Google's Tensor2Tensor makes it easier to conduct deep learning experiments

Google's brain team is open sourcing Tensor2Tensor, a new deep learning library designed to help researchers replicate results from recent papers in the field and push the boundaries of what's possible by trying new combinations of models, datasets and other parameters. The sheer number of variables in AI research combined with the fast pace of new developments makes it difficult for experiments run in two distinct settings to match. This is a pain for researchers and a drag on research progress. The Tensor2Tensor library makes it easier to maintain best practices while conducting AI research. It comes equipped with key ingredients including hyperparameters, data-sets, model architectures and learning rate decay schemes.