Regularization in Deep Learning (MEAP V06)

Make your deep learning models more generalized and adaptable! These practical regularization techniques improve training efficiency and help avoid overfitting errors.
 
Regularization in Deep Learning includes:
• Insights into model generalizability
• A holistic overview of regularization techniques and strategies
• Classical and modern views of generalization, including bias and variance tradeoff
• When and where to use different regularization techniques
• The background knowledge you need to understand cutting-edge research
 
Regularization in Deep Learning delivers practical techniques to help you build more general and adaptable deep learning models. It goes beyond basic techniques like data augmentation and explores strategies for architecture, objective function, and optimization. You’ll turn regularization theory into practice using PyTorch, following guided implementations that you can easily adapt and customize for your own model’s needs. Along the way, you’ll get just enough of the theory and mathematics behind regularization to understand the new research emerging in this important area.
 
About the reader
For data scientists, machine learning engineers, and researchers with basic model development experience.
 
About the author
Peng Liu is an experienced data scientist focusing on applied research and development of high-performance machine learning models in production. He holds a Ph.D. in statistics from the National University of Singapore, and teaches advanced analytics courses as an adjunct lecturer in universities. He specializes in the statistical aspects of deep learning.