🐇 One of the cutest animals is the Punny Rabbit
Late to the Party 🎉 is about insights into real-world AI without the hype.
Hello internet,
happy Saturday, everyone! I’m preparing for some travel, but before lift-off, let’s look at more machine learning!
The Latest Fashion
- Check out this course on CI/CD for machine learning by Weights & Biases!
- Reddit mods are bracing for the great chatGPT spampocalypse.
- EditAnything is an online demo from segmentation to style transfer to anything
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My Current Obsession
I’ll be at the Collaborations Workshop in Manchester next week. Finally, meeting a lot of my fellow SSI fellows in person. Super exciting!
We also just had our biweekly Casual Accountability Catch-up in the Latent Space today. That was a lovely chat about data science boot camps and projects and plans.
Generally, things are looking up on my end. I’ve been really productive at work, and I have some vacation lined up, which I’m looking forward to!
Thing I Like
I brought some of my quieter fidgets to work, and it has been lovely to have something to keep my mind focused during the more cerebral activities.
Hot off the Press
I published a little edit from The Data Scientist Show I was on: who wins the deep learning Face-Off: PyTorch or Keras?
What do you think? People seem to genuinely enjoy these! I think I’ll create a couple more of these.
In Case You Missed It
It looks like my essay on the differences between the tech hiring pipeline and ECMWF is still making rounds, so here it is for you too!
Machine Learning Insights
Last week I asked, Can you name good example problem to apply machine learning to?, and here’s the gist of it:
Machine learning can be applied to a wide range of problem domains. One good example problem where machine learning can be applied is weather prediction.
Weather prediction involves forecasting future weather conditions based on historical data and various meteorological factors. By leveraging machine learning algorithms, we can analyze patterns and relationships within the data to make accurate predictions about future weather conditions.
For instance, let's consider a scenario with a dataset containing historical meteorological data such as temperature, humidity, wind speed, and atmospheric pressure. The goal is to train a machine learning model to predict whether it will rain on a given day.
We can use this dataset to build a machine learning model, such as a decision tree or a random forest, which learns the relationships between the input variables (temperature, humidity, wind speed, atmospheric pressure) and the output variable (rain or no rain). The model learns from the historical data to predict whether it will rain on a coming day based on the given input variables.
Once the model is trained, we can use it to predict future weather conditions by providing it with the values of the input variables for a specific day. The model will then generate a prediction, indicating whether it will likely rain.
Weather prediction is just one example of the many practical applications of machine learning. Other examples include:
- Fraud detection in financial transactions.
- Sentiment analysis in social media.
- Medical diagnosis based on patient data.
- Recommendation systems for personalized movie or product recommendations.
In summary, machine learning can be applied to various problem domains. Weather prediction is a good example where machine learning algorithms can analyze historical meteorological data and make accurate predictions about future weather conditions.
Data Stories
Did you grow up watching Friends, How I met your mother and the Simpsons?
Maybe the 70s show, Scrubs, or Bojack Horseman?
I found this lovely data visualization about the average rating of comedy shows!
With context these are so fun to read. Two and a half men clearly took a nosedive when Charlie Sheen lost his mind. Family Guy was always mid, but got worse. Scrubs was good until Season 9 where they changed the entire cast.
It seems I should try Parcs & Recs again and get through the first season…
Source: Reddit
Question of the Week
- What is bagging and can you name examples of its usage?
Post them on Mastodon and Tag me. I'd love to see what you come up with. Then I can include them in the next issue!
Tidbits from the Web
- Maybe don’t use Substack
- A modern take on old carriages
- Crypto: the World’s Greatest Scam
Jesper Dramsch is the creator of PythonDeadlin.es, ML.recipes, data-science-gui.de and the Latent Space Community.
I laid out my ethics including my stance on sponsorships, in case you're interested!