Memory Optimization in Networks

Kyle and Rasmus discuss the challenges of memory optimization in neural networks, emphasizing the importance of having a loss on every step to maintain stability and avoid gradient issues. Rasmus explains how their network stores all sentences in memory, making it easier to remember and traverse information efficiently.