During live broadcasting on Twitch, many streamers and esports organizations struggle to extract interesting moments from the massive stream data. Maintaining staff to find the highlights during live broadcasts is expensive for large esports organizations as well. In addition to this problem, another one arises, live broadcasts usually go on…
I have always noticed that top experts in any field are able to create awesome things much faster than usual people, not because they are smarter, but because they can iterate over their ideas much quicker. One essential thing to iterate fast is to have some code snippets and libraries…
During the OSIC Pulmonary Fibrosis Progression competition, the competitors were asked to predict a patient’s severity of the decline in lung function based on a CT scan of their lungs and some additional tabular data fields. The challenge was to use machine learning techniques to make a prediction with the…
Today we’re going to talk about the chain rule and I hope that my visual explanations of this concept will help you a lot! …
The MixUp idea was introduced back in 2018 in this paper and was immediately taken into pipelines by many ML researchers. The implementation of MixUp is really simple, but still it can bring a huge benefit to your model performance.
MixUp can be represented with this simple equation:
newImage =…
Introduction
Recently I was given the task of creating an algorithm, to extract all possible metadata from the crossword photo. This seemed like an interesting task for me, so I decided to give it a try. These are the topics that will be covered in this blogpost:
In this article I will be using Python to implement the Support Vector Machine Classifier, which is considered as one of the most complex basic machine learning algorithms. Though, the only thing which really differs from Linear Regression implementation in my code is the loss function used. …
When using simple Machine Learning algorithms, like Nearest Neighbours, on huge datasets, it often becomes pain to find good model hyperparameters or even to build a strong crossvalidation framework, because it takes model ages to finish training even if using simple train test split! One way to overcome this would…
In this article, I will provide a really short and intuitive implementation of the famous Naive Bayes algorithm. In order to understand this simple concept, understanding the meaning of the picture below is all you need :) Meet the Bayes theorem!
PCA or Principal Component Analysis is one of the best-known dimensionality reduction algorithms. Its’ main purpose is to reduce the size of the given data, keeping as much information as needed. In this article, I will shortly explain the PCA algorithm and implement it. Let’s go!
