Many of the deep learning algorithms used in AI applications are powered by large matrix operations. TensorFlow provides data flow graphs for such operations, allowing algorithms to be easily parallelized across multiple processors or machines. This makes TensorFlow an ideal environment for implementing neural networks and other deep learning algorithms. Dylan Bargteil offers an overview of the TensorFlow graph using its Python API. You’ll start with simple machine learning algorithms and move on to implementing neural networks, including convolutional neural networks to provide object recognition for machine vision; recurrent neural networks such as long short-term memory architectures that allow the comprehension of time series and language; and generative networks, which give AI applications the ability to create output. Along the way, Dylan covers several real-world deep learning applications, including machine vision, text processing, and generative networks.
Dylan Bargteil is a data scientist in residence at the Data Incubator, where he works on research-guided curriculum development and instruction. Previously, he worked with deep learning models to assist surgical robots and was a research and teaching assistant at the University of Maryland, where he developed a new introductory physics curriculum and pedagogy in partnership with HHMI. Dylan studied physics and math at University of Maryland and holds a PhD in physics from New York University.
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