rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Applied Machine Learning Explainability Techniques
    sub title: Make ML Models Explainable and Trustworthy for Practical Applications Using LIME, SHAP, and More
  name:
    name part: Bhattacharya, Aditya.
    role:
      role term: creator
  type of resource: text
  origin info:
    place:
      place term: enk
    date issued: 2022
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource (304 pages)
  abstract: 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 Description Explainable 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 …
  note: Aditya Bhattacharya.
  subject:
    topic: Machine learning
  subject:
    topic: Machine learning
  classification: Q325.5 .B43 2022
  classification: 006.3/1
  related item:
    title info:
      title: Applied Machine Learning Explainability Techniques
    name:
      name part: Bhattacharya, Aditya.
    origin info:
      publisher: Birmingham : Packt Publishing, Limited, ©2022
  identifier: 9781803234168
  identifier: 1803234164
  identifier: 9781803246154 O'Reilly Media
  identifier: https://proxy.library.tamu.edu/login
  record info:
    record content source: EBLCP
    record creation date: 220702
    record change date: 20260130162502.5
    record identifier: in00004671728
    language of cataloging:
      language term: eng