Last week I wrote about the odds and probabilities of every five card poker hand. If you missed the article you can read it here.

As the Internet of Things (IoT) comes of age, we’re seeing more and more data from event-triggered sensors instead of sensors that record measurements at regular time intervals. These event-triggered sensors give rise to unevenly-spaced time series. Many analysts will immediately convert unevenly-spaced data to evenly-spaced time series to be compatible with existing sensor data analytics tools, but we have found that the conversion is usually unnecessary, and sometimes even causes problems.

In this post, I introduce and discuss binary stochastic neurons, implement trainable binary stochastic neurons in Tensorflow, and conduct several simple experiments on the MNIST dataset to get a feel for their behavior. Binary stochastic neurons offer two advantages over real-valued neurons: they can act as a regularizer and they enable conditional computation by enabling a network to make yes/no decisions. Conditional computation opens the door to new and exciting neural network architectures, such as the choice of experts architecture and heirarchical multiscale neural networks, which I plan to discuss in future posts.

In this post, I will talk about Language Models, when (and when not) to use LSTMs for language modeling, and some state of the art results. While I mostly discuss the “Exploring Limits” paper, I’m adding a few things elementary (for some) here for completeness sake. The Exploring Limits paper is not new, but I think it’s a good illustration of how deep sequence models should be used in practice. This is bigger than my usual posts, but hey we are exploring limits here. Ok, running out of italics now.

There are many tutorials with directions for how to use your Nvidia graphics card for GPU-accelerated Theano and Keras for Linux, but there is only limited information out there for you if you want to set everything up with Windows and the current CUDA toolkit. This is a shame however because there are a large number of computers out there with very nice video cards that are only running windows, and it is not always practical to use a Virtual Machine, or Dual-Boot.  So for today’s post we will go over how to get everything running in Windows 10 by saving you all the trial and error I went through. (All of these steps should also work in earlier versions of Windows).

I just found a minimal example of how easy it is to confuse R’s CSV parser when providing it with ill-formatted data. To make it easy to understand, I put material for reproducing the problem up on GitHub.

Every now and then I like to play a game of poker.

A deep learning platform that actually sells to the customer.

Deriving Distributions from Distances