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

個数:

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

  • 在庫がございません。海外の書籍取次会社を通じて出版社等からお取り寄せいたします。
    通常6~9週間ほどで発送の見込みですが、商品によってはさらに時間がかかることもございます。
    重要ご説明事項
    1. 納期遅延や、ご入手不能となる場合がございます。
    2. 複数冊ご注文の場合、分割発送となる場合がございます。
    3. 美品のご指定は承りかねます。

    ●3Dセキュア導入とクレジットカードによるお支払いについて
  • 【入荷遅延について】
    世界情勢の影響により、海外からお取り寄せとなる洋書・洋古書の入荷が、表示している標準的な納期よりも遅延する場合がございます。
    おそれいりますが、あらかじめご了承くださいますようお願い申し上げます。
  • ◆画像の表紙や帯等は実物とは異なる場合があります。
  • ◆ウェブストアでの洋書販売価格は、弊社店舗等での販売価格とは異なります。
    また、洋書販売価格は、ご注文確定時点での日本円価格となります。
    ご注文確定後に、同じ洋書の販売価格が変動しても、それは反映されません。
  • 製本 Paperback:紙装版/ペーパーバック版/ページ数 304 p.
  • 言語 ENG
  • 商品コード 9781803246154
  • DDC分類 006.31

Full Description

Leverage top XAI frameworks to explain your machine learning models with ease and discover best practices and guidelines to build scalable explainable ML systems

Key Features

Explore various explainability methods for designing robust and scalable explainable ML systems
Use XAI frameworks such as LIME and SHAP to make ML models explainable to solve practical problems
Design user-centric explainable ML systems using guidelines provided for industrial applications

Book DescriptionExplainable AI (XAI) is an emerging field that brings artificial intelligence (AI) closer to non-technical end users. XAI makes machine learning (ML) models transparent and trustworthy along with promoting AI adoption for industrial and research use cases.

Applied Machine Learning Explainability Techniques comes with a unique blend of industrial and academic research perspectives to help you acquire practical XAI skills. You'll begin by gaining a conceptual understanding of XAI and why it's so important in AI. Next, you'll get the practical experience needed to utilize XAI in AI/ML problem-solving processes using state-of-the-art methods and frameworks. Finally, you'll get the essential guidelines needed to take your XAI journey to the next level and bridge the existing gaps between AI and end users.

By the end of this ML book, you'll be equipped with best practices in the AI/ML life cycle and will be able to implement XAI methods and approaches using Python to solve industrial problems, successfully addressing key pain points encountered.

What you will learn

Explore various explanation methods and their evaluation criteria
Learn model explanation methods for structured and unstructured data
Apply data-centric XAI for practical problem-solving
Hands-on exposure to LIME, SHAP, TCAV, DALEX, ALIBI, DiCE, and others
Discover industrial best practices for explainable ML systems
Use user-centric XAI to bring AI closer to non-technical end users
Address open challenges in XAI using the recommended guidelines

Who this book is forThis book is for scientists, researchers, engineers, architects, and managers who are actively engaged in machine learning and related fields. Anyone who is interested in problem-solving using AI will benefit from this book. Foundational knowledge of Python, ML, DL, and data science is recommended. AI/ML experts working with data science, ML, DL, and AI will be able to put their knowledge to work with this practical guide. This book is ideal for you if you're a data and AI scientist, AI/ML engineer, AI/ML product manager, AI product owner, AI/ML researcher, and UX and HCI researcher.

Contents

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

最近チェックした商品