In previous sections, we covered neural networks and deep architectures running on a local computer and we found that neural networks are already highly vectorized but still computationally expensive. There is not much that we can do if we want to make the algorithm more scalable on a desktop computer other than utilizing Theano and GPU computing. So if we want to scale deep learning algorithms more drastically, we will need to find a tool that can run algorithms out-of-core instead of on a local CPU/GPU. H2O is, at this moment, the only open source out-of-core platform that can run deep learning algorithms quickly. It is also cross-platform; besides Python, there are APIs for R, Scala, and Java.
H2O is compiled on a Java-based platform developed for a wide range of data science-related tasks such as datahandling and machine learning. H2O runs on distributed and parallel CPUs in-memory so that data will be stored in the H2O cluster. The H2O platform—as of yet...
