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: convolutional neural networks can be used to provide object recognition for machine vision; recurrent neural networks, including long short-term memory architectures, allow the comprehension of time series and language; and generative networks give AI applications the ability to create output. The TensorFlow library provides for 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.
Robert Schroll demonstrates TensorFlow’s deep learning capabilities through its Python interface as he walks you through building machine learning algorithms piece by piece and implementing neural networks using TFLearn. Along the way, you’ll explore 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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