Thisis a light-hearted overview of what's been going on in the world of Data Science this week. See it as your 5-minute update such that you cansound at least slightly knowledgeable at your next coffee chat ☕
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I'm not sure what it's like in tech companies outside of Norway, but here we have a tendency to have "kick-offs" before seemingly every change in season. ☀️️🍂❄️🌱
These kick-offs involve gathering everyone physically in the same venue for a few days to align™️ on company strategy, have socials, eat cold pizza, and afterwards be released to work on our projects for the next few months.
This got me thinking... 💡
Why do we insist on "kicking things off"?
I don't know about you, but I don't tend to kick my software. Why not "switch on" or "compile"? 🤔
I will leave that thought with you as I segway into the news portion of this letter...
The tools that will make your life that little bit easier, or at least more interesting... but either way it's fun to play with new toys.
Dealing with outliers is one of the key steps when developing a machine learning model. This tool helps you with that by providing algorithms for outlier, adversarial and drift detection.
Having a feature store is a great time-saver when scaling machine learning teams. This is where Feathr could come in handy. It is in-fact the feature store used at LinkedIn, which has recently been open-sourced for all to use.
ipyflow is a Python kernel for Jupyter, and other notebook interfaces, that tracks dataflow relationships between symbols and cells during a given interactive session.
🧑🔬 In practice
Stories of those who are genuinely implementing Data Science. Step aside Titanic dataset, this is the real deal
Seeing machine learning mix with the creative arts is fascinating, and there are not many places better at doing this than Netflix. In this post, they outline how they use machine learning to find smooth visual transitions in their content.
🐦 The best of Data Twitter
Data Twitter is the best Twitter.
TensorFlow & Keras usage is at an all-time high, at >2.5M users. It has increased ~30% yoy.
In the largest developer survey in the world last year (60k respondents), 13% of *all devs* said they used TensorFlow. That's 1.5x more devs than PyTorch.
So I said,
So I said, what if we train the model the test set?
Content to inspire, or at the very least keep you informed.
I've been talking about Polars a lot recently, so I'll let someone else do it for me instead this time:
Could the market beat the best machine learning models when it comes to predicting stock prices? Apparently so:
This is an interesting take on looking at how an algorithm solves a problem in order to learn from it:
Did you know that your favourite Python packages actually get updated regularly and you should update your