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[null,"1.","Record Nr.",null,"UNINA9910456572203321"]
[null,null,"Autore",null,"Green Michael Cullen <1977->"]
[null,null,"Titolo",null,"Black Yanks in the Pacific [[electronic resource] ] : race in the making of American military empire after World War II / / Michael Cullen Green"]
[null,null,"Pubbl/distr/stampa",null,"Ithaca [N.Y.], : Cornell University Press, 2010"]
[null,null,"ISBN",null,"0-8014-6221-5"]
[null,null,"Descrizione fisica",null,"1 online resource (219 p.)"]
[null,null,"Collana",null,"United States in the world"]
[null,null,"Disciplina",null,"940.54/03"]
[null,null,"Soggetti",null,"African American soldiers - Japan - History - 20th century"]
[{"$nulls":4},"African American soldiers - Korea - History - 20th century"]
[{"$nulls":4},"Korean War, 1950-1953 - Participation, African American"]
[{"$nulls":4},"Electronic books."]
[{"$nulls":4},"United States Armed Forces African Americans History 20th century"]
[{"$nulls":4},"Japan History Allied occupation, 1945-1952"]
[{"$nulls":4},"United States Race relations History 20th century"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Bibliographic Level Mode of Issuance: Monograph"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Introduction : everyday racial politics in a military empire -- Reconversion blues and the appeal of (re)enlistment -- The American dream in a prostrate Japan -- The public politics of intimate affairs -- A brown baby crisis -- The race of combat in Korea -- Epilogue : military desegregation in a militarized world."]
[null,null,null,"Sommario/riassunto","By the end of World War II, many black citizens viewed service in the segregated American armed forces with distaste if not disgust. Meanwhile, domestic racism and Jim Crow, ongoing Asian struggles against European colonialism, and prewar calls for Afro-Asian solidarity had generated considerable black ambivalence toward American military expansion in the Pacific, in particular the impending occupation of Japan. However, over the following decade black military service enabled tens of thousands of African Americans to interact daily with Asian peoples-encounters on a scale impossible prior to 1945. It also encouraged African Americans to share many of the same racialized attitudes toward Asian peoples held by their white counterparts and to identify with their government's foreign policy objectives in Asia.In Black Yanks in the Pacific, Michael Cullen Green tells the story of African American engagement with military service in occupied Japan, war-torn South Korea, and an emerging empire of bases anchored in "]
[{"$nulls":4},"those two nations. After World War II, African Americans largely embraced the socioeconomic opportunities afforded by service overseas-despite the maintenance of military segregation into the early 1950s-while strained Afro-Asian social relations in Japan and South Korea encouraged a sense of insurmountable difference from Asian peoples. By the time the Supreme Court declared de jure segregation unconstitutional in its landmark 1954 Brown v. Board of Education decision, African American investment in overseas military expansion was largely secured. Although they were still subject to discrimination at home, many African Americans had come to distrust East Asian peoples and to accept the legitimacy of an expanding military empire abroad."]
[null,"2.","Record Nr.",null,"UNINA9910522997303321"]
[null,null,"Autore",null,"Nandi Anirban"]
[null,null,"Titolo",null,"Interpreting machine learning models : learn model interpretability and explainability methods / / Anirban Nandi and Aditya Kumar Pal"]
[null,null,"Pubbl/distr/stampa",null,"New York, New York : , : Apress, , [2022]"]
[{"$nulls":4},"©2022"]
[null,null,"ISBN",null,"1-5231-5100-5"]
[{"$nulls":4},"1-4842-7802-X"]
[null,null,"Descrizione fisica",null,"1 online resource (355 pages)"]
[null,null,"Disciplina",null,"006.31"]
[null,null,"Soggetti",null,"Machine learning - Mathematical models"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Chapter 1: The Evolution of Machine Learning -- Chapter 2: Introduction to Model interpretability. -- Chapter 3: Machine Learning Interpretability Taxonomy -- Chapter 4: Common Properties of Explanations Generated by Interpretability Methods -- Chapter 5: Human Factors in Model Interpretability -- Chapter 6: Explainability Facts: A Framework for Systematic Assessment of Explainable Approaches -- Chapter 7: Interpretable ML and Explainable ML Differences -- Chapter 8: Framework of Model Explanations -- Chapter 9: Feature Importance methods -- Details and usage examples -- Chapter 10: Detailing rule-based methods -- Chapter 11: Detailing Counterfactual Methods -- Chapter 12: Detailing Image interpretability methods -- Chapter 13: Explaining text classification models -- Chapter 14: Role of Data in Interpretability -- Chapter 15: The 8 pitfalls of explainability methods -- Conclusion. -- References."]
[null,null,null,"Sommario/riassunto","Understand model interpretability methods and apply the most suitable one for your machine learning project. This book details the concepts of machine learning interpretability along with different types of explainability algorithms. You'll begin by reviewing the theoretical aspects of machine learning interpretability. In the first few sections you'll learn what interpretability is, what the common properties of interpretability methods are, the general taxonomy for classifying methods into different sections, and how the methods should be assessed in terms of human factors and technical requirements. Using a holistic approach featuring detailed examples, this book also includes "]
[{"$nulls":4},"quotes from actual business leaders and technical experts to showcase how real life users perceive interpretability and its related methods, goals, stages, and properties. Progressing through the book, you'll dive deep into the technical details of the interpretability domain. Starting off with the general frameworks of different types of methods, you'll use a data set to see how each method generates output with actual code and implementations. These methods are divided into different types based on their explanation frameworks, with some common categories listed as feature importance based methods, rule based methods, saliency maps methods, counterfactuals, and concept attribution. The book concludes by showing how data effects interpretability and some of the pitfalls prevalent when using explainability methods. What You'll Learn Understand machine learning model interpretability Explore the different properties and selection requirements of various interpretability methods Review the different types of interpretability methods used in real life by technical experts Interpret the output of various methods and understand the underlying problems Who This Book Is For Machine learning practitioners, data scientists and statisticians interested in making machine learning models interpretable and explainable; academic students pursuing courses of data science and business analytics."]