The report of all talks during the fourth day of OxML Health 2023 where I’ve been on behalf of Appsilon..
This day talks were on:
The report of all talks during the third day of OxML Health 2023 where I’ve been on behalf of Appsilon..
This day talks were on:
The report of all talks during the second day of OxML Health 2023 where I’ve been on behalf of Appsilon..
This day talks were on:
The report of all talks during the first day of OxML Health 2023 where I’ve been on behalf of Appsilon.
This day talks were on:
Long time no see ^^ Today, I’d like to share my experience from OxML Health Summer School that I attended in person this year on behalf of Appsilon.
It’s always nice if you can use your technical skills for something good for our planet. I have this opportunity during work at Appsilon where from time to time we work on Data4Good projects. One such project was copepods/plankton project where I was doing segmentation of the oil sac in the Arctic plankton. This time I’d like to share something from the other pole of the globe.
Currently we work on detecting the Antarctic shags nests on the Antarctic islands. Together with JΔdrzej ΕwieΕΌewski and Joanna Kaleta we were cooperating with researchers from IBB PAS on this task. Checkout the engagement summary blog post on the Appsilon blog.
What if you just don’t like rmd/qmd format anymore?
You love jupyter notebooks and that’s your way of working (that’s me).
Still, you must generate report from time to time.
Fortunately quarto solves this problem brilliantly!
The reports look beautifully. Not only htmls what we might expect, but pdfs as well!
I wrote a post for Appsilon that has been published on medium regarding this topic. Be sure to check it out, it was a real game-changer for me!
Working with quarto files in RStudio is probably the suggested way.
However, you may prefer to use VS Code.
Especially if you work mainly in python.
It’s not a problem by any means!
I wrote a post on medium on working with quarto in VS Code on python files.
One thing is to train the model, but another is to serve it. In between those two phases, model serialization and deserialization occurs. In simpler words it’s just model saving and loading. It’s important because different methods result in different:
If you are curious what are the ways to serialize model in PyTorch and how they compare, checkout my new post on Appsilon blog.
I’d like to inform that the course has finished and the total of 56 people received a graduation certificate (which required passing homeworks for over 70%), congratulations! π It was a great experience and I’m happy that I was able to teach so many young students!
I’m happy to announce that I’ll create and teach the course Python LevelUP: Data Science by Appsilon in collaboration with DaftAcademy, part of DaftCode.
This will be a five-week course on the basics of Data Science in python. The course materials will be available on GitHub for free. Apart from teaching the classic stuff, I’ll try to show some of the newest and coolest libraries out there. Even if you already know most of this stuff, I really recommend to take a closer look on the unknown packages.
Some time ago, I’ve worked on an oil sac (energy reservoir) detection in copepods (kind of zooplankton) for UniversitΓ© Laval. Apart from presenting the results on the ML4Plankton conference I’ve published a detailed blog post on the Appsilon blog that I strongly encourage you to read!
As mentioned a month ago, I write a series of posts on detecting solar panels from satellite images at my company, Appsilon.
In this post I focus on using fastai python library to deliver a working ML model in few hours!
I’m writing a series of posts on detecting solar panels from satellite images at my company, Appsilon. To be honest, not from satellite images but from orthophotos. If you don’t know what are orthophotos, the good news is that the first post has been released recently!.