XGBoost vs Tensor2Tensor: What are the differences?

What is XGBoost? Scalable and Flexible Gradient Boosting. Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Flink and DataFlow.

What is Tensor2Tensor? Library of deep learning models & datasets designed to make deep learning more accessible (by Google Brain). It is a library of deep learning models and datasets designed to make deep learning more accessible and accelerate ML research. It was developed by researchers and engineers in the Google Brain team and a community of users.

XGBoost belongs to "Python Build Tools" category of the tech stack, while Tensor2Tensor can be primarily classified under "Machine Learning Tools".

Some of the features offered by XGBoost are:

• Flexible
• Portable
• Multiple Languages

On the other hand, Tensor2Tensor provides the following key features:

• Many state of the art and baseline models are built-in and new models can be added easily
• Many datasets across modalities - text, audio, image - available for generation and use, and new ones can be added easily
• Models can be used with any dataset and input mode (or even multiple)

Tensor2Tensor is an open source tool with 9.7K GitHub stars and 2.51K GitHub forks. Here's a link to Tensor2Tensor's open source repository on GitHub.