I was lucky enough to be part of a few book projects. This is where I keep them.
I wrote a small ebook about applying validation techniques to different types of real-world datasets. Going into short examples how different data types have to be treated to avoid overfitting.
I touch on the topics of:
I made it complementary with a newsletter subscription to my weekly machine learning newsletter. Subscribe to receive weekly insights from Late to the Party on machine learning, data science, and Python.
I was invited to write a review about the history of machine learning for Advances in Geophysics. I reviewed the historical co-development of machine learning and geoscientific techniques. A particular focus is laid on Gaussian Processes (Kriging) and Neural Networks.
I read and analyzed over 300 papers for this work, featuring a selection throughout the manuscript.
Modern applications are illustrated with reproducible code examples.
This discussion of modern deep learning also ventures into specific components including:
Including significant architectures that developed and were applied in this time. The review does not yet include the transformer architecture as it was barely used in geoscience (and the chapter got long enough as is).
There is a preprint version and the typeset version available.
This is my PhD thesis. Does it count as a book?
It's 300 pages long and took a bunch of work.
So let's say maybe.
All the code is open source and it's fully published as a website, where you can also find the PDF version and the video of my PhD defense.
This feels like it was ages ago. I wrote a blog post on the ethics of working in oil and gas.
That was made into a small book chapter.
I'm not sure how strongly I hold these beliefs today, seeing the negative impact of corporations on our climate.
You can pick up a copy, but the other essays are probably more entertaining.