This is the first post on the Machine Learning Notebook (MLN)! I’ll give a brief overview of what the site is for, how I’m constructing it and the kinds of things I’m learning along the way.
I’m a n00b when it comes to Github Pages and relatively new to working with Github at all! It took me a while to decide what platform to use for creating this humble resource. I was initially looking at writing everything in iPython notebooks, but it was confusing to figure out how to keep the scripts live rather than coverting them to static sites.
I’ve read several blogs, but this one seemed to be written to my liking and was pretty much a document of trail and error, but perhaps more imporantly, solutions. I’m actually writing this first post after reading this where I’ve settled on using Jekyll. Though I must say that I was initially pushed away from this idea after seeing that Jekyll was written for Ruby (in which I have no experience). But here it goes - hopefully it’ll be straight-forward.
Edit: Scratch that - after playing around with Hugo and reading the ‘start from scratch’ post here I think I’m a hugo-convert!
I’m (still) going to have to play about with writing in markdown just to get used to the command structure. Luckily, I found a good cheatsheet over here and another here.
Eventually, I want this site to build up into small sampling of what I’ve been learning to do whilst learning Python and applying it to Machine Learning problems. It may at times become quite specialised with my research in medical imaging, but the general processes (I hope) should benefit at least one other person in the world.
Here goes!
Edit: Just to test out the code-block highlighting…
def hello_world():
print “Hello there!”