The TensorFlow library provides for 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.
Robert Schroll demonstrates TensorFlow’s capabilities through its Python interface, walking you through building machine-learning algorithms piece by piece and using the higher-level abstractions provided by TensorFlow. You’ll then use this knowledge 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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