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

    (ebook) (audiobook) (audiobook) Książka w języku 1
    Applied Machine Learning Explainability Techniques. Make ML models explainable and trustworthy for practical applications using LIME, SHAP, and more Aditya Bhattacharya - okladka książki

    Applied Machine Learning Explainability Techniques. Make ML models explainable and trustworthy for practical applications using LIME, SHAP, and more Aditya Bhattacharya - okladka książki

    Applied Machine Learning Explainability Techniques. Make ML models explainable and trustworthy for practical applications using LIME, SHAP, and more Aditya Bhattacharya - audiobook MP3

    Applied Machine Learning Explainability Techniques. Make ML models explainable and trustworthy for practical applications using LIME, SHAP, and more Aditya Bhattacharya - audiobook CD

    Autor:
    Aditya Bhattacharya
    Wydawnictwo:
    Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
    Ocena:
    Bądź pierwszym, który oceni tę książkę
    Stron:
    306
    Dostępne formaty:
         PDF
         ePub
    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.

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    O autorze książki

    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.

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