Yoel Zeldes, an algorithm engineer at Taboola, shares insights from the TCE annual conference on deep learning, where multiple experts presented on diverse topics within the field. Highlights include Nati Srebro's exploration of optimization algorithms' inductive biases in deep learning, which influence generalization despite high model capacity, and Zachary Chase Lipton's discussion on enhancing efficiency in reinforcement learning through human interaction. Michal Irani presented a novel approach to super resolution using internal learning, and Lior Wolf explored transformative generative models for cross-instrument musical transformations. Uri Shalit introduced methods for causal inference in medical treatments, emphasizing the challenges of confounders in datasets, while Daniel Soudry investigated the counterintuitive phenomena of increasing test accuracy despite rising test loss. Finally, Yoav Goldberg analyzed the capabilities of different RNN architectures in language processing, revealing insights into their varied performance on tasks like sentence length prediction and word order. The conference concluded with a panel reflecting on the balance between deep learning and classical machine learning, emphasizing the importance of understanding the limitations of deep learning in critical applications.