AI no shinrigaku : arugorizumikku baiasu to no tatakaikata o t�oshite manabu, bijinesu p�ason to enjinia no tame no kikai gakush�u ny�umon
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Baer, T., Musha, H., & Musha, R. (2021). AI no shinrigaku: arugorizumikku baiasu to no tatakaikata o t�oshite manabu, bijinesu p�ason to enjinia no tame no kikai gakush�u ny�umon (Shohan.). Orair�i Japan.
Chicago / Turabian - Author Date Citation, 17th Edition (style guide)Baer, Tobias, Hiroyuki, Musha and Rumi, Musha. 2021. AI No Shinrigaku: Arugorizumikku Baiasu to No Tatakaikata O T�oshite Manabu, Bijinesu P�ason to Enjinia No Tame No Kikai Gakush�u Ny�umon. T�oky�o-to Shinjuku-ku: Orair�i Japan.
Chicago / Turabian - Humanities (Notes and Bibliography) Citation, 17th Edition (style guide)Baer, Tobias, Hiroyuki, Musha and Rumi, Musha. AI No Shinrigaku: Arugorizumikku Baiasu to No Tatakaikata O T�oshite Manabu, Bijinesu P�ason to Enjinia No Tame No Kikai Gakush�u Ny�umon T�oky�o-to Shinjuku-ku: Orair�i Japan, 2021.
Harvard Citation (style guide)Baer, T., Musha, H. and Musha, R. (2021). AI no shinrigaku: arugorizumikku baiasu to no tatakaikata o t�oshite manabu, bijinesu p�ason to enjinia no tame no kikai gakush�u ny�umon. Shohan. T�oky�o-to Shinjuku-ku: Orair�i Japan.
MLA Citation, 9th Edition (style guide)Baer, Tobias,, Hiroyuki Musha, and Rumi Musha. AI No Shinrigaku: Arugorizumikku Baiasu to No Tatakaikata O T�oshite Manabu, Bijinesu P�ason to Enjinia No Tame No Kikai Gakush�u Ny�umon Shohan., Orair�i Japan, 2021.
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Grouped Work ID | 4c32da71-a33c-e2cf-000f-af6e2def59d7-jpn |
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Full title | ai no shinrigaku arugorizumikku baiasu to no tatakaikata o t oshite manabu bijinesu p ason to enjinia no tame no kikai gakush u ny umon |
Author | baer tobias |
Grouping Category | book |
Last Update | 2025-01-24 12:33:29PM |
Last Indexed | 2025-01-30 03:10:58AM |
Book Cover Information
Image Source | default |
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First Loaded | Aug 15, 2023 |
Last Used | Jan 20, 2025 |
Marc Record
First Detected | Mar 07, 2023 02:24:36 PM |
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Last File Modification Time | Oct 04, 2023 03:50:54 PM |
Suppressed | Record had no items |
MARC Record
LEADER | 05462nam a22005417i 4500 | ||
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001 | on1356973594 | ||
003 | OCoLC | ||
005 | 20231004155004.0 | ||
006 | m o d | ||
007 | cr cnu|||unuuu | ||
008 | 230106s2021 ja o 000 0 jpn d | ||
020 | |a 9784873119625|q (electronic bk.) | ||
020 | |a 4873119626|q (electronic bk.) | ||
035 | |a (OCoLC)1356973594 | ||
037 | |a 9784873119625|b O'Reilly Media | ||
040 | |a ORMDA|b eng|e rda|e pn|c ORMDA|d UPM|d STF|d UAB | ||
041 | 1 | |a jpn|h eng | |
049 | |a MAIN | ||
050 | 4 | |a QA76.9.A43 | |
066 | |c Hani|c $1 | ||
082 | 0 | 4 | |a 006.31|2 23/eng/20230106 |
100 | 1 | |a Baer, Tobias,|e author. | |
240 | 1 | 0 | |a Understand, manage, and prevent algorithmic bias.|l Japanese |
245 | 1 | 0 | |6 880-01|a AI no shinrigaku :|b arugorizumikku baiasu to no tatakaikata o t�oshite manabu, bijinesu p�ason to enjinia no tame no kikai gakush�u ny�umon /|c Tobias Baer cho ; Musha Hiroyuki, Musha Rumi yaku = Understand, manage, and prevent algorithmic bias : a guide for business users and data scientists / Tobias Baer. |
246 | 3 | 1 | |a Understand, manage, and prevent algorithmic bias :|b a guide for business users and data scientists |
250 | |6 880-02|a Shohan. | ||
264 | 1 | |6 880-03|a T�oky�o-to Shinjuku-ku :|b Orair�i Japan,|c 2021. | |
300 | |a 1 online resource (344 pages) | ||
336 | |a text|b txt|2 rdacontent | ||
337 | |a computer|b c|2 rdamedia | ||
338 | |a online resource|b cr|2 rdacarrier | ||
520 | |a The human mind is evolutionarily designed to take shortcuts in order to survive. We jump to conclusions because our brains want to keep us safe. A majority of our biases work in our favor, such as when we feel a car speeding in our direction is dangerous and we instantly move, or when we decide not take a bite of food that appears to have gone bad. However, inherent bias negatively affects work environments and the decision-making surrounding our communities. While the creation of algorithms and machine learning attempts to eliminate bias, they are, after all, created by human beings, and thus are susceptible to what we call algorithmic bias. In Understand, Manage, and Prevent Algorithmic Bias, author Tobias Baer helps you understand where algorithmic bias comes from, how to manage it as a business user or regulator, and how data science can prevent bias from entering statistical algorithms. Baer expertly addresses some of the 100+ varieties of natural bias such as confirmation bias, stability bias, pattern-recognition bias, and many others. Algorithmic bias mirrors--and originates in--these human tendencies. While most writings on algorithmic bias focus on the dangers, the core of this positive, fun book points toward a path where bias is kept at bay and even eliminated. Youll come away with managerial techniques to develop unbiased algorithms, the ability to detect bias more quickly, and knowledge to create unbiased data. Understand, Manage, and Prevent Algorithmic Bias is an innovative, timely, and important book that belongs on your shelf. Whether you are a seasoned business executive, a data scientist, or simply an enthusiast, now is a crucial time to be educated about the larger sociological impact of bias in the digital era. | ||
546 | |a In Japanese. | ||
590 | |a O'Reilly|b O'Reilly Online Learning: Academic/Public Library Edition | ||
650 | 0 | |a Computer algorithms|x Psychological aspects. | |
650 | 0 | |a Machine learning.|9 46043 | |
700 | 1 | |6 880-04|a Musha, Hiroyuki,|e translator. | |
700 | 1 | |6 880-05|a Musha, Rumi,|e translator. | |
765 | 0 | 8 | |i Translation of:|a Baer, Tobias.|t Understand, manage, and prevent algorithmic bias.|d [New York, NY] : Apress, [2019]|z 1484248848|w (OCoLC)1091846682 |
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