It’s all about the numbers and getting the tensors right. The image is cc by David Asch
.

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). Continue reading →

Subsetting and batching is like dealing cards – should be random unless you are doing a trick. The image is cc from Steven Depolo.

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 projects. Continue reading →

Forming your data to your needs is crucial. The image i cc by Lennart Tange.

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 more powerful tools in other languages for this. The aim of the modifications is to do simple tasks without being forced to switch to a different language. 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 in R’s [data.frame](https://stat.ethz.ch/R-manual/R-devel/library/base/html/data.frame.html). After some searching I found Alex Mili’s [torch-dataframe](https://github.com/AlexMili/torch-dataframe) package that I decided to update to my needs. We have during the past few months been developing the package and it has now made it onto the Torch [cheat sheet](https://github.com/torch/torch7/wiki/Cheatsheet#data-formats) (partly the reason for the posting scarcity lately). This series of posts provide a short introduction to the package (version 1.5) and examples of how to implement basic networks in Torch. Continue reading →