Deep Learning Paradigm

This lecture covers the introduction to deep learning, challenges in deep learning theory and applications, generalization in deep learning, linear classifiers, neural networks, universal approximation theorem, the rise of neural networks post-2010, convolutional architectures in computer vision, inductive bias, model scaling, robustness challenges, fairness issues, interpretability concerns, and energy efficiency in deep learning.