Magnetz

Title: MANNING - GraphGroup: NOGRPSource: Manning

Info Hash

0DE85E2EB7E10C2A64EB479F64F61EEE47B23B48

Source

Unverified

Total Size

4.83 GB

Total Files

89

Seeders

1

Leechers

0

Health

1.00

Score

2

Type

Bookware

File List

File	Size
0. Websites you may like/1. Get Free Premium Accounts Daily On Our Discord Server!.txt	1.32 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	21.31 MB
02-Chapter 1 Machine learning and graphs - An introduction.mp4	69.7 MB
03-Chapter 1 Business understanding.mp4	39.1 MB
04-Chapter 1 Machine learning challenges.mp4	49.84 MB
05-Chapter 1 Performance.mp4	53.14 MB
06-Chapter 1 Graphs.mp4	33.32 MB
07-Chapter 1 Graphs as models of networks.mp4	71.29 MB
08-Chapter 1 The role of graphs in machine learning.mp4	73.83 MB
09-Chapter 2 Graph data engineering.mp4	82.01 MB
10-Chapter 2 Velocity.mp4	50.81 MB
11-Chapter 2 Graphs in the big data platform.mp4	49.38 MB
12-Chapter 2 Graphs are valuable for big data.mp4	43.18 MB
13-Chapter 2 Graphs are valuable for master data management.mp4	75.67 MB
14-Chapter 2 Graph databases.mp4	52.12 MB
15-Chapter 2 Sharding.mp4	70.52 MB
16-Chapter 2 Native vs. non-native graph databases.mp4	79.92 MB
17-Chapter 2 Label property graphs.mp4	37.69 MB
18-Chapter 3 Graphs in machine learning applications.mp4	65.87 MB
19-Chapter 3 Managing data sources.mp4	77.36 MB
20-Chapter 3 Detect a fraud.mp4	52.33 MB
21-Chapter 3 Recommend items.mp4	63.56 MB
22-Chapter 3 Algorithms.mp4	48.19 MB
23-Chapter 3 Find keywords in a document.mp4	53.6 MB
24-Chapter 3 Storing and accessing machine learning models.mp4	31.38 MB
25-Chapter 3 Monitoring a subject.mp4	55.54 MB
26-Chapter 3 Visualization.mp4	37.9 MB
27-Chapter 3 Leftover - Deep learning and graph neural networks.mp4	52.78 MB
28-Part 2 Recommendations.mp4	148.91 MB
29-Chapter 4 Content-based recommendations.mp4	67.48 MB
30-Chapter 4 Representing item features.mp4	63.39 MB
31-Chapter 4 Representing item features.mp4	60.23 MB
32-Chapter 4 User modeling.mp4	33.57 MB
33-Chapter 4 Providing recommendations.mp4	56.79 MB
34-Chapter 4 Providing recommendations.mp4	66.34 MB
35-Chapter 4 Providing recommendations.mp4	72.6 MB
36-Chapter 5 Collaborative filtering.mp4	98.97 MB
37-Chapter 5 Collaborative filtering recommendations.mp4	92.75 MB
38-Chapter 5 Computing the nearest neighbor network.mp4	69.04 MB
39-Chapter 5 Computing the nearest neighbor network.mp4	47.87 MB
40-Chapter 5 Providing recommendations.mp4	53.76 MB
41-Chapter 5 Dealing with the cold-start problem.mp4	40.18 MB
42-Chapter 6 Session-based recommendations.mp4	61.79 MB
43-Chapter 6 The events chain and the session graph.mp4	68.35 MB
44-Chapter 6 Providing recommendations.mp4	81.3 MB
45-Chapter 6 Session-based k-NN.mp4	63.6 MB
46-Chapter 7 Context-aware and hybrid recommendations.mp4	67.6 MB
47-Chapter 7 Representing contextual information.mp4	42.88 MB
48-Chapter 7 Providing recommendations.mp4	85.94 MB
49-Chapter 7 Providing recommendations.mp4	85.12 MB
50-Chapter 7 Advantages of the graph approach.mp4	51.81 MB
51-Chapter 7 Providing recommendations.mp4	38.56 MB
52-Part 3 Fighting fraud.mp4	34.38 MB
53-Chapter 8 Basic approaches to graph-powered fraud detection.mp4	48.49 MB
54-Chapter 8 Fraud prevention and detection.mp4	45.24 MB
55-Chapter 8 The role of graphs in fighting fraud.mp4	47.11 MB
56-Chapter 8 Warm-up - Basic approaches.mp4	55.49 MB
57-Chapter 8 Identifying a fraud ring.mp4	46.91 MB
58-Chapter 9 Proximity-based algorithms.mp4	68.99 MB
59-Chapter 9 Distance-based approach.mp4	49.88 MB
60-Chapter 9 Creating the k-nearest neighbors graph.mp4	52.11 MB
61-Chapter 9 Identifying fraudulent transactions.mp4	82.58 MB
62-Chapter 9 Identifying fraudulent transactions.mp4	32.51 MB
63-Chapter 10 Social network analysis against fraud.mp4	79.64 MB
64-Chapter 10 Social network analysis concepts.mp4	46.44 MB
65-Chapter 10 Score-based methods.mp4	32.24 MB
66-Chapter 10 Neighborhood metrics.mp4	45.87 MB
67-Chapter 10 Centrality metrics.mp4	61.27 MB
68-Chapter 10 Collective inference algorithms.mp4	50.6 MB
69-Chapter 10 Cluster-based methods.mp4	65.65 MB
70-Part 4 Taming text with graphs.mp4	24.45 MB
71-Chapter 11 Graph-based natural language processing.mp4	57.65 MB
72-Chapter 11 A basic approach - Store and access sequence of words.mp4	53.54 MB
73-Chapter 11 NLP and graphs.mp4	80.48 MB
74-Chapter 11 NLP and graphs.mp4	70.02 MB
75-Chapter 12 Knowledge graphs.mp4	60.09 MB
76-Chapter 12 Knowledge graph building - Entities.mp4	94.08 MB
77-Chapter 12 Knowledge graph building - Relationships.mp4	68.65 MB
78-Chapter 12 Semantic networks.mp4	38.36 MB
79-Chapter 12 Unsupervised keyword extraction.mp4	52.87 MB
80-Chapter 12 Unsupervised keyword extraction.mp4	35.89 MB
81-Chapter 12 Keyword co-occurrence graph.mp4	50.57 MB
82-Appendix A. Machine learning algorithms taxonomy.mp4	65.16 MB
83-Appendix C Graphs for processing patterns and workflows.mp4	43.83 MB
84-Appendix C Graphs for defining complex processing workflows.mp4	50.43 MB
85-Appendix D. Representing graphs.mp4	40.52 MB

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