Arama Sonuçları - second baseman~

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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The impact of the OECD on education worldwide /

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Dimensional Analysis Beyond the Pi Theorem Yazar: Zohuri, Bahman

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Community colleges worldwide investigating the global phenomenon /

İçindekiler: “…Community colleges : where are they (not)? / Alexander W. Wiseman ... [et al.] -- Educational borrowing and the emergence of community college global counterparts / Rosalind Latiner Raby, Edward J. …”
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Complex Systems Design & Management Asia Smart Nations - Sustaining and Designing: Proceedings of the Second Asia-Pacific Conference on Complex Systems Design & Management, CSD&M A...

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Tree-Shaped Fluid Flow and Heat Transfer Yazar: Miguel, António F., Rocha, Luiz A. O.

İçindekiler: “….- V References.- 5 ITrapezoidal Basement.- II Optimization Applying Genetic Algorithm (GA).- III Cylindrical Basement.- IV Best Geometries Determined by Exhaustive Search Method.- V Best geometries determined by Genetic Algorithm (GA).- VI References.- 6 I. …”
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Concrete Structures Yazar: Setareh, Mehdi, Darvas, Robert

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Disputes in everyday life social and moral orders of children and young people /

İçindekiler: “…/ Alexandra Kent -- "Pretend I was Mummy" : children's production of authority and subordinance in their pretend play interaction during disputes / Charlotte Cobb-Moore -- Being doggy : disputes embedded in preschoolers' family role-play / Polly Björk-Willén -- Working towards trouble : some categorial resources for accomplishing disputes in a correctional youth facility / Jakob Cromdal, Karin Osvaldsson -- Tattling and dispute resolution : moral order, emotions and embodiment in the teacher-mediated disputes of young second language learners / Asta Cekaite -- Challenging and orienting to monolingual school norms in Turkish American children's peer disputes and classroom negotiations at a U.S. …”
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Essays on the History of Mechanical Engineering

İçindekiler: “…Section I: Eminent Scientists of the Past -- 1 Lagrange as a Historian of Mechanics, by Agamenon Rodrigues Eufrásio Oliveira -- 2 Giuseppe Antonio Borgnis and his Handbook of Machine Designs, by Marco Ceccarelli -- 3 Tribute to Reuleaux, Roberto Bragastini -- 4 Elia Ovazza, Professor of TMM in Palermo around the End of the 19th Century, by Marco Ceccarelli, Francesco Sorge, Giuseppe Genchi -- 5 Academician K.V. Frolov at Bauman Moscow State Technical University, by Olga Egorova, Gennady Timofeev -- Section II: History of Industrial Installations -- 6 Historical Watermills: Architectural, Mechanical and Hydraulic Heritage, by Mario Centofanti, Stefano Brusaporci, Vittorio Lucchese -- 7 The Mill at the Lagoon of Orbetello. …”
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English Language Teaching Research in the Middle East and North Africa Multiple Perspectives /

İçindekiler: “…A. Balilah, Yasaman Rafat and Lisa M. D. Archibald -- Chapter Nineteen: Difficulties Facing Children in Learning English as a Foreign Language in the Libyan Context, Deya M. …”
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Deep Learning-Based Face Analytics. Yazar: Ratha, Nalini K.

İçindekiler: “…10.5.1 Training Details -- 10.5.2 Methods for Evaluation -- 10.5.3 Results on IJB-A Dataset -- 10.5.4 Results on YouTube Face dataset -- 10.5.5 Results on Celebrity-1000 Dataset -- 10.6 Conclusions -- References -- 11 Thermal-to-Visible Face Synthesis and Recognition -- 11.1 The Infrared Spectrum -- 11.2 GAN-Based Synthesis of Visible Faces From Thermal Faces -- 11.2.1 Generative Adversarial Networks (GANs) -- 11.2.2 GAN-Based Synthesis Network -- 11.3 Synthesis of High-Quality Visible Faces From Polarimetric Thermal Faces Using GANs -- 11.4 Thermal-to-Visible Face Verification Via Attribute-Preserved Synthesis -- 11.5 Self-attention Guided Synthesis -- 11.6 Conclusion -- References -- 12 Obstructing DeepFakes by Disrupting Face Detection and Facial Landmarks Extraction -- 12.1 Introduction -- 12.2 Background and Related Works -- 12.3 Attacking Face Detectors -- 12.3.1 White-Box Adversarial Perturbation Generation -- 12.3.2 Gray-Box Adversarial Perturbation Generation -- 12.3.3 Black-Box Adversarial Perturbation Generation -- 12.4 Attacking Facial Landmark Extractors -- 12.5 Experiments -- 12.5.1 Attacking Face Detection -- 12.5.2 Attacking Landmark Extractors -- 12.6 Conclusion -- References -- 13 Multi-channel Face Presentation Attack Detection Using Deep Learning -- 13.1 Introduction -- 13.2 Related Work -- 13.2.1 Feature-Based Approaches for Face PAD -- 13.2.2 CNN-Based Approaches for Face PAD -- 13.2.3 One-Class Models for Face PAD -- 13.2.4 Multi-channel-Based Approaches for Face PAD -- 13.2.5 Challenges in PAD -- 13.3 Proposed Method -- 13.3.1 Preprocessing -- 13.3.2 Network Architecture -- 13.3.3 One-Class Contrastive Loss (OCCL) -- 13.3.4 Implementation Details -- 13.4 Experiments -- 13.4.1 WMCA Dataset -- 13.4.2 MLFP Dataset -- 13.4.3 SiW-M Dataset -- 13.4.4 Evaluation Metrics -- 13.4.5 Baselines.…”
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