 MB
14-Chapter 2 Graph databases.mp4
52 MB
15-Chapter 2 Sharding.mp4
70 MB
16-Chapter 2 Native vs. non-native graph databases.mp4
80 MB
17-Chapter 2 Label property graphs.mp4
38 MB
18-Chapter 3 Graphs in machine learning applications.mp4
66 MB
19-Chapter 3 Managing data sources.mp4
77 MB
20-Chapter 3 Detect a fraud.mp4
52 MB
21-Chapter 3 Recommend items.mp4
64 MB
22-Chapter 3 Algorithms.mp4
48 MB
23-Chapter 3 Find keywords in a document.mp4
54 MB
24-Chapter 3 Storing and accessing machine learning models.mp4
31 MB
25-Chapter 3 Monitoring a subject.mp4
56 MB
26-Chapter 3 Visualization.mp4
38 MB
27-Chapter 3 Leftover - Deep learning and graph neural networks.mp4
53 MB
28-Part 2 Recommendations.mp4
149 MB
29-Chapter 4 Content-based recommendations.mp4
68 MB
30-Chapter 4 Representing item features.mp4
63 MB
31-Chapter 4 Representing item features.mp4
60 MB
32-Chapter 4 User modeling.mp4
34 MB
33-Chapter 4 Providing recommendations.mp4
57 MB
34-Chapter 4 Providing recommendations.mp4
66 MB
35-Chapter 4 Providing recommendations.mp4
73 MB
36-Chapter 5 Collaborative filtering.mp4
99 MB
37-Chapter 5 Collaborative filtering recommendations.mp4
93 MB
38-Chapter 5 Computing the nearest neighbor network.mp4
69 MB
39-Chapter 5 Computing the nearest neighbor network.mp4
48 MB
40-Chapter 5 Providing recommendations.mp4
54 MB
41-Chapter 5 Dealing with the cold-start problem.mp4
40 MB
42-Chapter 6 Session-based recommendations.mp4
62 MB
43-Chapter 6 The events chain and the session graph.mp4
68 MB
44-Chapter 6 Providing recommendations.mp4
81 MB
45-Chapter 6 Session-based k-NN.mp4
64 MB
46-Chapter 7 Context-aware and hybrid recommendations.mp4
68 MB
47-Chapter 7 Representing contextual information.mp4
43 MB
48-Chapter 7 Providing recommendations.mp4
86 MB
49-Chapter 7 Providing recommendations.mp4
85 MB
50-Chapter 7 Advantages of the graph approach.mp4
52 MB
51-Chapter 7 Providing recommendations.mp4
39 MB
52-Part 3 Fighting fraud.mp4
34 MB
53-Chapter 8 Basic approaches to graph-powered fraud detection.mp4
48 MB
54-Chapter 8 Fraud prevention and detection.mp4
45 MB
55-Chapter 8 The role of graphs in fighting fraud.mp4
47 MB
56-Chapter 8 Warm-up - Basic approaches.mp4
56 MB
57-Chapter 8 Identifying a fraud ring.mp4
47 MB
58-Chapter 9 Proximity-based algorithms.mp4
69 MB
59-Chapter 9 Distance-based approach.mp4
50 MB
60-Chapter 9 Creating the k-nearest neighbors graph.mp4
52 MB
61-Chapter 9 Identifying fraudulent transactions.mp4
83 MB
62-Chapter 9 Identifying fraudulent transactions.mp4
32 MB
63-Chapter 10 Social network analysis against fraud.mp4
80 MB
64-Chapter 10 Social network analysis concepts.mp4
46 MB
65-Chapter 10 Score-based methods.mp4
32 MB
66-Chapter 10 Neighborhood metrics.mp4
46 MB
67-Chapter 10 Centrality metrics.mp4
61 MB
68-Chapter 10 Collective inference algorithms.mp4
51 MB
69-Chapter 10 Cluster-based methods.mp4
66 MB
70-Part 4 Taming text with graphs.mp4
24 MB
71-Chapter 11 Graph-based natural language processing.mp4
58 MB
72-Chapter 11 A basic approach - Store and access sequence of words.mp4
54 MB
73-Chapter 11 NLP and graphs.mp4
80 MB
74-Chapter 11 NLP and graphs.mp4
70 MB
75-Chapter 12 Knowledge graphs.mp4
60 MB
76-Chapter 12 Knowledge graph building - Entities.mp4
94 MB
77-Chapter 12 Knowledge graph building - Relationships.mp4
69 MB
78-Chapter 12 Semantic networks.mp4
38 MB
79-Chapter 12 Unsupervised keyword extraction.mp4
53 MB
80-Chapter 12 Unsupervised keyword extraction.mp4
36 MB
81-Chapter 12 Keyword co-occurrence graph.mp4
51 MB
82-Appendix A. Machine learning algorithms taxonomy.mp4
65 MB
83-Appendix C Graphs for processing patterns and workflows.mp4
44 MB
84-Appendix C Graphs for defining complex processing workflows.mp4
50 MB
85-Appendix D. Representing graphs.mp4
40 MB