Robert Schroll demonstrates TensorFlow’s capabilities through its Python interface and explores TFLearn, a high-level deep learning library built on TensorFlow. The TensorFlow library allows the use of data flow graphs for numerical computations with automatic parallelization across several CPUs or GPUs. This architecture makes it ideal for implementing neural networks and other machine-learning algorithms. Join in to learn how to use TFLearn and TensorFlow to build machine-learning models on real-world data.
Robert Schroll is a data scientist in residence at the Data Incubator. Previously, he held postdocs in Amherst, Massachusetts, and Santiago, Chile, where he realized that his favorite parts of his job were teaching and analyzing data. He made the switch to data science and has been at the Data Incubator since. Robert holds a PhD in physics from the University of Chicago.
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