Feb 21, 2017
Posted by Shane Hinshaw in categories: information science, robotics/AI
It wasn’t that long ago that building and training neural networks was strictly for seasoned computer scientists and grad students. That began to change with the release of a number of open-source machine learning frameworks like Theano, Spark ML, Microsoft’s CNTK, and Google’s TensorFlow. Among them, TensorFlow stands out for its powerful, yet accessible, functionality, coupled with the stunning growth of its user base. With this week’s release of TensorFlow 1.0, Google has pushed the frontiers of machine learning further in a number of directions.
TensorFlow isn’t just for neural networks anymore
In an effort to make TensorFlow a more-general machine learning framework, Google has added both built-in Estimator functionality, and support for a number of more traditional machine learning algorithms including K-means, SVM (Support Vector Machines), and Random Forest. While there are certainly other frameworks like SparkML that support those tools, having a solution that can combine them with neural networks makes TensorFlow a great option for hybrid problems.