Abstract: Deep learning revolutionized computer vision, speech recognition, natural language understanding, and more. However, from the theoretical perspective we know that training neural networks is computationally hard and one can construct distributions on which deep learning fails. The talk will propose several parameters of distributions that can move them from being easy-to-train to being hard-to-train.