Subject archive for "deep-learning," page 6

We have seen an explosion of interest among data scientists who want to use GPUs for training deep learning models. While the libraries to support this (e.g., keras, TensorFlow, etc) have become very powerful, data scientists are still plagued with configuration issues that limit their productivity.

When you have at your disposal 128 cores and 2TB of RAM, it’s hard not to experiment and attempt to find ways to leverage the amount of power that is at your fingertips. We’re excited to remind our readers that we support Amazon’s X1 instances in Domino, you can do data science on machines with 128 cores and 2TB of RAM — with one click:

This post provides a brief history lesson and overview of deep learning, coupled with a quick "how to" guide for dipping your toes into the water with H2O.ai. Then I describe how Domino lets us easily run H2O on scalable hardware and track the results of our deep learning experiments, to take analyses to the next level.