This repo contains a series of notebooks on various topics I find interesting where a notebook is a great way to explain them. Initially I'll focus on different versions of applied linear algebra, mainly deep learning focused. Later on I'll visit other areas such as decomposition, dimension reduction, and who knows what else. If it becomes too long I'll end up adding sections, but that's a future problem for me.

These notebooks originated from my general notebooks repo (now private). That repo ended up with a bunch of crap and I wanted a cleaned up version of it. For the notebooks I generally focus on problems where I find myself wanting to see intuitively what is happening so that I can improve my understanding of it.

There may be errors and issues in the notebooks. I more focus on them running and update any issues I see. If you see an issue, message me or submit a bug/PR. The goal is to learn, not to be perfect

Each notebook is special. Since they're mainly written for myself they go deeper in areas I was less familiar with at the time of writing. That said, you should probably know some basic python, linear algebra, and biology to not get blown away by these notebooks.

• Transform Deep Dive with GPT components
• Convolutional neural network (CNN) with ResNet components
• Recurrent Neural Networks (RNN) with Gated Recurrent Unit (GRU)
• Recurrent Neural Networks (RNN) with Long Short-Term Memory (LSTM)
• Graph Neural Network (GNN) with Graph Convolutional Network (GCN)
• Graph Neural Network (GNN) with Graph Attention Network (GAT)

If you want to view the notebooks without downloading them, you can just click the links here in the README, or use the Jupyter nbviewer website, where you can just paste the URL to the notebook and it should render correctly.