In this section, we will cover an example of training and modeling a DNN using h2o. h2o is an open source, in-memory, scalable machine learning and AI platform used to build models with large datasets and implement predictions with high-accuracy methods. The h2o library is adapted at a large scale in numerous organizations to operationalize data science and provide a platform to build data products. h2o can run on individual laptops or large clusters of high-performance scalable servers. It works very fast, exploiting the machine architecture advancements and GPU processing. It has high-accuracy implementations of deep learning, neural networks, and other machine learning algorithms.
As said earlier, the h2o R package has functions for building general linear regression, K-means, Naive Bayes, PCA, forests, and deep learning (multilayer neuralnet...
