Categories
code recommender

Variational Autoencoders for Recommendation

We can turn a simple autoencoder into something sophisticated. Autoencoders discover a latent mapping z, which as a lower-dimensional representation of the input x, can be useful for pre-training networks and creating recommender models. However, how autoencoders compress information may come in millions of ways. To truly polish how z can be generated, we turn […]

Categories
general opinion

The Best Candidate for Our Land

Opinion piece on Leni, and the Philippine elections

Categories
general reactions

A medically themed reflection on the future of AI

Medicine is quite different from my profession but I think having this kind of interest is healthy (get it?). Eventually, I came to reach some parallels between my discipline and the medical sciences.

Categories
code deep learning recommender

Collaborative Denoising Autoencoders on Steam Games

Autoencoders are a simple neural network approach to recommendation

Categories
code computer vision deep learning

Studying Variational Autoencoders

Variational autoencoders can discover unique ways to encode-decode the input from a distribution. Bonus: It can generate images!

Categories
code computer vision deep learning

Classifying Paintings through Deep Learning

Back in college, I did a class project where I used computer vision techniques for the first time. It was the age before deep learning. Today, I want to revisit this old project.

Categories
code data engineering recommender

Deploying a Recommendation System the Kedro Way

A tutorial to create a recommender pipeline with Kedro and MLFlow.

Categories
computer vision

My Heritage In Color

An uploaded family photo inspired me to pick up a colorizer tool. Here is my post honoring my family’s history.

Categories
code data engineering general

Level Up Your MLOps Journey with Kedro

MLOps is all about creating sustainability in machine learning. To maintain structure in this fast-paced field, you can try out Kedro, an open-source Python project that aims to help ML practitioners create modular, maintainable, and reproducible pipelines.

Categories
kaggle recommender

R-Recommender Roadtrip

This is part 2 of my talk on recommenders. The presentation describes the intuitions of matrix factorization and how to implement it in R. I delivered this in the R-Users Group PH meetup group this November. If you wish to see some more recommender material in this blog, check this out: