Deep learning (DL) is still one of the fastest developing areas in machine learning. As models increase their complexity and data sets grow in size, your model training can last hours or even days. In this session we will explore some of the trends in Deep Neural Networks to accelerate training using parallelize/distribute deep learning.
We will also present how to apply some of these strategies using Cloudera Data Science Workbenck and some popular (DL) open source frameworks like Uber Horovod, Tensorflow and Keras.
Rafael Arana, Senior Solutions Architect
Zuling Kang, Senior Solutions Architect