Attendees should have experience coding in Python and familiarity with matrix algebra. Having a computer that can connect to the internet is a necessity. No downloads or software installations are required, but it is recommended that attendees have either Chrome or Firefox installed on their computers.
The TensorFlow library enables 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. Dana Mastropole details TensorFlow’s capabilities through its Python interface, moving from building machine learning algorithms piece by piece to using the higher-level abstractions provided by TensorFlow. You’ll use this knowledge to build machine learning models on real-world data.
You’ll be provisioned a cloud instance with TensorFlow as a part of this course.
Dana Mastropole is a data scientist in residence at the Data Incubator and contributes to curriculum development and instruction. Previously, Dana taught elementary school science after completing MIT’s Kaufman teaching certificate program. She studied physics as an undergraduate student at Georgetown University and holds a master’s in physical oceanography from MIT.
Get the Platinum pass or the Training pass to add this course to your package.
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