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210123 ||| eng |
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4 |
|a ZA3084
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| 100 |
1 |
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|a Barbour, Jesse
|e on-screen presenter
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| 245 |
0 |
0 |
|a Learning node embeddings in transaction networks
|c Data Science Salon
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| 246 |
3 |
1 |
|a Building a recommender system from node embeddings on a transaction graph
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| 260 |
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|a [Austin, Texas]
|b Data Science Salon
|c 2020
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| 300 |
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|a 1 streaming video file (21 min., 22 sec.)
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| 653 |
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|a Réseaux neuronaux (Informatique)
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| 653 |
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|a Logiciels d'application / Développement
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| 653 |
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|a Application software / Development / fast / (OCoLC)fst00811707
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| 653 |
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|a Transaction systems (Computer systems) / fast / (OCoLC)fst01154510
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| 653 |
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|a Computer networks / Security measures / http://id.loc.gov/authorities/subjects/sh94001277
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| 653 |
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|a Recommender systems (Information filtering) / fast / (OCoLC)fst01743365
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| 653 |
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|a Systèmes de recommandation (Filtrage d'information)
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| 653 |
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|a Recommender systems (Information filtering) / http://id.loc.gov/authorities/subjects/sh2007003098
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| 653 |
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|a Neural Networks, Computer
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| 653 |
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|a Computer networks / Security measures / fast / (OCoLC)fst00872341
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| 653 |
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|a Neural networks (Computer science) / http://id.loc.gov/authorities/subjects/sh90001937
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| 653 |
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|a Transaction systems (Computer systems) / http://id.loc.gov/authorities/subjects/sh89002252
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| 653 |
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|a Réseaux d'ordinateurs / Sécurité / Mesures
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| 653 |
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|a Neural networks (Computer science) / fast / (OCoLC)fst01036260
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| 653 |
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|a Systèmes transactionnels
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| 653 |
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|a Application software / Development / http://id.loc.gov/authorities/subjects/sh95009362
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b OREILLY
|a O'Reilly
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| 500 |
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|a Title from resource description page (Safari, viewed November 3, 2020). - Place of publication from title screen
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| 856 |
4 |
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|u https://learning.oreilly.com/videos/~/00000HQHJ2T3KE2A/?ar
|x Verlag
|3 Volltext
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|a 331
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|a 500
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|a 000
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| 520 |
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|a "Presented by Jesse Barbour, Chief Data Scientist at Q2ebanking. Due to the specialized and sophisticated nature of many commercially focused financial products offered by banks and fintechs, building recommender systems around those products is especially difficult. Taking inspiration from the field of neural language modeling, we will discuss an application of learning node embeddings on a large-scale financial transaction graph in order to solve this problem."--Resource description page
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