Explores perception in deep learning for autonomous vehicles, covering image classification, optimization methods, and the role of representation in machine learning.
Explores the inductive bias of different nonlinear supervised learning methods and the challenges of hyper-parameter tuning.
Explores neural networks optimization, including backpropagation, batch normalization, weight initialization, and hyperparameter search strategies.
Offers insights into the statistical physics of learning, exploring the relationship between neural network structure and disordered systems.
