Dana Mastropole and Michael Li demonstrate TensorFlow’s capabilities through its Python interface and explore 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.
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.
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