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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You’ll need to understand Python’s syntax — we’ll jump right in writing code right away. But that’s about it. The only library we’ll be using extensively is TensorFlow itself, and we’ll teach you all about that.
How much experience in Python is needed for this class? Is it an understanding of the syntax, or a deeper understanding of the different libraries and their uses? Thanks!
Hi Yingpei – sorry for the delay in getting back to you – the prerequisites for this training are that attendees should be familiar with Python, and should should come with a computer with a modern version of Chrome or Firefox. That’s it! Please let us know if you have any other quesitons.
What prerequisites do we need for this class?