I've implemented a process for recording the things I learn in the form of questions and answers, using the active recall technique. I document how I use the latex exam class to generate worksheets with and without the answers.
Serializing a TensorFlow model to Onnx in order to call it from Scala.
Configuring a GitHub Action to deploy updates to my blog.
Introduction to metaprogramming, with a specific example using Scala to generate a case class to describe the schema of a dataset.
The architecture I use to deploy my personal blog
Writing simple tests in Postgres without requiring extra dependencies.
Using org mode to generate documentation and infrastructure.
Literate programming with org-mode and sql
A script for upserting data from Pandas DataFrames into Postgres safely.
Using scikit-learn and some Python features to set up a machine learning pipeline to quantify the efficiency of different classifers for classifying the movement of a stock price.
I wanted to use lower level linear algebra constructs in Python to calculate a covariance matrix.
A script illustrating how the memory usage of a pandas DataFrame can be reduced through altering data types.
Some useful Python packages and how they fit into my work.
Some notes on useful SQL commands and when they might be useful.
A review of different sources on when JSONB is suitable for use.
Some of the books I have read which might be useful for Data Science.
Recovering point locations from vectors.
A review of the MIT 18.065 Spring 2018 opencourseware course, instructed by Gilbert Strang.
Some resources that I use to practice for passing coding interviews.
A review of numerical linear algebra for machine learning.
A review of mathematical concepts helpful for data science.
A review of the literature on uncertainty and sensitivity analysis when applied to optimisation models.