In my previous article Visualizing Optimization Trajectory of Neural Nets, I showed a number of animated figures to demonstrate the training process of neural networks. Since then some readers expressed interest in the code, so I ported it to a Python package and published it on PyPI. …

The handiest visual note of PCA — Nowadays, the knowledge depth of data scientists and ML engineers is often judged by their regurgitation of the math and implementation details of an algorithm. I disagree with this approach. The reality is, nobody can remember every detail, and that’s okay. You don’t need to know how to implement an…

How to get started building Alexa skills for things you are too lazy to search online — I moved to New York City from California two years ago. Having been spoiled by the Californian sunshine and nice weather for several years, I forgot how it’s like living in a city with a lot of precipitation. Sometimes I feel the rain never stops. No matter what season it…

The road to end-to-end data scientist/machine learning engineer — Earlier this year I came across a viral Twitter thread by Randall Kanna about how to create one’s own computer science degree with free online content. It was not only excellent for people who don’t have prior knowledge in computer science, it’s also valuable for new software engineers who didn’t…

A quick guide to fractals and the Space Colonization algorithm — In this article, I’m going to code up some special kind of squiggly shapes that might not help us colonize the galaxy any time soon, but probably will bend your mind and make math fun again. The subject is called fractals. What are Fractals? Before answering this question, let’s first look at some…

A short probability and algorithm tutorial that’s worth your while — When you want some values from a certain probability distribution, say, a normal distribution, you could simply call rnorm in R, or numpy.random.normal in Python. But have you ever wondered how they do it under the hood? The underlying idea is incredibly simple yet powerful. In this article, I'm going…

By implementing your own deep learning framework in Python — As the beating heart of deep learning, a solid understanding of backpropagation is required for any deep learning practitioner. Although there are a lot of good resources that explain backpropagation on the internet already, most of them explain from very different angles and each is good for a certain type…

Update: I have ported the code to a Python package here. Feel free to experiment and produce similar plots like the ones in this post! In the previous post, I showed some animated plots for the training process of linear regression and logistic regression. Developing a good “feel” of how…

This is one post in a series for machine learning optimization animations. Each plot can serve as a flashcard for easy consumption. If you are like me, you may prefer looking at pictures that move to pages of Greek symbols when it comes to learning math. It’s more intuitive, more…