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The speakers dicuss TF Hub, a new library built to foster the publication, discovery, and consumption of reusable parts of machine learning models. The main part of the launch is a repository of modules, which are self-contained pieces of TensorFlow graphs that can be reused across different tasks. Modules contain variables that have been pre-trained for a task using a large dataset. By reusing a module on a related or similar task, a user can train a model with a smaller dataset, improve generalization, or simply speed up training. Try out the end-to-end example on GitHub → https://goo.gl/4DBvX7