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A typical issue of Late to the Party 🎉 includes the following sections:
Just mix and match what you want to read!
Check out a selection past issues below 👇
How are you doing today? We’ve got some fresh, hot machine learning right from the source today!
I am deeply enjoying the long Easter weekend here, but I won’t deprive you of your weekly machine learning dose!
it’s been 2 years of this newsletter! 🥳
In these anniversary issues, I like to go through the last year of writing this weekly newsletter and see what everyone liked. So, in case you missed these awesome links, this is what everyone else enjoyed!
what an eventful week. Twitter seems to be dying slowly, and winter has eventually arrived. Let’s warm our hearts with some machine learning!
The Christmas season started! Let’s look at some merry machine learning!
Late to the party is a newsletter focused on exploring various aspects of real-world machine learning.
It includes topics like new machine learning models, courses like the scikit-learn MOOC, and Deepminds latest and greatest scientific revolution, ensuring you're equipped to build robust and effective machine learning career.
Get insights on real-world machine learning, data science, and Python every Friday from Scientist for Machine Learning at ECMWF Dr. Jesper Dramsch. 🎉
I joined Facebook, when it became trendy to delete it. I learned Python, when Javascript ate the world. I learned Tensorflow when everyone already loved Pytorch. If like me, you're usually Late To The Party, this newsletter is for you.
In this weekly newsletter, I share interesting tidbits real-world machine learning that I found around the web. Because, while often being late to the party, I love learning new stuff and I love sharing what I learn. So maybe this is something for you too.
I don't take sponsorships to sell you "links", you can read about this in my Ethics Statement
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Each issue has a little survey at the end, where you can suggest topics for future issues. You can also reply to any email directly.
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You can read more about this in my ethics statement.
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Jesper Dramsch (they/them) works at the intersection of machine learning and physical, real-world data with 8 years of experience.
Currently, they're working as a scientist for machine learning in numerical weather prediction at the coordinated organisation ECMWF.
Jesper is a fellow of the Software Sustainability Institute, creating awareness and educational resources around the reproducibility of machine learning results in applied science. Before, they have worked on applied exploratory machine learning problems, e.g. satellites and Lidar imaging on trains, and defended a PhD in machine learning for geoscience. During the PhD, Jesper wrote multiple publications and often presented at workshops and conferences, eventually holding keynote presentations on the future of machine learning in geoscience.
Moreover, they worked as consultant machine learning and Python educator in international companies and the UK government. They create educational notebooks on Kaggle applying ML to different domains, reaching rank 81 worldwide out of over 100,000 participants and their video courses on Skillshare have been watched over 128 days by over 4500 students. Recently, Jesper was invited into the Youtube Partner programme creating videos around programming, machine learning, and tech.
Jesper Dramsch is the creator of PythonDeadlin.es, ML.recipes, data-science-gui.de and the Latent Space Community.
