Writing on machine learning, engineering, and the systems behind AI products.
Tutorials, experiments, and implementation notes written to be directly useful.
Station Rules
Clear explainers
Complex topics are broken into steps, examples, and implementation notes.
Practical bias
Most posts tie theory back to code, tooling, or product decisions.
Galton-Watson Process
In this post, we outline the Galton-Watson Process.
Browse the remaining stops on the line.
Policy Iteration with Maze Example.
Check if an agent is doing the right thing with Policy Evaluation Methods
Reinforcement learning is about an agent learning to make better decisions through trial and error to maximize long-term rewards. What does that entail in practice?
In this blog I will focus on the ways in which I set up a backend and frontend to do rapid prototyping of full stack applications.
Details on how the gradient decent algorithm is implemented.
In this blog post I explain how the Maximum Likelihood Estimator is used in Machine Learning.
Get hands-on experience with deploying Large Language Models (LLMs) at scale using NVIDIA's Triton Inference Server.
In this tutorial we will look at what TF and IDF are and how they can be use to process text data in Machine learning.
In this post, we will look at the quora qna dataset and aim to encode and compare all question pairs. The purpose of is to look at a real dataset.
This tutorial will focus on the math behind text vector similarity using numpy, pytorch, and stentence-transformers libraries in python.
In this post, I outline-solid the ways in which d3 library works with the <svg> elements.