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Prestressed Concrete Building, Design, and Construction /
Published 2019Full-text access
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Class, Culture and Belonging in Rural Childhoods
Published 2019Full-text access
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Wellbeing and Self-Transformation in Natural Landscapes
Published 2019Full-text access
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Restating Orientalism : a critique of modern knowledge /
Published 2018Full-text access
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Wrinkled Polymer Surfaces Strategies, Methods and Applications /
Published 2019Full-text access
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Interpretable Machine Learning with Python : Build Explainable, Fair, and Robust High-Performance Models with Hands-on, Real-world Examples.
Published 2022Table of Contents: “…-- Local interpretation for a single prediction at a time using LimeTabularExplainer -- Using LIME for NLP -- Training a LightGBM model -- Local interpretation for a single prediction at a time using LimeTextExplainer -- Trying SHAP for NLP -- Comparing SHAP with LIME -- Mission accomplished -- Summary -- Dataset sources -- Further reading -- Chapter 6: Anchors and Counterfactual Explanations -- Technical requirements -- The mission -- Unfair bias in recidivism risk assessments -- The approach -- The preparations -- Loading the libraries -- Understanding and preparing the data -- The data dictionary -- Examining predictive bias with confusion matrices -- Data preparation -- Modeling -- Getting acquainted with our "instance of interest" -- Understanding anchor explanations -- Preparations for anchor and counterfactual explanations with alibi -- Local interpretations for anchor explanations -- Exploring counterfactual explanations -- Counterfactual explanations guided by prototypes -- Counterfactual instances and much more with WIT -- Configuring WIT -- Datapoint editor -- Performance & -- Fairness -- Mission accomplished -- Summary -- Dataset sources -- Further reading -- Chapter 7: Visualizing Convolutional Neural Networks -- Technical requirements -- The mission -- The approach -- Preparations -- Loading the libraries -- Understanding and preparing the data -- Data preparation -- Inspect data -- The CNN models -- Load the CNN model.…”
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