Rothman Hands-On Explainable AI (XAI) with Python

Interpret, visualize, explain, and integrate reliable AI for fair, secure, and trustworthy AI apps
1. Auflage 2020
ISBN: 978-1-80020-276-4
Verlag: De Gruyter
Format: EPUB
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 454 Seiten

No detailed description available for "Hands-On Explainable AI (XAI) with Python".

Autoren/Hrsg.

Rothman, Denis

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Neuronale Netzwerke
• Mathematik | Informatik EDV | Informatik Informatik Theoretische Informatik

Inhaltsverzeichnis

Table of Contents - Explaining Artificial Intelligence with Python
- White Box XAI for AI Bias and Ethics
- Explaining Machine Learning with Facets
- Microsoft Azure Machine Learning Model Interpretability with SHAP
- Building an Explainable AI Solution from Scratch
- AI Fairness with Google's What-If Tool (WIT)
- A Python Client for Explainable AI Chatbots
- Local Interpretable Model-Agnostic Explanations (LIME)
- The Counterfactual Explanations Method
- Contrastive XAI
- Anchors XAI
- Cognitive XAI

Über Autor(innen)

Rothman Denis:

Denis Rothman graduated from Sorbonne University and Paris-Diderot University, designing one of the very first word2matrix patented embedding and patented AI conversational agents. He began his career authoring one of the first AI cognitive Natural Language Processing (NLP) chatbots applied as an automated language teacher for Moet et Chandon and other companies. He authored an AI resource optimizer for IBM and apparel producers. He then authored an Advanced Planning and Scheduling (APS) solution used worldwide.