Richard Li

Using deep learning to analyze data

I’ve been hacking away at various deep learning models for the past few months. Here are my notes, starting with the basics.

Deep Learning and Analysis

Deep learning usually means “machine learning but with neural networks.” I’ve been focused on analyzing large amounts of sequential data (e.g., time series data). The word “analysis” does a lot of work in the preceding sentence. When people say they want to “analyze” data, they usually mean at least one of the following:

The Analysis Ecosystem

There are lots of libraries to do lots of different things in ML. When we’re looking at the above analysis problems, I’ve found the following.

Python Packaging

Packaging is a hard problem, and Python seems to suffer more than most from the challenges of good packaging. I’ve historically been a virtualenv kind of developer. However, many of the wheels that I use are not easily available, so I’ve ended up using conda instead. YMMV. (Note that I have an M2, which doesn’t help things.)


I had previously written about LLMs. Here are some more things that I’ve found useful since then. Note that my focus area has moved beyond LLMs, so there isn’t anything LLM-related here.

Development Notes

If you’re trying to build your own NN, I’d start simple and then layer in complexity (duh). I started here with Neural Regression Using PyTorch: Defining a Network, which was very well written and the best of the tutorials that I explored.

These days, besides conda, I rely heavily on Jupyter Notebooks running in VSCode for exploring different models.

More fundamentals

I’ve found that while you can treat ML models as a black box, it’s super-helpful to have some intuition of how they work when you inevitably run into problems.