This document discusses neural Turing machines, which are neural networks combined with external memory systems that allow them to be trained end-to-end using backpropagation. Neural Turing machines can learn simple algorithms and generalize well for tasks like language modeling and question answering. However, they are difficult to train due to numerical instability and optimizing memory usage. The document recommends techniques like gradient clipping, loss clipping, and curriculum learning to improve training. It also covers developments like dynamic neural computers that can allocate and deallocate memory.