Download Graph-Powered Machine Learning, Video Edition torrent

Graph-Powered Machine Learning, Video Edition

Torrent:	Graph-Powered Machine Learning, Video Edition
Description:	Description Upgrade your machine learning models with graph-based algorithms, the perfect structure for complex and interlinked data In Graph-Powered Machine Learning you will learn The lifecycle of a machine learning project Graphs in big data platforms Data source modeling using graphs Graph-based natural language processing, recommendations, and fraud detection techniques Graph algorithms Working with Neo4J Graph-Powered Machine Learning teaches to use graph-based algorithms and data organization strategies to develop superior machine learning applications. You’ll dive into the role of graphs in machine learning and big data platforms, and take an in-depth look at data source modeling, algorithm design, recommendations, and fraud detection. Explore end-to-end projects that illustrate architectures and help you optimize with best design practices. Author Alessandro Negro’s extensive experience shines through in every chapter, as you learn from examples and concrete scenarios based on his work with real clients! about the technology Identifying relationships is the foundation of machine learning. By recognizing and analyzing the connections in your data, graph-centric algorithms like K-nearest neighbor or PageRank radically improve the effectiveness of ML applications. Graph-based machine learning techniques offer a powerful new perspective for machine learning in social networking, fraud detection, natural language processing, and recommendation systems. about the book Graph-Powered Machine Learning teaches you how to exploit the natural relationships in structured and unstructured datasets using graph-oriented machine learning algorithms and tools. In this authoritative book, you’ll master the architectures and design practices of graphs, and avoid common pitfalls. Author Alessandro Negro explores examples from real-world applications that connect GraphML concepts to real world tasks. about the audience For readers comfortable with machine learning basics. Published 9/2021
Downloads:	110
Category:	Other /Tutorials
Size:	4.9 GB
Show Files »	Graph-Powered Machine Learning, Video Edition [TutsNode.com] - Graph-Powered Machine Learning, Video Edition 28-Part 2 Recommendations.mp4 (148.9 MB) 36-Chapter 5 Collaborative filtering.mp4 (99.0 MB) 76-Chapter 12 Knowledge graph building - Entities.mp4 (94.1 MB) 37-Chapter 5 Collaborative filtering recommendations.mp4 (92.7 MB) 48-Chapter 7 Providing recommendations.mp4 (85.9 MB) 49-Chapter 7 Providing recommendations.mp4 (85.1 MB) 61-Chapter 9 Identifying fraudulent transactions.mp4 (82.6 MB) 09-Chapter 2 Graph data engineering.mp4 (82.0 MB) 44-Chapter 6 Providing recommendations.mp4 (81.3 MB) 73-Chapter 11 NLP and graphs.mp4 (80.5 MB) 16-Chapter 2 Native vs. non-native graph databases.mp4 (79.9 MB) 63-Chapter 10 Social network analysis against fraud.mp4 (79.6 MB) 19-Chapter 3 Managing data sources.mp4 (77.4 MB) 13-Chapter 2 Graphs are valuable for master data management.mp4 (75.7 MB) 08-Chapter 1 The role of graphs in machine learning.mp4 (73.8 MB) 35-Chapter 4 Providing recommendations.mp4 (72.6 MB) 07-Chapter 1 Graphs as models of networks.mp4 (71.3 MB) 15-Chapter 2 Sharding.mp4 (70.5 MB) 74-Chapter 11 NLP and graphs.mp4 (70.0 MB) 02-Chapter 1 Machine learning and graphs - An introduction.mp4 (69.7 MB) 38-Chapter 5 Computing the nearest neighbor network.mp4 (69.0 MB) 58-Chapter 9 Proximity-based algorithms.mp4 (69.0 MB) 77-Chapter 12 Knowledge graph building - Relationships.mp4 (68.6 MB) 43-Chapter 6 The events chain and the session graph.mp4 (68.3 MB) 46-Chapter 7 Context-aware and hybrid recommendations.mp4 (67.6 MB) 29-Chapter 4 Content-based recommendations.mp4 (67.5 MB) 34-Chapter 4 Providing recommendations.mp4 (66.3 MB) 18-Chapter 3 Graphs in machine learning applications.mp4 (65.9 MB) 69-Chapter 10 Cluster-based methods.mp4 (65.7 MB) 82-Appendix A. Machine learning algorithms taxonomy.mp4 (65.2 MB) 45-Chapter 6 Session-based k-NN.mp4 (63.6 MB) 21-Chapter 3 Recommend items.mp4 (63.6 MB) 30-Chapter 4 Representing item features.mp4 (63.4 MB) 42-Chapter 6 Session-based recommendations.mp4 (61.8 MB) 67-Chapter 10 Centrality metrics.mp4 (61.3 MB) 31-Chapter 4 Representing item features.mp4 (60.2 MB) 75-Chapter 12 Knowledge graphs.mp4 (60.1 MB) 71-Chapter 11 Graph-based natural language processing.mp4 (57.7 MB) 33-Chapter 4 Providing recommendations.mp4 (56.8 MB) 25-Chapter 3 Monitoring a subject.mp4 (55.5 MB) 56-Chapter 8 Warm-up - Basic approaches.mp4 (55.5 MB) 40-Chapter 5 Providing recommendations.mp4 (53.8 MB) 23-Chapter 3 Find keywords in a document.mp4 (53.6 MB) 72-Chapter 11 A basic approach - Store and access sequence of words.mp4 (53.5 MB) 05-Chapter 1 Performance.mp4 (53.1 MB) 79-Chapter 12 Unsupervised keyword extraction.mp4 (52.9 MB) 27-Chapter 3 Leftover - Deep learning and graph neural networks.mp4 (52.8 MB) 20-Chapter 3 Detect a fraud.mp4 (52.3 MB) 14-Chapter 2 Graph databases.mp4 (52.1 MB) 60-Chapter 9 Creating the k-nearest neighbors graph.mp4 (52.1 MB) 50-Chapter 7 Advantages of the graph approach.mp4 (51.8 MB) 10-Chapter 2 Velocity.mp4 (50.8 MB) 68-Chapter 10 Collective inference algorithms.mp4 (50.6 MB) 81-Chapter 12 Keyword co-occurrence graph.mp4 (50.6 MB) 84-Appendix C Graphs for defining complex processing workflows.mp4 (50.4 MB) 59-Chapter 9 Distance-based approach.mp4 (49.9 MB) 04-Chapter 1 Machine learning challenges.mp4 (49.8 MB) 11-Chapter 2 Graphs in the big data platform.mp4 (49.4 MB) 53-Chapter 8 Basic approaches to graph-powered fraud detection.mp4 (48.5 MB) 22-Chapter 3 Algorithms.mp4 (48.2 MB) 39-Chapter 5 Computing the nearest neighbor network.mp4 (47.9 MB) 55-Chapter 8 The role of graphs in fighting fraud.mp4 (47.1 MB) 57-Chapter 8 Identifying a fraud ring.mp4 (46.9 MB) 64-Chapter 10 Social network analysis concepts.mp4 (46.4 MB) 66-Chapter 10 Neighborhood metrics.mp4 (45.9 MB) 54-Chapter 8 Fraud prevention and detection.mp4 (45.2 MB) 83-Appendix C Graphs for processing patterns and workflows.mp4 (43.8 MB) 12-Chapter 2 Graphs are valuable for big data.mp4 (43.2 MB) 47-Chapter 7 Representing contextual information.mp4 (42.9 MB) 85-Appendix D. Representing graphs.mp4 (40.5 MB) 41-Chapter 5 Dealing with the cold-start problem.mp4 (40.2 MB) 03-Chapter 1 Business understanding.mp4 (39.1 MB) 51-Chapter 7 Providing recommendations.mp4 (38.6 MB) 78-Chapter 12 Semantic networks.mp4 (38.4 MB) 26-Chapter 3 Visualization.mp4 (37.9 MB) 17-Chapter 2 Label property graphs.mp4 (37.7 MB) 80-Chapter 12 Unsupervised keyword extraction.mp4 (35.9 MB) 52-Part 3 Fighting fraud.mp4 (34.4 MB) 32-Chapter 4 User modeling.mp4 (33.6 MB) 06-Chapter 1 Graphs.mp4 (33.3 MB) 62-Chapter 9 Identifying fraudulent transactions.mp4 (32.5 MB) 65-Chapter 10 Score-based methods.mp4 (32.2 MB) 24-Chapter 3 Storing and accessing machine learning models.mp4 (31.4 MB) 70-Part 4 Taming text with graphs.mp4 (24.5 MB) 01-Part 1 Introduction.mp4 (21.3 MB) TutsNode.com.txt (0.1 KB) [TGx]Downloaded from torrentgalaxy.to .txt (0.6 KB) .pad 0 (94.7 KB) 1 (35.1 KB) 2 (938.6 KB) 3 (259.2 KB) 4 (66.1 KB) 5 (896.8 KB) 6 (427.3 KB) 7 (1,009.6 KB) 8 (714.2 KB) 9 (529.1 KB) 10 (77.3 KB) 11 (367.0 KB) 12 (657.5 KB) 13 (338.2 KB) 14 (177.7 KB) 15 (408.9 KB) 16 (723.1 KB) 17 (490.3 KB) 18 (1,002.0 KB) 19 (309.6 KB) 20 (978.0 KB) 21 (13.0 KB) files
Added:	2022-04-18 10:06:58
Language:	English
Peers:	Seeders : 26 , Leechers : 6
Release name:	Graph-Powered Machine Learning, Video Edition
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