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Explainable AI Recipes - Implement Solutions to Model Explainability and Interpretability with Python (Paperback, 1st ed.)
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Explainable AI Recipes - Implement Solutions to Model Explainability and Interpretability with Python (Paperback, 1st ed.)
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Understand how to use Explainable AI (XAI) libraries and build
trust in AI and machine learning models. This book utilizes a
problem-solution approach to explaining machine learning models and
their algorithms. The book starts with model interpretation for
supervised learning linear models, which includes feature
importance, partial dependency analysis, and influential data point
analysis for both classification and regression models. Next, it
explains supervised learning using non-linear models and
state-of-the-art frameworks such as SHAP values/scores and LIME for
local interpretation. Explainability for time series models is
covered using LIME and SHAP, as are natural language
processing-related tasks such as text classification, and sentiment
analysis with ELI5, and ALIBI. The book concludes with complex
model classification and regression-like neural networks and deep
learning models using the CAPTUM framework that shows feature
attribution, neuron attribution, and activation attribution. After
reading this book, you will understand AI and machine learning
models and be able to put that knowledge into practice to bring
more accuracy and transparency to your analyses. What You Will
Learn Create code snippets and explain machine learning models
using Python Leverage deep learning models using the latest code
with agile implementations Build, train, and explain neural network
models designed to scale Understand the different variants of
neural network models Who This Book Is For AI engineers, data
scientists, and software developers interested in XAI
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