Google's open source machine learning library, TensorFlow, is a well-known tool for building neural networks and is available via conda-forge. But ever since the 1.3 release (Fall 2017), Google has introduced many underutilized features that simplify the data science workflow. In this session, Joseph will introduce the high-level benefits of TensorFlow and walk through live examples exploring: (1) the Datasets API for seamlessly reading in datasets larger than memory; (2) TensorFlow Estimators, simple pre-packaged machine learning models comparable to those found in sklearn; and (3) TensorFlow Eager Execution Mode, which enables simpler debugging. Along the way, Joseph will solve real-world data science problems and highlight opportunities for open source contribution.
I am looking for editors/curators to help with branches of the tree. Please send me an email if you are interested.