Bhattacharya Applied Machine Learning Explainability Techniques

Make ML models explainable and trustworthy for practical applications using LIME, SHAP, and more
1. Auflage 2022
ISBN: 978-1-80323-416-8
Verlag: De Gruyter
Format: EPUB
Kopierschutz: 0 - No protection

E-Book, Englisch, 306 Seiten

No detailed description available for "Applied Machine Learning Explainability Techniques".

Autoren/Hrsg.

Bhattacharya, Aditya

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Mensch-Maschine-Interaktion Informationsvisualisierung
• Mathematik | Informatik EDV | Informatik Informatik Theoretische Informatik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz

Inhaltsverzeichnis

Table of Contents - Foundational Concepts of Explainability Techniques
- Model Explainability Methods
- Data-Centric Approaches
- LIME for Model Interpretability
- Practical Exposure to Using LIME in ML
- Model Interpretability Using SHAP
- Practical Exposure to Using SHAP in ML
- Human-Friendly Explanations with TCAV
- Other Popular XAI Frameworks
- XAI Industry Best Practices
- End User-Centered Artificial Intelligence

Über Autor(innen)

Bhattacharya Aditya:

Aditya Bhattacharya is an explainable AI researcher at KU Leuven with 7 years of experience in data science, machine learning, IoT, and software engineering. Prior to his current role, Aditya worked in various roles in organizations such as West Pharma, Microsoft, and Intel to democratize AI adoption for industrial solutions. As the AI lead at West Pharma, he contributed to forming the AI Center of Excellence, managing and leading a global team of 10+ members focused on building AI products. He also holds a master's degree from Georgia Tech in computer science with machine learning and a bachelor's degree from VIT University in ECE. Aditya is passionate about bringing AI closer to end users through his various initiatives for the AI community.