Blog

Coding has become a very accessible commodity. In software engineering, code has become the cheapest output. This is due to LLMs being very good at producing structured text. And code is structured text. It s very well structured in fact. It follows type definitions, grouping, clear hierarchies. But does that mean we don’t need software engineers? Well we do. Firstly, coding and programming are different. I’ve briefly explained this in my previous post. Secondly, frontier models are very expensive to run in terms of energy. The models we use are largely subsidised. So, it will gradually become infeasible to access such models current prices and at current volumes if they raise the prices. ...

Deep Neural Networks are usually robust to hyper parameter choices. The training will be reasonably good with various reasonable combinations of hyper parameters. However, Generative Adversarial Neural Networks(GANN) do not follow this pattern. GANNs are used to generate synthetic data, such as real looking fake images. I won’t go deep into what a GANN is right now. I’ll provide some practical facts. Training such networks require specific hyper parameters and architectural choices. ...

I was reading up on Graph Neural Networks(GNN) recently. The way researchers formulated the GNN architectures is really interesting. The easiest way to understand GNNs is to compare it with CNN. Consequently, there is an architecture called GCNN or Graph Convolutional Neural Networks. GNNs and CNNs share some common motivation like, using shared parameters. Without shared parameters, CNN and GNNs both would need infeasible amount of parameters. CNNs solve this problem by exploiting the local spatial correlation of images. GNNs solve this problem using a node’s adjacency matrix. ...

I have wanted to write publicly for a long time. I have kept notes, half-finished essays, and ideas scattered across too many apps for years. This is the push I needed. What This Blog Is Three things, roughly: 1. Technical writing — Notes from my work in machine learning. When I figure something out that took me longer than it should have, I will write it down here. Paper summaries, implementation notes, things that tripped me up. Mostly for myself, but publicly, so maybe it helps someone else. ...