Machine Learning Study Goals – September 2022


By now, this is turning into a monthly tradition on this blog: I’m sharing my goals for improving at Machine Learning with you and at the end of the month I update you on how I did to keep myself accountable.

This one looks quite ambitious, but I have some free brain capacity due to time off from work – at least that’s the idea… Let’s get into my lofty goals:

Goal 1: September’s Playground Kaggle Challenge (Time series prediction :o)

If you’ve read my previous two goal posts (July & August), this will not be much of a surprise. I enjoy doing these simulated challenges a lot.

As always my “deliverables” will be

  • submit two different predictions myself no matter how bad
  • read the code submission of two other Kagglers and learn something from them

This month’s challenge is a time series prediction, which is a bit of weak spot for me, so I’m happy (and nervous) to try my hand at this and improve.

Goal 2: First steps towards Transformers

Since I need to use some model for the time series challenge above, I might as well use neural networks for this (secret passion of mine) and try my hand at a transformer architecture for the first time.

I found a tutorial for transformers for time series prediction, so the goal is to make this code work for the Kaggle challenge – wish me luck please.

Goal 3: Attention is all you need Paper

Building on top of goal nr. 2, I want to read the “Attention is all you need” research paper that introduced the transformer architecture back in 2017.

I will probably not understand half of it, but I’ll share my insights regardless.

Goal 4: Read Storytelling with Data Chapter 2

Since I have 3 weeks off in September and am away from my computer, I want to do some reading of books I started ages ago and never finished. First up is “Storytelling with Data” which focuses all on presenting the insights we generate as data scientists in a way that is ideal for the respective audience.

I shared my thoughts of the first chapter here: How to present data in context – Chapter 1 Summary Storytelling with Data

Goal 5: Read Hands-On Machine Learning Chapter 2

More reading. This book is a great beginner book, but it does include some nice tricks with sci-kit learn that I never used in university because we implemented a lot from scratch.

So this will be easy reading for me probably, but will be useful long term.

Update coming after the month

As always, I will update in the beginning of next month of how I did – place your bets in the comment section 😉

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