Rise of Kingdoms: Holiday Events

Rise of Kingdoms holiday events come around every few months. The last one involved collecting ornaments for the Christmas tree, and the current Valentine’s Day event requires collection of roses. Using a rose gives you points, and you need a certain number of points to complete the event. We know how many points are needed … Read moreRise of Kingdoms: Holiday Events

Rise of Kingdoms: Commander Pairings

Rise of Kingdoms (iOS, Android) has many possible primary/secondary commander pairings, some of which are amazing, while others are absolutely terrible. Tier lists for commander pairings have been discussed by many people on YouTube, but I wanted to find a way to condense the information in those lengthy videos into a more digestible form. The … Read moreRise of Kingdoms: Commander Pairings

Qualtrics: Conditional display of images

Let’s say you want to create a Qualtrics survey where you assign respondents to groups, and you want to display certain images depending on their group assignment. For example, if they’re assigned to Group 1 you want a question item to display one set of images, while a different set will be shown to Group … Read moreQualtrics: Conditional display of images

Overwatch Data Visualization

In a previous post I talked about an Overwatch dataset I’ve been collecting from my ranked games. Before running any statistical analysis on the Overwatch data, it is usually a good idea to explore and visualize the dataset. This helps us get a general sense of data patterns, which can help generate hypotheses that can … Read moreOverwatch Data Visualization

Colour image classification (CIFAR-10) using a CNN

As I mentioned in a previous post, a convolutional neural network (CNN) can be used to classify colour images in much the same way as grey scale classification. The way to achieve this is by utilizing the depth dimension of our input tensors and kernels. In this example I’ll be using the CIFAR-10 dataset, which … Read moreColour image classification (CIFAR-10) using a CNN

Visualizing Convolutional Neural Networks using nolearn

We previously talked about Convolutional Neural Networks (CNN) and how use them to recognize handwritten digits using Lasagne. While we can manually extract kernel parameters to visualize weights and activation maps (as discussed in the previous post), the nolearn package offers an easy way to visualize different elements of CNNs. nolearn is a wrapper around Lasagne … Read moreVisualizing Convolutional Neural Networks using nolearn

Handwritten digit recognition with a CNN using Lasagne

Following my overview of Convolutional Neural Networks (CNN) in a previous post, now lets build a CNN model to 1) classify images of handwritten digits, and 2) see what is learned by this type of model. Handwritten digit recognition is the ‘Hello World’ example of the CNN world. I’ll be using the MNIST database of … Read moreHandwritten digit recognition with a CNN using Lasagne

Automatic Python documentation with Sphinx autodoc and ReadTheDocs

Generating Python documentation for packages/modules can be quite time consuming, but there’s a way to generate it automatically from docstrings. This post is mostly a summary of the fantastic guide by Sam Nicholls found here, but with one important addition (see the section on mocking). We’ll be using the following: Sphinx – Python package for … Read moreAutomatic Python documentation with Sphinx autodoc and ReadTheDocs

Overview of Convolutional Neural Networks (CNN)

Regular feed-forward artificial neural networks (ANN), like the type featured below, allow us to learn higher order non-linear features, which typically results in improved prediction accuracy over smaller models like logistic regression. However, artificial neural networks have a number of problems that make them less ideal for certain types of problems. For example, imagine a … Read moreOverview of Convolutional Neural Networks (CNN)