Training Cutting-Edge Neural Networks with Tensor2Tensor and 10 lines of code

New neural network architectures and novel AI research papers come out every week from professors at universities, researchers at Google and other big tech firms, or even just developers with a strong interest in Deep Learning. Unfortunately for individuals who don't have a Ph.D. or robust fluency in back-propagation, linear algebra, or computational math, implementing these new Deep Learning techniques with no high-level API (like Keras) can be challenging and time-consuming. Thankfully, the Google Brain team recognizes these widespread problems in the AI community and subsequently created an open source library to help address these issues. Although Deep Learning isn't always the silver bullet people hope for in the Data Science world, it is a very useful tool for Natural Language Processing (NLP) task. For example, the use of word embeddings has revolutionized the effectiveness of language understanding techniques. I wanted to make an offline French to English translator for my team and our clients, using the best current techniques, which at the time was the Transformer architecture.