This article is a collection of Torch examples meant as introduction to get started with Lua and Torch for deep learning research. The examples can also be considered individually and cover common use cases such as training on CPU and GPU, weight initialization and visualization, custom modules and criteria as well as saving and fine-tuning models.
In this series, I collect problems I come across when using Ubuntu for research and development. In this article: installing Bazel on Ubuntu and masking graphics cards from being considered by Tensorflow.
In this article, I discuss a simple Tensorflow operation implemented in C++. While the example mostly builds upon the official documentation, it includes trainable parameters and the gradient computation is implemented in C++, as well. As such, the example is slightly more complex compared to the simple
ZeroOut operation discussed in the documentation.
Sphinx is a Python documentation tool that allows to automatically create clear documentation by parsing Python docstrings. The documentation can further be complemented using reStructuredText — a markup language similar to Markdown. This article gives a brief overview of setting up Sphinx on Ubuntu.
This article presents an implementation of Felzenszwalb and Huttenlocher’s  graph-based image segmentation algorithm. The implementation is compared to the original implementation by Felzenszwalb in terms of Boundary Recall, Undersegmentation Error and Explained Variation, as used for evaluating superpixel algorithms. In addition, qualitative results are provided. The implementation is publicly available on GitHub.
While using Twitter Bootstrap for a web application I am working on, I came across the (still) very ugly default file upload form element. After doing some research and setting up a demo, I wrote a tiny plugin for simple but pretty, cross-browser (including Internet Explorer 7,8 and 9) file upload form elements with Twitter Bootstrap.
In the course of the last couple of semesters, I extended the initial comparison of superpixel algorithms in my bachelor thesis to a comprehensive comparison of 28 state-of-the-art algorithms on 5 datasets with regard to quantitative and qualitative performance. The results are now available on ArXiv.