I recently created a little notebook that describes popular supervised learning algorithms. It can be used as a little cheat sheet when it comes to remembering what these algorithms do. I embedded the notebook here. If you want to fullscreen version then head over to GitHub and open the Gist.
Not long ago Kaggle got the new dataset feature. Every member of the community can now upload their own datasets for others to play with. This is a very cool thing and there are lots of interesting datasets out there. You can also use Kaggle to promote your dataset. I was thinking about a dataset that I could provide and when I was reading through the LiveFromNewYork subreddit I got the idea: what about a Saturday Night Live dataset? I searched around the web and found the website snlarchives.net which has a very comprehensive database. I contacted the creator but got no answer. But I didn’t want to stop my project before it really began so I decided to try to scrape the data from the website. This blog post shows you how I did that and what we can learn from over 40 seasons of hilarious data.
xgboost is a very popular machine learning library and widely used (for example on kaggle). However, contrary to a lot of other popular libraries it can not be easily installed with conda or pip. You have to compile the library first. The installation guide can be found here. This blog post is a short version of the installation guide and mainly targeted for myself for reference.
Yesterday a very fun competition over at kaggle.com finished: Goblins, Ghosts and Ghouls was this years halloween competition. It was a competition targeted at beginners and therefor right up my alley. The task was to classify three types of monsters: goblins, ghosts and ghouls. In this blog post I will talk about how I went about predicting the type of monsters.
While working through the Google YouTube series on machine learning I watched episode six Train an Image Classifier with Tensorflow for Poets. Since I create notebooks for every episode I did this here, too. The following text is taken from this notebook and it is a short tutorial on how to implement an image classifier with tensorflow in a very short amount of time. You can transfer this knowledge to every other image classification task without much effort. You can find the original python notebook here and my other notebooks on my iPython Notebook page.
I just added a new page called iPython Notebooks. You can find a collection of my personal iPython Notebooks there. These notebooks are a very cool tool of the python community that makes it easy to share your code. On top of that they also provide inline graphs and formated text.