EbookBell.com

Most ebook files are in PDF format, so you can easily read them using various software such as Foxit Reader or directly on the Google Chrome browser.
Some ebook files are released by publishers in other formats such as .awz, .mobi, .epub, .fb2, etc. You may need to install specific software to read these formats on mobile/PC, such as Calibre.

Please read the tutorial at this link: https://ebookbell.com/faq

We offer FREE conversion to the popular formats you request; however, this may take some time. Therefore, right after payment, please email us, and we will try to provide the service as quickly as possible.

For some exceptional file formats or broken links (if any), please refrain from opening any disputes. Instead, email us first, and we will try to assist within a maximum of 6 hours.

EbookBell Team

Interpretable Ai 1st Edition Ajay Thampi

Name: Interpretable Ai 1st Edition Ajay Thampi
SKU: BELL-49118264
Price: 31.00 USD
Availability: InStock
Rating: 4 (36 reviews)

SKU: BELL-49118264

$ 31.00 ~~$ 45.00~~ (-31%)

4.0

36 reviews

Interpretable Ai 1st Edition Ajay Thampi instant download after payment.

Publisher: Simon and Schuster

File Extension: PDF

File size: 5.34 MB

Pages: 326

Author: Ajay Thampi

ISBN: 9781617297649, 161729764X

Language: English

Year: 2022

Edition: 1

Product desciption

Interpretable Ai 1st Edition Ajay Thampi by Ajay Thampi 9781617297649, 161729764X instant download after payment.

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.