Deep Learning Generalization by Liu Peng (.ePUB)+
File Size: 13.4 MB
Deep Learning Generalization: Theoretical Foundations and Practical Strategies by Liu Peng
Requirements: .ePUB, .PDF reader, 13.4 MB
Overview: 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.
Genre: Non-Fiction > Tech & Devices
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