Hands-On Explainable AI (XAI) with Python - Rothman, Denis

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Résumé :
Effectively translating Al insights for business stakeholders requires careful planning, design, and visualization choices. The problem, the model, and the relationships among variables and their findings are often subtle, surprising, and technically complex. Hands-On Explainable Al (XAI) with Python will see you work with specific hands-on machine learning Python projects that are strategically arranged to enhance your grasp of Al results analysis. You will be building models, interpreting results with visualizations, and integrating XAI reporting tools and different applications. You will build XAI solutions in Python, TensorFlow 2, Google Cloud's XAI platform, Google Colaboratory, and other frameworks to open up the black box of machine learning models. The book will introduce you to several open-source XAI tools for Python that can be used throughout the machine learning project life cycle. You will learn how to explore machine learning model results, review key influencing variables and variable relationships, detect and handle bias and ethics issues, and integrate predictions using Python along with supporting the visualization of machine learning models into user explainable interfaces. By the end of this Al book, you will possess an in-depth understanding of the core concepts of XAI.

Biographie:
Denis Rothman graduated from Sorbonne University and Paris-Diderot University, writing one of the very first word2vector embedding solutions. He began his career authoring one of the first AI cognitive natural language processing (NLP) chatbots applied as a language teacher for Mo?t et Chandon and other companies. He has also authored an AI resource optimizer for IBM and apparel producers. He then authored an advanced planning and scheduling (APS) solution that is used worldwide. Denis is an expert in explainable AI (XAI), having added interpretable mandatory, acceptance-based explanation data and explanation interfaces to the solutions implemented for major corporate aerospace, apparel, and supply chain projects.