We finally published our first article on deep learning (a form of artificial intelligence, AI) in orthopedics! We got standard off-the-shelf neural networks to perform equally well as senior orthopedic surgeons for identifying fractures. This was under the premise that … Continue reading
In previous posts we’ve looked into the basic structure of the torch-dataframe package. In this post we’ll go through the [mnist example][mnist ex] that shows how to best integrate the dataframe with [torchnet](https://github.com/torchnet/torchnet).
In my previous two posts I covered the most basic data manipulation that you may need. In this post I’ll try to give a quick introduction to some of the sampling methods that we can use in our machine learning … Continue reading
In my [previous post][intro post] we took a look at some of the basic functionality. In this post I’ll try to show how to manipulate your dataframe. Note though, the [torch-dataframe][tdf github] is not about data munging, there are far … Continue reading
Handling [tabular data](https://en.wikipedia.org/wiki/Table_(information)) is generally at the heart of most research projects. As I started exploring [Torch](http://torch.ch/) that uses the [Lua](https://www.lua.org/) language for [deep learning](https://en.wikipedia.org/wiki/Deep_learning) I was surprised that there was no package that would correspond to the functionality available … Continue reading
One of the successful insights to training neural networks has been the rectified linear unit, or short the ReLU, as a fast alternative to the traditional activation functions such as the sigmoid or the tanh. One of the major advantages … Continue reading