Applied machine learning for spreading financial statements /

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Bibliographic Details
Imprint:[Austin, Texas] : Data Science Salon, 2020.
Description:1 online resource (1 streaming video file (22 min., 18 sec.))
Language:English
Subject:
Format: E-Resource Video Streaming Video
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/13685065
Hidden Bibliographic Details
Other authors / contributors:Hadi, Moody, on-screen presenter.
Data Science Salon, publisher.
Notes:Title from resource description page (Safari, viewed November 3, 2020).
Place of publication from title screen.
Presenter, Moody Hadi.
Summary:"Presented by Moody Hadi, Group Manager, Financial Engineering at S & P Global Market Intelligence. Counterparty financial statements, particularly for small and medium enterprises can be difficult to handle. Financial analysts need to be able to distill out relevant line items in order to calculate their credit exposure to a counterparty for lending purposes. The solution solves a labor intensive, expert driven inefficient process and frees up the analysts to focus on their high value add operations. This involves combining Optical Character Recognition using pre-trained language neural networks, with context sensitive semantic matching. We will go over the developed ML pipleline and architecture."--Resource description page

MARC

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500 |a Title from resource description page (Safari, viewed November 3, 2020). 
500 |a Place of publication from title screen. 
520 |a "Presented by Moody Hadi, Group Manager, Financial Engineering at S & P Global Market Intelligence. Counterparty financial statements, particularly for small and medium enterprises can be difficult to handle. Financial analysts need to be able to distill out relevant line items in order to calculate their credit exposure to a counterparty for lending purposes. The solution solves a labor intensive, expert driven inefficient process and frees up the analysts to focus on their high value add operations. This involves combining Optical Character Recognition using pre-trained language neural networks, with context sensitive semantic matching. We will go over the developed ML pipleline and architecture."--Resource description page 
650 0 |a Machine learning.  |0 http://id.loc.gov/authorities/subjects/sh85079324 
650 0 |a Artificial intelligence  |x Data processing.  |0 http://id.loc.gov/authorities/subjects/sh85008182 
650 0 |a Neural networks (Computer science)  |0 http://id.loc.gov/authorities/subjects/sh90001937 
650 0 |a Financial statements.  |0 http://id.loc.gov/authorities/subjects/sh85048313 
650 0 |a Bank loans  |x Mathematical models. 
650 2 |a Neural Networks, Computer  |0 https://id.nlm.nih.gov/mesh/D016571 
650 6 |a Apprentissage automatique. 
650 6 |a Intelligence artificielle  |x Informatique. 
650 6 |a Réseaux neuronaux (Informatique) 
650 6 |a Prêts bancaires  |x Modèles mathématiques. 
650 7 |a Artificial intelligence  |x Data processing.  |2 fast  |0 (OCoLC)fst00817255 
650 7 |a Bank loans  |x Mathematical models.  |2 fast  |0 (OCoLC)fst00826717 
650 7 |a Financial statements.  |2 fast  |0 (OCoLC)fst00924782 
650 7 |a Machine learning.  |2 fast  |0 (OCoLC)fst01004795 
650 7 |a Neural networks (Computer science)  |2 fast  |0 (OCoLC)fst01036260 
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