Many of the deep- earning 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, including 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.
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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