Statistics

Rise of Kingdoms: Saving gold keys

Here it is. The answer to the age-old question. The question that shows up at least once a day on Reddit and Discord: “should I save my gold keys and open them all at once?”. The assumption being that opening gold keys together in a batch gives more/better rewards than opening them one at a … Read moreRise of Kingdoms: Saving gold keys

Rise of Kingdoms: Show Your Love Event

In a previous post I discussed the point requirements of the last holiday event (Valentine’s Day – Pledge of Thorns). Here, I will do the same analysis with the new holiday event (Show Your Love) to confirm those findings. I’ll be drawing comparisons between the two events throughout. The supporting code and data for this … Read moreRise of Kingdoms: Show Your Love Event

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

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

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)

XOR Logic Gate – Neural Networks

We have previously discussed OR logic gates and the importance of bias units in AND gates. Here, we will introduce the XOR gate and show why logistic regression can’t model the non-linearity required for this particular problem. As always, the full code for these examples can be found in my GitHub repository here. XOR gates … Read moreXOR Logic Gate – Neural Networks