WEBVTT 00:00.050 --> 00:05.660 So we're now going to start week one of the course when we are going to be looking at exploring frontier 00:05.690 --> 00:10.880 models, models at the forefront of what's possible in this first day, we're going to set up an environment 00:10.880 --> 00:13.310 and then build your first LM solution. 00:13.310 --> 00:17.000 We're then going to talk about how to be positioned for success in the next few weeks. 00:17.000 --> 00:21.320 We're then going to compare models at the very frontier of what's possible. 00:21.320 --> 00:23.900 Some that you know well and some I think will be new to you. 00:23.930 --> 00:29.030 We're then going to talk about transformers, which is the architecture at the heart of the different 00:29.030 --> 00:32.210 models that we use in this course, almost all of the models. 00:32.270 --> 00:38.090 And then finally, we'll end with a juicy commercial project that you'll be able to use, and I'll have 00:38.090 --> 00:40.310 an exercise for you to make it your own. 00:40.310 --> 00:44.540 Apply it to different business areas, something that maybe you can use in your day job immediately. 00:44.540 --> 00:49.580 Maybe you'll be able to use it as a project that you can have as an example project for yourself. 00:50.240 --> 00:55.910 So we're now going to get hopefully some satisfying instant results for you. 00:55.940 --> 01:00.080 We already used a llama to do some some fun things on your computer already, but now we're going to 01:00.080 --> 01:03.890 take it a notch higher by calling out to APIs directly. 01:03.980 --> 01:11.630 But first, the necessary first step is to set up your environment, create a full spec data science 01:11.630 --> 01:16.700 environment that will have the the sufficient horsepower to take you through the entire eight week program 01:16.700 --> 01:17.450 we have ahead. 01:17.450 --> 01:22.190 And there's quite a lot of stuff that we're going to need to install to make sure we're at that point. 01:22.400 --> 01:28.580 And I guess I should mention that sometimes, sometimes this experience can be a bit frustrating. 01:28.580 --> 01:32.090 Most of the time it goes really smoothly and everything just fits in place. 01:32.090 --> 01:33.560 And it's five minutes and you're done. 01:33.560 --> 01:38.600 But sometimes some people hit speed bumps for whatever reason, because of something about the system 01:38.630 --> 01:41.720 or or some other new effect. 01:41.780 --> 01:47.720 And it can be a bit frustrating, but I am here to take you through it step by step and make sure it's 01:47.720 --> 01:50.420 successful for you so that this will go smoothly. 01:50.420 --> 01:55.280 It'll be behind you in no time, and we'll be on to actual projects, and we'll never have to worry 01:55.280 --> 01:58.250 about environment setup again for the next eight weeks. 01:58.250 --> 02:04.590 So creating the full spec data science environment, it ain't as easy as I wish it were. 02:04.950 --> 02:07.860 There is some stuff to do, but we'll get through it fast. 02:07.860 --> 02:11.910 First of all, we'll clone the repo for people who are familiar with git. 02:11.910 --> 02:16.500 That's a that's easy, but but I will make sure I walk you through it in case this is a new thing. 02:16.620 --> 02:21.930 Then we will go through the Readme instructions to set up the environment. 02:21.960 --> 02:27.180 Now I recommend that we use something called Anaconda that some of you may have already used Anaconda. 02:27.180 --> 02:30.960 It's a high powered thing and it's quite heavyweight. 02:30.960 --> 02:37.140 It creates an entire environment for you, a dedicated, isolated environment on your computer for this 02:37.140 --> 02:43.200 course, and it does so in a way that guarantees high compatibility between what you're doing on your 02:43.200 --> 02:46.590 computer and what I do on mine and everyone else on this course. 02:46.680 --> 02:48.900 So it's very good at that. 02:49.170 --> 02:50.550 But it is quite heavyweight. 02:50.550 --> 02:55.110 It involves downloading a lot from the internet and installs a lot because it builds everything from 02:55.110 --> 02:56.130 the ground up. 02:56.520 --> 02:59.100 If it causes you problems, there's an alternative. 02:59.100 --> 03:01.080 We can use a simpler approach. 03:01.080 --> 03:06.440 The standard Python virtual environment approach with Pip, and I've got instructions for that too. 03:06.470 --> 03:13.580 It's quicker, it's simpler, and it's just not as guaranteed to be compatible as Anaconda, so I'd 03:13.580 --> 03:15.740 prefer we try Anaconda if possible. 03:15.770 --> 03:17.150 It does work most of the time. 03:17.150 --> 03:18.380 Any problems at all? 03:18.380 --> 03:20.900 The Python virtual environment is your fallback. 03:20.930 --> 03:24.620 We then need to set up an OpenAI key and I'll talk more about that. 03:24.620 --> 03:28.310 I'll talk about API costs and what to do if that's a that's a problem for you. 03:28.400 --> 03:29.930 We'll go through that at the time. 03:29.930 --> 03:35.960 We have to create something called a EMV file, which has your private keys in it. 03:35.960 --> 03:40.460 And it's going to be important that that file is called exactly dot EMV, as we'll see. 03:40.460 --> 03:45.410 I will explain, but it can't be called keys dot EMV or dot EMV dot text. 03:45.440 --> 03:47.600 It has to be called exactly dot EMV. 03:48.050 --> 03:53.840 And then finally we activate your environment and we run JupyterLab to start the program. 03:54.560 --> 03:59.720 So as I say, most of the time all of this will go great. 03:59.930 --> 04:06.190 Most people report that everything goes through fine, but if it doesn't, then then there are some 04:06.190 --> 04:09.460 secret weapons at your disposal to make sure that it does. 04:09.490 --> 04:15.490 The first of them is that I've put a troubleshooting notebook that I will show you in, in the in, 04:15.520 --> 04:20.650 in the GitHub repo, which will take you step by step through diagnosing problems, running some code 04:20.650 --> 04:24.340 that will figure out what's wrong, and then fixing each problem in turn. 04:24.340 --> 04:25.900 So that is there. 04:25.930 --> 04:31.840 The second resource you have is a kind of surprising one at ChatGPT and and Claude. 04:31.870 --> 04:34.840 They are spooky in how good they are. 04:34.870 --> 04:42.670 If you say a problem and paste like a stack trace in there, they are very, uh, impressive at giving 04:42.670 --> 04:45.310 quite nuanced answers about what you need to do. 04:45.340 --> 04:48.970 I've been caught off guard by the times I've run into an infrastructure problem, and I thought, this 04:48.970 --> 04:49.870 is way too much. 04:49.870 --> 04:52.300 I can't even get the answer on Stack Overflow. 04:52.480 --> 04:55.570 Uh, but I've been rescued by Claude. 04:55.660 --> 04:57.400 I think Claude is the stronger of the two. 04:57.430 --> 04:57.640 But. 04:57.640 --> 04:59.440 But both of them give good answers. 04:59.440 --> 05:04.990 So it's worth realizing that you can often just fix the problem by pasting it in there. 05:05.020 --> 05:06.670 It's worth giving it a shot. 05:06.730 --> 05:12.140 But if if if those two fail, or even if you'd just rather go straight to this third option. 05:12.140 --> 05:15.770 The third option is there, and the third option is to contact me. 05:15.890 --> 05:17.780 I am standing by to help you. 05:17.780 --> 05:19.070 That is my job. 05:19.130 --> 05:20.990 So please, please reach out. 05:20.990 --> 05:23.900 You can reach out to me by messaging me on the platform. 05:23.900 --> 05:25.130 You can email me. 05:25.160 --> 05:28.700 My email address is in the is in the readme and you can also linked in with me. 05:28.700 --> 05:30.500 I love getting LinkedIn connections by the way. 05:30.560 --> 05:36.650 So LinkedIn with me for sure and message me there too and you'll find that I'm very responsive. 05:36.740 --> 05:41.900 I typically if I'm not sleeping or traveling, then you'll see from my other answers that I get back 05:41.990 --> 05:47.210 very quickly, and I see it as my job to fix it and to get you up and running. 05:47.210 --> 05:48.320 And I have never failed. 05:48.320 --> 05:55.340 Yet I have always, I think, 5000 people through and everyone has a running environment, as will you. 05:55.340 --> 05:59.570 So if you do hit any speed bumps at all, you can go with these other two options. 05:59.570 --> 06:00.860 But you can come straight to me. 06:00.860 --> 06:01.670 I will fix it. 06:01.670 --> 06:03.200 I will find what's going on. 06:03.200 --> 06:05.690 We will have you up and running quickly. 06:05.840 --> 06:06.680 All right. 06:06.680 --> 06:11.630 With that, let me now take you on a walkthrough of what it takes to set up your environment.