Training a neural net to drive by pure observation requires ensuring that good behavior is being learned from the right signals and that test results in simulation can be transferred to the real world. This talk will walk you through the evolution of a deep network to deal with these challenges by incorporating techniques like data perturbation, input and loss dropout, and domain-specific losses. You’ll learn how input ablation can help debug what the network has really learned.