Applied Machine Learning Explainability Techniques

Download Applied Machine Learning Explainability Techniques full books in PDF, epub, and Kindle. Read online free Applied Machine Learning Explainability Techniques ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!


Related Books

Applied Machine Learning Explainability Techniques
Language: en
Pages: 306
Authors: Aditya Bhattacharya
Categories: Computers
Type: BOOK - Published: 2022-07-29 - Publisher: Packt Publishing Ltd

DOWNLOAD EBOOK

Leverage top XAI frameworks to explain your machine learning models with ease and discover best practices and guidelines to build scalable explainable ML system
Interpretable Machine Learning
Language: en
Pages: 320
Authors: Christoph Molnar
Categories: Computers
Type: BOOK - Published: 2020 - Publisher: Lulu.com

DOWNLOAD EBOOK

This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simp
Explainable AI with Python
Language: en
Pages: 202
Authors: Leonida Gianfagna
Categories: Computers
Type: BOOK - Published: 2021-04-28 - Publisher: Springer Nature

DOWNLOAD EBOOK

This book provides a full presentation of the current concepts and available techniques to make “machine learning” systems more explainable. The approaches
Applied Machine Learning for Healthcare and Life Sciences Using AWS
Language: en
Pages: 224
Authors: Ujjwal Ratan
Categories: Computers
Type: BOOK - Published: 2022-11-25 - Publisher: Packt Publishing Ltd

DOWNLOAD EBOOK

Build real-world artificial intelligence apps on AWS to overcome challenges faced by healthcare providers and payers, as well as pharmaceutical, life sciences r
Applied Machine Learning and Deep Learning: Architectures and Techniques
Language: en
Pages: 215
Authors: Nitin Liladhar Rane
Categories: Computers
Type: BOOK - Published: 2024-10-13 - Publisher: Deep Science Publishing

DOWNLOAD EBOOK

This book provides an extensive overview of recent advances in machine learning (ML) and deep learning (DL). It starts with a comprehensive introduction to the