Interpretable AI : building explainable machine learning systems / Ajay Thampi.
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. Interpretab...
Saved in:
| Online Access: |
Full Text (via Skillsoft) |
|---|---|
| Main Author: | |
| Other title: | Interpretable artificial intelligence |
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Shelter Island, NY :
Manning Publications Co.,
[2022]
|
| Edition: | [First edition]. |
| Subjects: |
| Summary: | 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. |
|---|---|
| Item Description: | Includes index. |
| Physical Description: | 1 online resource (328 pages) : illustrations |
| ISBN: | 9781617297649 161729764X 9781638350422 1638350426 |