Deep Learning Meets Statistical Modeling

This chapter explores the integration of deep learning with traditional statistical methods, emphasizing the importance of simplified models to reduce data dependency in neural network training. It discusses the constraints applied to deep neural networks, particularly focusing on orthonormality and manifold structures, which enhance performance in tasks like image recognition. The chapter highlights the challenges posed by variations in object transformations and human motion, suggesting robust methodologies to improve model efficacy and adaptability.