Okay, so that was your introduction to Hugging Face.
And now I'm going to turn to a different resource available which is Google Colab.
There are a bunch of different alternatives to Google Colab that all do much the same thing, and you
can really use any of them.
I like Colab in particular for a couple of reasons.
One of them is that so many people use it.
And another is it's so easy to share, but let's just talk about what it is.
So Google Colab, um, it's a it's a few things.
But the reason, the main thing that it is, and what we're going to do with it, is the ability to
run a Jupyter notebook like the ones we've been using, and run it in the cloud on a Google box, which
will have not only a decent CPU, but also a GPU that might be high spec, uh, and uh, in addition
to that, the thing that I like about it is that you can share and collaborate your Jupyter notebook
with others using the same kind of familiar interface.
You can use to share other types of Google Doc.
So if, like me, you're very used to using Google Docs and Google Sheets and the like and sharing them
and editing them and so on, then it's a very familiar experience to be able to share and collaborate
on a Jupyter notebook running in Colab.
Uh, and it's also integrated with other Google services.
So for example, you can very easily access your own Google Drive if you have data there or something
like that.
So it's it's nicely part of the Google ecosystem.
But as I say, there are a bunch of other offerings.
And you can if you if you're using a something that is a competitor to Google Colab and you like it,
then by all means use it.
Uh, you may have to, uh, copy across the colab that I'll be using in sharing, but otherwise everything
should work just fine.
When you're using Colab, you get to choose what runtimes you're working with, what kind of box it's,
what kind of VM is essentially, uh, running.
There are CPU based boxes which don't have a GPU, are just CPUs.
There is, uh, there are lower spec boxes running cheaper GPUs, and then there's higher spec, beefier
boxes for resource intensive stuff.
Everything that we do in this course can run on on up to number two, the lower spec GPU runtimes.
I'm going to be trying my absolute best to keep it so that you can do everything and not spend anything,
any material amount of money.
Um, perhaps at this point, if you, if we if you go as far as training a full deep neural network
yourself, we might be starting to talk about, uh, a few dollars, but nothing that's going to break
the bank, I hope, uh, unless you wish to take it a step further and train faster, do more experimenting.
In which case, you certainly have the ability to opt for number three and spend a little bit more.
Uh, and again, we're talking about maybe spending $10 to get a decent, like, a day or two's worth
of work, uh, against a top end GPU box.
So without further ado, that's a quick intro.
Let's go in and take a look at Colab and get comfortable with it.