This library fixes the researcher’s issue of maintaining their practices in AI research. It replicates the results published in recent papers allowing them to try new combinations. Despite similar variables, researches on AI often give different results. This harms the purpose of research. Since there are a massive number of variables in such researches, deep learning has to step in. It is like trying to build your building on top of what has already been set in foundation. This may not seem like a big deal, but in the field of AI research, it is invaluable. The library comes equipped with several essential requirements. It helps in faster training and modeling of machine learning models in TensorFlow. It is designed especially for creating deep learning models. Not only can one compare their work, but also can find how to stand out by comparing.
It can help users to define the pieces a user may need for their deep learning tool. The Tensor2Tensor (T2T) also beats GNMT+MoE which was the previous benchmark. It is also possible to define the architecture with some dozen coding lines. Additionally, it can also help to create models capable of doing multiple tasks. Everything that Google found out to be the best is present in the T2T framework. The framework has such a structure that one can replace its components in modules and it works. That is, one can also tailor it as per requirement without destroying its function. This also lets it train and create models much faster than possible by ordinary means. This library and further information is available on GitHub here