Cook - Practical machine learning with H2O: powerful, scalable techniques for deep learning and AI

by Darren Cook

Copyright 2017 Darren Cook. All rights reserved.

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978-1-491-96460-6

[LSI]

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. Weve 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 Id 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: trees, linear models, unsupervised learning, etc.), and I was immediately impressed. It ticks all the boxes:

• Open source (the liberal Apache license)

• Easy to use

• Scalable to big data

• Well-documented and commercially supported

• On its third version (i.e., a mature architecture)

• Wide range of OS/language support

With the high-quality team that H2O.ai (the company behind H2O) has put together, it is only going to get better. There is the attitude of not just How do we get this to work? but How do we get this to work efficiently at big data scale? permeating the whole development.

If machine learning has come of age, H2O looks to be not just an economical family car for it, but simultaneously the large load delivery truck for it. Stretching my vehicle analogy a bit further, this book will show you not just what the dashboard controls do, but also the best way to use them to get from A to B. It will be as practical as possible, with only the bare minimum explanation of the maths or theory behind the learning algorithms.

Of course H2O is not perfect; here are a few issues Ive noticed people mutter about. There is no GPU support (which could make deep learning, in particular, quicker). The cluster support is all bout that bass (big data), no treble (complex but relatively small data), so for the latter you may be limited to needing a single, fast, machine with lots of cores. Also no high availability (HA) for clusters. H2O compiles to Java; it is well-optimized and the H2O algorithms are known for their speed but, theoretically at least, carefully optimized C++ could be quicker. There is no SVM algorithm. Finally, it tries to support numerous platforms, so each has some rough edges, and development is sometimes slowed by trying to keep them all in sync.

In other words, and wringing the last bit of life out of my car analogy: a Formula 1 car might beat it on the straights, and it isnt yet available in yellow.