Cargando…

Behaviors in Bookkeeping

Bookkeeping is an established process businesses need to follow in order to keep track of their financials and tax returns. When translating a machine learning model into what a customer considers to be 'kept books' and what a bookkeeper considers to be 'kept books' variability a...

Descripción completa

Detalles Bibliográficos
Autor principal: Salon, Data (Autor)
Autor Corporativo: Safari, an O'Reilly Media Company
Formato: Electrónico Video
Idioma:Inglés
Publicado: Data Science Salon, 2020.
Edición:1st edition.
Temas:
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)

MARC

LEADER 00000cgm a22000007a 4500
001 OR_on1192531786
003 OCoLC
005 20231017213018.0
006 m o c
007 cr cnu||||||||
007 vz czazuu
008 050820s2020 xx 020 vleng
040 |a AU@  |b eng  |c AU@  |d TOH  |d NZCPL  |d OCLCF  |d OCLCO  |d FZL  |d OCLCQ  |d DXU  |d OCLCQ 
019 |a 1224592960  |a 1232117016  |a 1305895002  |a 1351591210  |a 1380764785  |a 1385506272 
020 |z 00051YNWSTM8VK 
024 8 |a 00051YNWSTM8VK 
029 0 |a AU@  |b 000067830735 
035 |a (OCoLC)1192531786  |z (OCoLC)1224592960  |z (OCoLC)1232117016  |z (OCoLC)1305895002  |z (OCoLC)1351591210  |z (OCoLC)1380764785  |z (OCoLC)1385506272 
082 0 4 |a E VIDEO 
049 |a UAMI 
100 1 |a Salon, Data,  |e author. 
245 1 0 |a Behaviors in Bookkeeping  |h [electronic resource] /  |c Salon, Data. 
250 |a 1st edition. 
264 1 |b Data Science Salon,  |c 2020. 
300 |a 1 online resource (1 video file, approximately 20 min.) 
336 |a two-dimensional moving image  |b tdi  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
344 |a digital  |2 rdatr 
347 |a video file 
520 |a Bookkeeping is an established process businesses need to follow in order to keep track of their financials and tax returns. When translating a machine learning model into what a customer considers to be 'kept books' and what a bookkeeper considers to be 'kept books' variability and a dependency on the customer-bookkeeper relationship come into play. Some of this can be handled by education and process changes, but other elements can be instilled by creating a logic that is applied before, during and after a machine learning model to control for the various types of error. The presentation will go over the ways we can apply logical algorithms outside of the central model to improve the central model, and create a less error-prone training set and output. 
538 |a Mode of access: World Wide Web. 
542 |f Copyright © Data Science Salon  |g 2019 
550 |a Made available through: Safari, an O'Reilly Media Company. 
588 |a Online resource; Title from title screen (viewed March 24, 2020) 
533 |a Electronic reproduction.  |b Boston, MA :  |c Safari.  |n Available via World Wide Web.,  |d 2019. 
590 |a O'Reilly  |b O'Reilly Online Learning: Academic/Public Library Edition 
655 4 |a Electronic videos. 
710 2 |a Safari, an O'Reilly Media Company. 
856 4 0 |u https://learning.oreilly.com/videos/~/00051YNWSTM8VK/?ar  |z Texto completo (Requiere registro previo con correo institucional) 
936 |a BATCHLOAD 
994 |a 92  |b IZTAP