Practical Machine Learning with H2O

Preface

It feels like machine learning has finally come of age. It has been a long childhood, stretching back to the 1950s and the first program to learn from experience (playing checkers), as well as the first neural networks. We’ve been told so many times by AI researchers that the breakthrough is “just around the corner” that we long ago stopped listening. But maybe they were on the right track all along, maybe an idea just needs one more order of magnitude of processing power, or a slight algorithmic tweak, to go from being pathetic and pointless to productive and profitable.

In the early ’90s, neural nets were being hailed as the new AI breakthrough. I did some experiments applying them to computer go, but they were truly awful when compared to the (still quite mediocre) results I could get using a mix of domain-specific knowledge engineering, and heavily pruned tree searches. And the ability to scale looked poor, too. When, 20 years later, I heard talk of this new and shiny deep learning thing that was giving impressive results in computer go, I was confused how this was different from the neural nets I’d rejected all those years earlier. “Not that much” was the answer; sometimes you just need more processing power (five or six orders of magnitude in this case) for an algorithm to bear fruit.

H2O is software for machine learning and data analysis. Wanting to see what other magic deep learning could perform was what personally led me to H2O (though it does more than that: ...