This series is a repost from A panoramic tour of Factor. I will split the original tutorial in more manageable chunks, but the content will stay mostly the same.

Simple but neat python class AppendableList is introduced and explained in this post.

Deep learning is a technique used to solve complex problems such as natural language processing and image recognition. We are now able to solve these computational problems quickly, thanks to a component called the Graphics Processing Unit (GPU). Originally used to generate high-resolution computer images at fast speeds, the GPU’s computational efficiency makes it ideal for executing deep learning algorithms. Analysis which used to take weeks can now be completed in a few days.

IP address conversion

Intro to pandas data structures: This is the first post in Greg Reda’s classic three-part pandas tutorial (part 2, part 3). It’s highly readable, presents the “right” level of detail for a pandas beginner, and includes lots of useful examples.

To better understand neural networks, I’ve decided to implement several from scratch.

This post tries to demonstrates how to approximate a sequence of vectors using a recurrent neural networks, in particular I will be using the LSTM architecture, The complete code used for this post could be found here. Most of the examples I found in the internet apply the LSTM architecture to natural language processing problems, and I couldn’t find an example where this architecture could be used to predict continuous values.

Meanshift is a clustering algorithm that assigns the datapoints to the clusters iteratively by shifting points towards the mode. The mode can be understood as the highest density of datapoints (in the region, in the context of the Meanshift). As such, it is also known as the mode-seeking algorithm. Meanshift algorithm has applications in the field of image processing and computer vision.