WEBVTT 00:01.010 --> 00:03.500 Okay, so that was your introduction to Hugging Face. 00:03.500 --> 00:10.010 And now I'm going to turn to a different resource available which is Google Colab. 00:10.040 --> 00:13.880 There are a bunch of different alternatives to Google Colab that all do much the same thing, and you 00:13.880 --> 00:15.470 can really use any of them. 00:15.560 --> 00:18.080 I like Colab in particular for a couple of reasons. 00:18.170 --> 00:19.910 One of them is that so many people use it. 00:20.330 --> 00:24.770 And another is it's so easy to share, but let's just talk about what it is. 00:24.800 --> 00:28.400 So Google Colab, um, it's a it's a few things. 00:28.400 --> 00:33.440 But the reason, the main thing that it is, and what we're going to do with it, is the ability to 00:33.440 --> 00:41.480 run a Jupyter notebook like the ones we've been using, and run it in the cloud on a Google box, which 00:41.480 --> 00:50.120 will have not only a decent CPU, but also a GPU that might be high spec, uh, and uh, in addition 00:50.120 --> 00:55.670 to that, the thing that I like about it is that you can share and collaborate your Jupyter notebook 00:55.670 --> 00:59.270 with others using the same kind of familiar interface. 00:59.270 --> 01:01.940 You can use to share other types of Google Doc. 01:01.940 --> 01:08.260 So if, like me, you're very used to using Google Docs and Google Sheets and the like and sharing them 01:08.260 --> 01:13.750 and editing them and so on, then it's a very familiar experience to be able to share and collaborate 01:13.750 --> 01:17.920 on a Jupyter notebook running in Colab. 01:18.280 --> 01:21.130 Uh, and it's also integrated with other Google services. 01:21.130 --> 01:25.990 So for example, you can very easily access your own Google Drive if you have data there or something 01:25.990 --> 01:26.620 like that. 01:26.620 --> 01:29.650 So it's it's nicely part of the Google ecosystem. 01:29.650 --> 01:33.130 But as I say, there are a bunch of other offerings. 01:33.190 --> 01:38.650 And you can if you if you're using a something that is a competitor to Google Colab and you like it, 01:38.650 --> 01:40.300 then by all means use it. 01:40.300 --> 01:47.080 Uh, you may have to, uh, copy across the colab that I'll be using in sharing, but otherwise everything 01:47.110 --> 01:48.730 should work just fine. 01:49.120 --> 01:55.420 When you're using Colab, you get to choose what runtimes you're working with, what kind of box it's, 01:55.450 --> 01:58.330 what kind of VM is essentially, uh, running. 01:58.330 --> 02:04.240 There are CPU based boxes which don't have a GPU, are just CPUs. 02:04.240 --> 02:12.080 There is, uh, there are lower spec boxes running cheaper GPUs, and then there's higher spec, beefier 02:12.110 --> 02:15.740 boxes for resource intensive stuff. 02:16.190 --> 02:23.900 Everything that we do in this course can run on on up to number two, the lower spec GPU runtimes. 02:23.900 --> 02:29.900 I'm going to be trying my absolute best to keep it so that you can do everything and not spend anything, 02:29.900 --> 02:31.700 any material amount of money. 02:31.820 --> 02:37.220 Um, perhaps at this point, if you, if we if you go as far as training a full deep neural network 02:37.250 --> 02:43.400 yourself, we might be starting to talk about, uh, a few dollars, but nothing that's going to break 02:43.430 --> 02:50.270 the bank, I hope, uh, unless you wish to take it a step further and train faster, do more experimenting. 02:50.300 --> 02:56.600 In which case, you certainly have the ability to opt for number three and spend a little bit more. 02:56.600 --> 03:02.270 Uh, and again, we're talking about maybe spending $10 to get a decent, like, a day or two's worth 03:02.270 --> 03:07.310 of work, uh, against a top end GPU box. 03:07.790 --> 03:11.690 So without further ado, that's a quick intro. 03:11.690 --> 03:16.400 Let's go in and take a look at Colab and get comfortable with it.