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WEBVTT
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So we're now going to start week one of the course when we are going to be looking at exploring frontier
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models, models at the forefront of what's possible in this first day, we're going to set up an environment
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and then build your first LM solution.
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We're then going to talk about how to be positioned for success in the next few weeks.
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We're then going to compare models at the very frontier of what's possible.
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Some that you know well and some I think will be new to you.
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We're then going to talk about transformers, which is the architecture at the heart of the different
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models that we use in this course, almost all of the models.
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And then finally, we'll end with a juicy commercial project that you'll be able to use, and I'll have
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an exercise for you to make it your own.
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Apply it to different business areas, something that maybe you can use in your day job immediately.
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Maybe you'll be able to use it as a project that you can have as an example project for yourself.
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So we're now going to get hopefully some satisfying instant results for you.
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We already used a llama to do some some fun things on your computer already, but now we're going to
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take it a notch higher by calling out to APIs directly.
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But first, the necessary first step is to set up your environment, create a full spec data science
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environment that will have the the sufficient horsepower to take you through the entire eight week program
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we have ahead.
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And there's quite a lot of stuff that we're going to need to install to make sure we're at that point.
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And I guess I should mention that sometimes, sometimes this experience can be a bit frustrating.
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Most of the time it goes really smoothly and everything just fits in place.
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And it's five minutes and you're done.
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But sometimes some people hit speed bumps for whatever reason, because of something about the system
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or or some other new effect.
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And it can be a bit frustrating, but I am here to take you through it step by step and make sure it's
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successful for you so that this will go smoothly.
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It'll be behind you in no time, and we'll be on to actual projects, and we'll never have to worry
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about environment setup again for the next eight weeks.
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So creating the full spec data science environment, it ain't as easy as I wish it were.
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There is some stuff to do, but we'll get through it fast.
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First of all, we'll clone the repo for people who are familiar with git.
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That's a that's easy, but but I will make sure I walk you through it in case this is a new thing.
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Then we will go through the Readme instructions to set up the environment.
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Now I recommend that we use something called Anaconda that some of you may have already used Anaconda.
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It's a high powered thing and it's quite heavyweight.
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It creates an entire environment for you, a dedicated, isolated environment on your computer for this
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course, and it does so in a way that guarantees high compatibility between what you're doing on your
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computer and what I do on mine and everyone else on this course.
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So it's very good at that.
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But it is quite heavyweight.
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It involves downloading a lot from the internet and installs a lot because it builds everything from
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the ground up.
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If it causes you problems, there's an alternative.
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We can use a simpler approach.
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The standard Python virtual environment approach with Pip, and I've got instructions for that too.
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It's quicker, it's simpler, and it's just not as guaranteed to be compatible as Anaconda, so I'd
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prefer we try Anaconda if possible.
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It does work most of the time.
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Any problems at all?
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The Python virtual environment is your fallback.
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We then need to set up an OpenAI key and I'll talk more about that.
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I'll talk about API costs and what to do if that's a that's a problem for you.
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We'll go through that at the time.
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We have to create something called a EMV file, which has your private keys in it.
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And it's going to be important that that file is called exactly dot EMV, as we'll see.
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I will explain, but it can't be called keys dot EMV or dot EMV dot text.
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It has to be called exactly dot EMV.
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And then finally we activate your environment and we run JupyterLab to start the program.
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So as I say, most of the time all of this will go great.
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Most people report that everything goes through fine, but if it doesn't, then then there are some
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secret weapons at your disposal to make sure that it does.
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The first of them is that I've put a troubleshooting notebook that I will show you in, in the in,
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in the GitHub repo, which will take you step by step through diagnosing problems, running some code
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that will figure out what's wrong, and then fixing each problem in turn.
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So that is there.
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The second resource you have is a kind of surprising one at ChatGPT and and Claude.
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They are spooky in how good they are.
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If you say a problem and paste like a stack trace in there, they are very, uh, impressive at giving
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quite nuanced answers about what you need to do.
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I've been caught off guard by the times I've run into an infrastructure problem, and I thought, this
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is way too much.
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I can't even get the answer on Stack Overflow.
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Uh, but I've been rescued by Claude.
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I think Claude is the stronger of the two.
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But.
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But both of them give good answers.
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So it's worth realizing that you can often just fix the problem by pasting it in there.
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It's worth giving it a shot.
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But if if if those two fail, or even if you'd just rather go straight to this third option.
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The third option is there, and the third option is to contact me.
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I am standing by to help you.
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That is my job.
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So please, please reach out.
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You can reach out to me by messaging me on the platform.
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You can email me.
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My email address is in the is in the readme and you can also linked in with me.
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I love getting LinkedIn connections by the way.
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So LinkedIn with me for sure and message me there too and you'll find that I'm very responsive.
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I typically if I'm not sleeping or traveling, then you'll see from my other answers that I get back
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very quickly, and I see it as my job to fix it and to get you up and running.
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And I have never failed.
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Yet I have always, I think, 5000 people through and everyone has a running environment, as will you.
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So if you do hit any speed bumps at all, you can go with these other two options.
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But you can come straight to me.
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I will fix it.
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I will find what's going on.
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We will have you up and running quickly.
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All right.
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With that, let me now take you on a walkthrough of what it takes to set up your environment.