Hi and welcome to this birth, cause I'm super glad that you decided to join me on this adventure because
for me, but it is super exciting algorithm and not only for me, for the whole community.
And it changed a lot of thinking recently about how we do an LP.
It's super powerful.
It's super interesting.
And I'm really glad to be able to share it with you, to teach you how to use it, how it works and
everything that goes with it before everything else is.
Just quickly for a minute or two to talk about l'alpe in a more general way before introducing birth
and then going quickly into the plan of attack for this whole course.
So L.P stands, as you may know, for natural language processing.
So it means that it's the part of A.I. that deals with human language.
So it's pretty much everywhere, actually.
For instance, in your on the Web, on your search engines, of course, it uses NLB to optimize the
results of the recent vocal assistance like Siri or of course, they use and to understand what what
we say to make something understandable for a computer.
It's also used in your email inbox, for instance, for spam detection.
It's used in the translators that we can use everyday and that are widely used everywhere.
Of course, chat bots are based on L.P, so it's used in a wide variety of context and applications.
So there is a lot of research that is done in this field, but also it's way more complicated than what
we expected a few years or a few decades ago.
Actually, enthalpy is the part of AI that that we are the most frustrated about, if I may say so,
because in other fields we advanced really quickly in the results that we could obtain with AI.
But for an LP, we are not at the level that we expected some decades ago.
It's really more complicated than expected to deal with the human language.
But that means that it's also a super excited to go into it and find a new way to compute human language
and also to follow what happens in the research community and everything.
So of course, it's more challenging, but it's also more exciting to be part of it, and that is where
birth comes into place so quickly.
What is Berts about is an algorithm released by Google about a year ago and it was really game changing
in the whole community of Alpay.
Google stated that it's the most game changing advance in MLB since the last five years because it beats
all the previous algorithms in many Benchmark's tasks in L.P.
And basically what it does is that it provides a better understanding of words and sentences using the
context.
So so it's not a model that is made for specific tasks.
It's more a tool that is supposed to understand the language and provide what we call a language modeling.
And actually it's already used in many applications.
And for instance, Google already started to use it in the search engine.
So here's a quick example of a research using Google, the research being math practice books for adults.
So someone is looking for books to practice math, but books should be for adults and not for the kids.
And so we can see that before using.
But this is the kind of resource that we could get.
And we see that it's not really what the person was looking for, because this is for for kids with
birds.
Google has a better understanding of the meaning of each word, and he can make relations between the
words adult and the words grown ups so it improves the results of the research.
So that's just a quick idea of what that is, what it can do and how it is already applied.
Not I'm sure that you want to know more about birds.
Let's see how we will deal with it.
So in the first part, of course, we will go into the intuition behind birds.
So we'll see how it works first by getting a general idea of birds then.
And for it's really interesting to do that.
We go through the history of birds.
So so how we used to do NLB before with Arnon's, for instance, and how it led us to create the attention
mechanism that was then used in the transformer, which is a very powerful algorithm released by Google
too, and how the Transformer inspired Google to release bots.
So this history behind birds is first to be interesting, but it's also really important to understand
the theory behind birds.
Then we'll go into more details, into birds, architecture, what it is made of and so how it works
and why it works.
And finally, we'll talk about birds for training, which is how it has been trained, and it's deeply
related to how we use it and how it generally works.
Then, of course, we will go into the different applications.
And the idea here is to show you all the different ways that you can use birds and actually how you
can use the different tools that come with birds.
So the first thing you'll do is that we will simply use the tokenized that has been used for birds and
that is available with the birds package.
So here we will not heavily use, but we will just use a tool that comes with a.
But you see, that is a really useful one, and that's the model that we will use after the tokenized
will be used right after in the second application, where we'll use that as an amazing layer.
So basically, we'll just use birds as a tool to have a better representation of our words, of our
sentences, and then we will be able to plug this embedding layer into our personal and model, and
that will make it much more powerful and flexible.
And finally, we will go into the ultimate level of matching birds with fine tuning, which is actually
to retrain the bird architecture for our specific task, which is actually what Bird has been thought
for originally.
So that's it for the plan of touch.
Lots of interesting things to do, interesting things to see and a lot of things to learn.
Popular, super excited about that.
So let's go into it right now.