Deep Learning Generalization Theoretical Foundations and Practical Strategies

Product Description

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 p…