As recently covered by the New York Times, Google has totally revamped its Translate tool using deep learning. We'll learn about what's behind this system, and similar state of the art systems?including some more recent advances that haven't yet found their way into Google's tool. We'll start with looking at the original encoder-decoder model that neural machine translation is based on, and will discuss the various potential applications of this kind of sequence to sequence algorithm. We'll then look at attentional models, including applications in computer vision (where they are useful for large and complex images). In addition, we'll investigate stacking layers, both in the form of bidirectional layers and deep RNN architectures. We'll focus on the practical details of training real-world translation systems, and showing how to take advantage of PyTorch's dynamic nature to heavily customize an RNN as required for modern translation approaches.