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7.2 MB
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C348CE6564C3D963104241F78D9BA98A5C7050CF
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Sept. 14, 2025, 10:48 a.m.
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(Last updated: Sept. 14, 2025, 10:49 a.m.)
| File | Size |
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| Peng L. Deep Learning Generalization. Theoretical Foundations...Strategies 2026.pdf | 7.2 MB |
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25.8 MB
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| Uploaded by indexFroggy | Size 25.8 MB | Health [ 12 /1 ] | Added 2023-10-27 |
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| Uploaded by roflcopter | Size 260.5 MB | Health [ 17 /0 ] | Added 2023-06-02 |
NOTE
SOURCE: Peng L. Deep Learning Generalization. Theoretical Foundations...Strategies 2026
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COVER
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MEDIAINFO
Textbook in PDF format 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. Key topics include balancing model complexity, addressing overfitting and underfitting, and understanding modern phenomena such as the double descent curve and implicit regularization. The book offers a holistic perspective by addressing the four critical components of model training: data, model architecture, objective functions, and optimization processes. It combines mathematical rigor with hands-on guidance, introducing practical implementation techniques using PyTorch to bridge the gap between theory and real-world applications. For instance, the book highlights how regularized Deep Learning models not only achieve better predictive performance but also assume a more compact and efficient parameter space. Structured to accommodate a progressive learning curve, the content spans foundational concepts like statistical learning theory to advanced topics like Neural Tangent Kernels and overparameterization paradoxes. Examples in Python. The overarching goal is to develop a robust model that not only performs well on the training data (the practice exam) but also generalizes effectively to the test data (the final exam). In Deep Learning, this entails uncovering patterns that are not merely memorized from the training set but are broadly applicable to new, unseen data. If the model is overfitting, memorizing the practice exam questions without grasping the underlying concepts and patterns, it can perform poorly on the final exam. In contrast, a well-generalized model is better equipped to handle new questions (test data) confidently, reflecting a deeper understanding of the fundamental patterns of the data. Depending on the model's ability to generalize, we can encounter four possible outcomes: good performance on both the training and test data, representing strong generalization because the model captures underlying patterns without overfitting; poor performance on both, reflecting underfitting when the model fails to learn meaningful relationships; strong performance on the training set yet poor performance on the test set, indicating overfitting caused by memorizing training examples rather than understanding broader patterns; and, in some rare cases, unexpectedly better performance on the test data than on the training data, which can occur due to randomness or a fortuitous alignment with the test distribution. By synthesizing classical and modern views of generalization, the book equips readers to develop a nuanced understanding of key concepts while mastering practical applications. For academics, the book serves as a definitive resource to solidify theoretical knowledge and explore cutting-edge research directions. For industry professionals, it provides actionable insights to enhance model performance systematically. Whether you're a beginner seeking foundational understanding or a practitioner exploring advanced methodologies, this book offers an indispensable guide to achieving robust generalization in Deep Learning. Preface Unveiling Generalization in Deep Learning Introductionto Statistical Learning Theory Classical Perspectives on Generalization Modern Perspectives on Generalization Fundamentals of Deep Neural Networks A Concluding Perspective
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