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