Discover how imposing constraints in deep learning can enhance model performance, particularly through the use of loss functions and manifold structures. Pavan explains the significance of invariance in feature spaces and how deep learning models can learn to crumple these spaces to better represent data. This exploration reveals the intricate relationship between architecture and the mathematical expressions that guide neural networks in their learning journey.
