Aaron Schumacher takes a building-block approach to exploring the tools TensorFlow provides so you can build the systems you need and write your own TensorFlow—not just run other people’s scripts. Aaron discusses the many aspects of TensorFlow—including data management, machine learning, distribution, and serving—by comparing them with similar functionality in other toolkits (databases like LMDB and machine-learning libraries like scikit-learn, not to mention log monitors, web-serving frameworks, and distributed systems).
Aaron focuses on maintaining clarity with TensorFlow systems by separating concerns so that pieces of functionality can be more easily understood and even replaced. You’ll use concrete examples built around supervised learning, with code that’s ready to be understood and reused in multiple contexts, to identify where TensorFlow fits in machine-learning systems, implement system components using TensorFlow, and use TensorFlow tooling across the development process.
Aaron Schumacher is a data scientist and software engineer for Deep Learning Analytics. He has taught with Python and R for General Assembly and the Metis data science bootcamp. Aaron has also worked with data at Booz Allen Hamilton, New York University, and the New York City Department of Education. In his spare time, Aaron is a breakdancer. His career-best result was advancing to the semifinals of the R16 Korea 2009 individual footwork battle. He is honored to be the least significant contributor to TensorFlow 0.9.
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