This book provides a comprehensive exploration of generalization in deep learning, focusing on both theoretical foundations and practical strategies. It delves deeply into how machine learning models, particularly deep neural networks, achieve robust performance on unseen data.
This book provides a comprehensive exploration of generalization in deep learning, focusing on both theoretical foundations and practical strategies. It delves deeply into how machine learning models, particularly deep neural networks, achieve robust performance on unseen data.
Liu Peng is currently an Assistant Professor of Quantitative Finance at the Singapore Management University (SMU). His research interests include generalization in deep learning, sparse estimation, Bayesian optimization.
Inhaltsangabe
1. Unveiling Generalization in Deep Learning 2. Introduction to Statistical Learning Theory 3. Classical Perspectives on Generalization 4. Modern Perspectives on Generalization 5. Fundamentals of Deep Neural Networks 6. A Concluding Perspective
1. Unveiling Generalization in Deep Learning 2. Introduction to Statistical Learning Theory 3. Classical Perspectives on Generalization 4. Modern Perspectives on Generalization 5. Fundamentals of Deep Neural Networks 6. A Concluding Perspective
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