I had the opportunity to again host a Kaggle-like competition. Here is how I would have solved my own problem and how user’s preferences apply in e-commerce.
Category: code
Join me in this roadtrip of different recommender algorithms.
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You Might Like… Why?
Often, recommendations are printed as “You Might Like X because you watched Y.” This connection grounds the user from a prior experience and encourages her to consume the recommended content.
DOST organized a summer school tackling AI, machine learning and data science for researchers, teachers, graduate students and industry practitioners. I had the privilege of teaching the first module, Python programming.
Hey all, it’s time for another machine learning blog. This time, I’m tackling recommendation systems. I’ve found this neat dataset, Steam Video games over at Kaggle. You should definitely check it out. We’ll be going from analyzing how people play these games, to how we can infer ratings from usage patterns. I’ll be implementing my […]
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Word Embeddings and Keras
Hi! This is my first attempt to write a Jupyter notebook to wordpress. Jupyter notebooks are an increasingly popular way to combine code, results and content to one viewable platform. Notebooks use ‘kernels’ as interpreters to scripted languages. So far, I’ve seen Python, Julia and R kernels here. (The full notebook is over at GitHub. […]
These are my materials for Deep Dive sessions to ML using popular Python libraries.
Here’s a post from one of my Kaggle competitions, the State Farm Distracted Driver Detection challenge. It’s a very interesting challenge, one that pits computer vision and foolish drivers who even had the guts to text on the phone while driving. Well, technically, these drivers aren’t really doing anything risky. These are artificial images made […]
It is the REAL DYL 