Arama Sonuçları - "common sense"

Sense and sensibility / Yazar: West, Clare

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Quality in the 21st Century Perspectives from ASQ Feigenbaum Medal Winners /

İçindekiler: “…Introduction -- Quality: From Past Perfect to Future Conditional -- Importance of Data Quality for Analysis -- The Future of Quality: Strategy, Leadership, and an Opportunity to Improve Quality of Life on a Global Scale there to be Seized or Lost -- Common Sense, Use the Right Tool for the Job -- Development of Strategic Quality Metrics for Organizations Using Hoshin Kanri -- Customer Experience Driving Quality Transformation -- The Role of Learning and Exploration in Quality Management and Continuous Improvement -- The Efficienti: Quality Professionals of the 21st Century -- Final Thoughts -- Feigenbaum Medalists (Non-Authors) Short Bios.…”
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Achieving evidence-informed policy and practice in education : EvidencED / Yazar: Brown, Chris, 1975-

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Children and Materialities The Force of the More-than-human in Children's Classroom Lives / Yazar: Myers, Casey Y.

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A Brief History of Mechanical Engineering Yazar: Dixit, Uday Shanker, Hazarika, Manjuri, Davim, J. Paulo

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Globalization, critique and social theory diagnoses and challenges /

İçindekiler: “…The task of critical theory today : rethinking the critique of capitalism and its futures / Moishe Postone -- Profit maxims : capitalism and the common sense of time and money / David Norman Smith -- Theorizing modern society as an inverted reality : how critical theory and indigenous critiques of globalization must learn from each other / Asafa Jalata, Harry F. …”
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On the Logos: A Naïve View on Ordinary Reasoning and Fuzzy Logic Yazar: Trillas, Enric

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Sociology in Brazil A Brief Institutional and Intellectual History / Yazar: Domingos Cordeiro, Veridiana, Neri, Hugo

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Innovations in Big Data Mining and Embedded Knowledge

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Maritime Spatial Planning past, present, future /

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Deep Learning with Python, Second Edition. Yazar: Chollet, Francois

İçindekiler: “…4.1.5 Using a trained model to generate predictions on new data -- 4.1.6 Further experiments -- 4.1.7 Wrapping up -- 4.2 Classifying newswires: A multiclass classification example -- 4.2.1 The Reuters dataset -- 4.2.2 Preparing the data -- 4.2.3 Building your model -- 4.2.4 Validating your approach -- 4.2.5 Generating predictions on new data -- 4.2.6 A different way to handle the labels and the loss -- 4.2.7 The importance of having sufficiently large intermediate layers -- 4.2.8 Further experiments -- 4.2.9 Wrapping up -- 4.3 Predicting house prices: A regression example -- 4.3.1 The Boston housing price dataset -- 4.3.2 Preparing the data -- 4.3.3 Building your model -- 4.3.4 Validating your approach using K-fold validation -- 4.3.5 Generating predictions on new data -- 4.3.6 Wrapping up -- Summary -- 5 Fundamentals of machine learning -- 5.1 Generalization: The goal of machine learning -- 5.1.1 Underfitting and overfitting -- 5.1.2 The nature of generalization in deep learning -- 5.2 Evaluating machine learning models -- 5.2.1 Training, validation, and test sets -- 5.2.2 Beating a common-sense baseline -- 5.2.3 Things to keep in mind about model evaluation -- 5.3 Improving model fit -- 5.3.1 Tuning key gradient descent parameters -- 5.3.2 Leveraging better architecture priors -- 5.3.3 Increasing model capacity -- 5.4 Improving generalization -- 5.4.1 Dataset curation -- 5.4.2 Feature engineering -- 5.4.3 Using early stopping -- 5.4.4 Regularizing your model -- Summary -- 6 The universal workflow of machine learning -- 6.1 Define the task -- 6.1.1 Frame the problem -- 6.1.2 Collect a dataset -- 6.1.3 Understand your data -- 6.1.4 Choose a measure of success -- 6.2 Develop a model -- 6.2.1 Prepare the data -- 6.2.2 Choose an evaluation protocol -- 6.2.3 Beat a baseline -- 6.2.4 Scale up: Develop a model that overfits.…”
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