Data scientists everywhere are delving more deeply into deep learning (DL). If you’re only skimming the surface of this trend, you might think that the Spark community, which focuses on broader applications of machine learning, is watching it all from the sidelines.
Though Spark is certainly at the forefront of many innovations in machine intelligence, DL industry tools and frameworks—such as TensorFlow and Caffe—seem to be grabbing much of the limelight now. Be that as it may, Spark is playing a significant, growing, and occasionally unsung role in the DL revolution. Developers of convolutional, recurrent, and other DL models use Spark in their projects for the following reasons:
Author: James Kobielus