Applied Machine Learning Explainability Techniques: Make ML...

Main
Computers - Web Development
Applied Machine Learning Explainability...

Applied Machine Learning Explainability Techniques: Make ML models explainable and trustworthy for practical applications using LIME, SHAP, and more

Aditya Bhattacharya

0 / 5.0

Sukakah anda buku ini?

Bagaimana kualiti fail ini?

Muat turun buku untuk menilai kualitinya

Bagaimana kualiti fail yang dimuat turun?

Kategori:

Computers - Web Development

Tahun:

2022

Penerbit:

Packt Publishing

Bahasa:

english

Halaman:

306

ISBN 10:

1803246154

ISBN 13:

9781803246154

Fail:

EPUB, 15.68 MB

IPFS:

,

english, 2022

Muat turun buku ini tidak tersedia lagi kerana aduan pemegang hak cipta

Beware of he who would deny you access to information, for in his heart he dreams himself your master

— Pravin Lal

Mungkin anda berminat

unknown — IEEE Signal Processing Magazine

Michael Munn;David Pitman; — Explainable AI for Practitioners

B. K. Tripathy & Hari Seetha — Explainable, Interpretable and Transparent AI Systems

Christoph Molnar — Interpretable Machine Learning: A Guide For Making Black Box Models Explainable

Aditya Bhattacharya — Applied Machine Learning Explainability Techniques: Make ML models explainable and trustworthy for practical applications using LIME, SHAP, and more

B. K. Tripathy — Explainable, Interpretable and Transparent AI Systems

Christoph Molnar — Interpretable Machine Learning

Amina Adadi, Afaf Bouhoute, (eds.) — Explainable Artificial Intelligence for Intelligent Transportation Systems

Patrick Hall & James Curtis & Parul Pandey — Machine Learning for High-Risk Applications (9th Early Release)

Patrick Hall — Machine Learning for High-Risk Applications: Approaches to Responsible AI

Michael Munn, David Pitman — Explainable AI for Practitioners: Designing and Implementing Explainable ML Solutions

Pradeepta Mishra — Practical Explainable AI Using Python: Artificial Intelligence Model Explanations Using Python-based Libraries, Extensions, and Frameworks

Pradeepta Mishra — Explainable AI Recipes: Implement Solutions to Model Explainability and Interpretability with Python

anonymous — Interpretable Machine Learning

Agbotiname Lucky Imoize, Jude Hemanth, Dinh-Thuan Do, Samarendra Nath Sur, (eds.) — Explainable Artificial Intelligence in Medical Decision Support Systems

Pethuru Raj, Utku Köse, Usha Sakthivel, Susila Nagarajan, Vijanth Sagayan Asirvadam — Explainable Artificial Intelligence (XAI): Concepts, enabling tools, technologies and applications

Davide Calvaresi, Amro Najjar, Michael Winikoff, Kary Främling — Explainable, Transparent Autonomous Agents and Multi-Agent Systems: Second International Workshop, EXTRAAMAS 2020, Auckland, New Zealand, May 9–13, 2020, Revised Selected Papers

Andreas Holzinger, Peter Kieseberg, A Min Tjoa, Edgar Weippl — Machine Learning and Knowledge Extraction: 4th IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference, CD-MAKE 2020, Dublin, Ireland, August 25–28, 2020, Proceedings

Leonida Gianfagna; Antonio Di Cecco — Explainable AI Using Python

Aboul Ella Hassanien, Deepak Gupta, Anuj Kumar Singh, Ankit Garg — Explainable Edge AI: A Futuristic Computing Perspective

Rajanikanth Aluvalu, Mayuri Mehta, Patrick Siarry — Explainable AI in Health Informatics

Christoph Molnar — Interpretable Machine Learning: A Guide for Making Black Box Models Explainable

Istilah utama