Data & AI Digest for 2018-05-02

I’ve been in the process of transferring my blog (along with creating a personal website) to blogdown, which is hosted on Github Pages. The new blog, or rather, the continuation of this blog, will be at webbedfeet.github.io/posts, and it went live today. I’ll be cross-posting here for a while, at least until Tal gets my […]
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Hi there! I was training some ways to simulate animal (or other organisms) movements having into account habitat suitability. To do this, I used my previous eWalk model as the underlying process to simulate random or directional walks. This model is based on Brownian / Ornstein–Uhlenbeck process. You can find more about eWalk model here! Today, I will add one more element to this movement simulations. In this case, we will have into account the habitat or environmental preferences of the simulated species, to perform a simulation like this: First, we will create a raster layer as a random environmental variable, for example tree cover. library (raster) library (dismo) tc
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Summary: Our starting assumption that sequence problems (language, speech, and others) are the natural domain of RNNs is being challenged.  Temporal Convolutional Nets (TCNs) which are our workhorse CNNs with a few new features are outperforming RNNs on major applications today.  Looks like RNNs may well be history.
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