| Description |
1 online resource (328 pages) : illustrations |
| Note |
Includes index. |
| 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. |
| Subject |
Machine learning.
|
|
Artificial intelligence.
|
|
Python (Computer program language)
|
|
Apprentissage automatique. |
|
Intelligence artificielle. |
|
Python (Langage de programmation) |
|
artificial intelligence. |
|
Artificial intelligence |
|
Machine learning |
|
Python (Computer program language) |
| Added Title |
Interpretable artificial intelligence |
| ISBN |
9781617297649 (electronic bk.) |
|
161729764X (electronic bk.) |
|
9781638350422 (electronic bk.) |
|
1638350426 (electronic bk.) |
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