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