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Interpretable AI : building explainable machine learning systems /
AI doesn’t have to be a black box. These practical techniques help shine a light on your model’s mysterious inner workings. Make your AI more transparent, and you’ll improve trust in your results, combat data leakage and bias, and ensure compliance with legal require...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Shelter Island, NY :
Manning Publications Co.,
[2022]
|
| Edición: | [First edition]. |
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
| LEADER | 00000cam a22000007i 4500 | ||
|---|---|---|---|
| 001 | OR_on1335127400 | ||
| 003 | OCoLC | ||
| 005 | 20231017213018.0 | ||
| 006 | m o d | ||
| 007 | cr cnu|||unuuu | ||
| 008 | 220712s2022 nyua o 001 0 eng d | ||
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| 019 | |a 1365390855 | ||
| 020 | |a 9781617297649 |q (electronic bk.) | ||
| 020 | |a 161729764X |q (electronic bk.) | ||
| 020 | |a 9781638350422 |q (electronic bk.) | ||
| 020 | |a 1638350426 |q (electronic bk.) | ||
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| 082 | 0 | 4 | |a 006.3/1 |2 23/eng/20220712 |
| 049 | |a UAMI | ||
| 100 | 1 | |a Thampi, Ajay, |e author. | |
| 245 | 1 | 0 | |a Interpretable AI : |b building explainable machine learning systems / |c Ajay Thampi. |
| 246 | 3 | |a Interpretable artificial intelligence | |
| 250 | |a [First edition]. | ||
| 264 | 1 | |a Shelter Island, NY : |b Manning Publications Co., |c [2022] | |
| 300 | |a 1 online resource (328 pages) : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 500 | |a Includes index. | ||
| 520 | |a AI doesn’t have to be a black box. These practical techniques help shine a light on your model’s mysterious inner workings. Make your AI more transparent, and you’ll improve trust in your results, combat data leakage and bias, and ensure compliance with legal requirements. Interpretable AI opens up the black box of your AI models. It teaches cutting-edge techniques and best practices that can make even complex AI systems interpretable. Each method is easy to implement with just Python and open source libraries. You’ll learn to identify when you can utilize models that are inherently transparent, and how to mitigate opacity when your problem demands the power of a hard-to-interpret deep learning model. | ||
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a Artificial intelligence. | |
| 650 | 0 | |a Python (Computer program language) | |
| 650 | 7 | |a Artificial intelligence. |2 fast |0 (OCoLC)fst00817247 | |
| 650 | 7 | |a Machine learning. |2 fast |0 (OCoLC)fst01004795 | |
| 650 | 7 | |a Python (Computer program language) |2 fast |0 (OCoLC)fst01084736 | |
| 856 | 4 | 0 | |u https://learning.oreilly.com/library/view/~/9781617297649/?ar |z Texto completo (Requiere registro previo con correo institucional) |
| 938 | |a EBSCOhost |b EBSC |n 3292824 | ||
| 994 | |a 92 |b IZTAP | ||