A potpourri of ML experiments and notes. The experiments are (mostly) correct, but often hacky. The notes are (mostly) factual, but often opinionated.
What? Answers to a few questions from the ML Interview Book by Chip Huyen.
Why? Revisiting a few old ML concepts to gauge interview preparedness.
**What? Answers to a few algorithmic programming questions from Grind75 and common Coding Patterns. Why? Practice to do well on the programming component of MLE interviews.
What? Solve a few public Kaggle competitions.
Why? Understanding how real-world data science competitions work.
What? Prototype a few ideas with langchain to understand how LLM-powered applications are built.
Why? Knowing how to build LLM-powered applications is the new "Naive Bayes spam-ham detector" for MLEs.