Search Results - (star OR fair)

Vanity fair / by Thackeray, William Makepeace

Vanity fair / by Thackeray, William Makepeace

Vainty fair / by Thackeray, William

Star reporter / by Escott, John

The star zoo / by Gilbert, Harry

Star Identification Methods, Techniques and Algorithms / by Zhang, Guangjun

Table of Contents: “…From the Contents: Introduction -- Processing of Star Catalog and Star Image -- Star Identification using Modified Triangle Algorithm -- Star Identification using Star Patterns -- Star Identification using Neural Networks -- Rapid Star Tracking using Star Matching between Adjacent Frames -- Hardware Implement and Performance Test of Star Identification.…”
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My fair lady by Lerner, Alan Jay

A handful of stars / by Girion, Barbara

Marcel and white star / by Rabley, Stephen

Troubling a star.l by L'Engle, Madeleine

Who wants to be a star ? / by Allen, Julia

Hacettepe Sanat Müzesi Koleksiyonu'ndan seçmelerle bakış, iz ve bellek : Egeart etkinlikleri 2008 - 2009 /

Subjects: “…Art Fairs. 65124…”

Global perspectives on educational testing : examining fairness, high-stakes and policy reform / by Arbuthnot, Keena

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Measuring public space : The star model / by Varna, Georgiana

The eagle under the crescent and the star Polonezköy / by Akova, Akgün

Principles of economics / by Case, Karl E. (Karl Edwin)

Interactions in Multiagent Systems: Fairness, Social Optimality and Individual Rationality by Hao, Jianye, Leung, Ho-fung

Table of Contents: “…Introduction -- Background and Previous Work -- Fairness in Cooperative Multiagent Systems -- Social Optimality in Cooperative Multiagent Systems -- Individual Rationality in Competitive Multiagent Systems -- Social Optimality in Competitive Multiagent Systems -- Conclusion.…”
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Interpretable Machine Learning with Python : Build Explainable, Fair, and Robust High-Performance Models with Hands-on, Real-world Examples. by Mas�is, Serg

Table 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 &amp -- 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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The UN global compact fair competition and environmental and labour justice in international markets /

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Interpretable Machine Learning with Python : Build Explainable, Fair, and Robust High-Performance Models with Hands-on, Real-world Examples / by Masis, Serg, Molak, Aleksander, Rothman, Denis

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