The TensorFlow library enables the use of data flow graphs for numerical computations, with automatic parallelization across several CPUs or GPUs, making it ideal for implementing neural networks and other machine learning algorithms. Robert Schroll demonstrates TensorFlow’s capabilities through its Python interface and walks you through building machine learning models on real-world data piece by piece to using the higher-level abstractions provided by TensorFlow.
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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