Few other technologies have penetrated technology companies as rapidly as Docker (or more generally, containers). It seems like the majority of developers and companies are using containers in one way or another. Many use containers to simplify the setup of local development environments, but more and more companies are starting to completely re-architect their infrastructure and deployment processes around containers. In this post I’m hoping to provide a brief overview of the current state of the ecosystem.

A type of “Recurrent neural networks” architecture initially developed in Juergen Schmidhuber’s research groups at the Swiss AI Lab IDSIA and TU Munich greatly improved Google Voice (by 49%) and are now available to a billion users. You can find the recent Google Research Blog on this, by Haşim Sak, Andrew Senior, Kanishka Rao, Françoise Beaufays and Johan Schalkwyk:

Yesterday Hadley’s functional programming package purrr was published to CRAN. It is designed to bring convenient functional programming paradigma and add another data manipulation framework to R.

## Yet Another PhD to Data Science Post (Part II)

Our theano.function call looks a bit different than in our first example. Yeah, we have this additional updates parameter. updates allows us to update our shared variables according to an expression. updates expects a list of 2-tuples:

## Yet Another PhD to Data Science Post (Part I)

By the time I’d finished building the model in my last blog, I’d started to overload my computer’s RAM and CPU, so much so that I couldn’t add any more features. One solution could be for me to upgrade my hardware, or rent out a cluster in the cloud, but I’m trying to save money at the moment. So today I’m going to restructure my predictive solution into separate predictive models, each of which do not individually overload my computer, but which are combined via stacking to give an overall solution that is more predictive than any of the individual models.

An increasing number of startups seem to be building distributed teams, particularly in engineering. But making a distributed team work well isn’t easy. From what I’ve seen most distributed teams are less productive than their centralized counterparts. Here are a few things from my own experience that I think are crucial to the effectiveness such teams. If I had to summarize all of the below I would do it as: Remove synchronous communication, and when in doubt, over-communicate.

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