Datasets:

Yassmen

/

TTS_English_Technical_data

like 2

I hope you're going to learn a lot in this video so let's jump right into it

Now our URL here has a scheme portion and this one happens to be HTTP.

token number five and then I can actually make pretty decent predictions

We're going through that. There's a Spotify web API that returns JSON,

numbers and we'll have 27 rows and we'll have

2B URL encode and happens to be a plus. And I know that and I just happen to know

pretty low negative log likelihood and finally here it thought this was one

So this is basically doing the same thing, right? These messages are the same messages that were coming

numbers that make up these three words so this is a fully connected layer

close to the labels but in some cases they differ

to make sure that I create it on the device number two what I introduced is

when you create a list with a for Loop within one line we could also make it in

output probabilities that's very often used and we used it as well here

There's a link to a repository of built out collections that are ready for you to use.

we can see that this is a all the counts across all the

to this embedding table and it's going to pluck out a row of that embedding

back to our get response function right here and remember that so far we're

the play method on the media player API. I suppose too, the song isn't stored on the device,

representation and then i plot just the diagram text

we're going to sign up for a trial account. So I'm gonna click the Sign Up button here

image Bas 6 4 bites to base 64 and here we just enter the image base 64 and our

We were reading, right? Just getting information to display it like

this to a float so that we can multiply now the output actually here

And last but certainly not least, take advantage of the fact that you are in a video-based course.

is dl by dc and what we have so far

send button or send input is triggered if uploaded image then what we want to

So we're gonna drop this one into the no column. So we're looking good so far.

like you're being taken care of, right? This is so nice.

so if we want to calculate x2.grad then

and by default you'll notice a.d type which is short for data type is float32

And we did see this in both the Spotify and Twilio ones. However, ours doesn't provide links to other resources.

I want to work with this toy example to just get you used to this operation and

So when we are creating a message, we must include the to, right?

So that also is on my console. So I'm gonna flip back to my console.

a RESTful API should support caching. It's up to you to maintain the cache on your client

impressive so this is a with Adam this is without

because obviously something wrong happened, I didn't get any of the messages back,

so this is 1 minus 10 h of x squared

special start token here i'm going to

So you can look at, there's these different things where you can, I was right, you can check

so that's parameters of neuron and i'm calling it this way because also pi

but that is also available in the header here, right? So like it's just a different view of it.

and max of one w for w in words so the longest word will be

and the the tests or the def set is 2.38 as well

so it is every crimp tap be a house oh those

So let's go ahead, let's give it a, we'll we're gonna go ahead and make this an empty array, right?

if you take that model and it looks like it's working really well but you try to

file so uh each file has like a title and let me show you we go to the wave

to know the local derivative of 10h so if we have that

and so if this is strong enough then we are not able to overcome the

So also I wanted to show you the request that happened. Let's take a look at that.

and so the log of the probability here i have the log of x from zero to one

and click Active Numbers, I'll open up a new tab. Nice little hack to keep your screen open here.

to minimize the loss so if we nudge all the parameters by

138 is telling us that g will grow and the slope of that growth is going to be

and so the optimization problem here and here are actually equivalent because

getting 4.87 and so that's telling us that the initial predictions are not uh

counts for all the first letters so uh j starts a word h starts a word i

taking more and more of these characters and we're going to be feeding them into

a random noise with it then you're predicting the noise in this random

will use an encoder block of self attention and uh all it means that it's

So we can come here into the message log. We can go pull that number from the query string.

so but what you can see here obviously we don't have the llm yet and we don't

audio if your data is like five sentences L long then you wouldn't be

which was published last year its name is a speech T5 Unified

of all the bigrams okay

every one of our five examples we now have a row that came out of a

We saw how to organize our request together into collections as well as share common

I'll be right here waiting for you. I know that I've gotten into some pretty bad

zero but if w has non-zero numbers you accumulate loss

respond this stays the same almost and so that's why the slope is zero okay now

left to right basically so here we have a graph it's a directory

there's a unique input for a unique output in those cases we do what's

response qus this response but right now let's just keep going before we create

clear on the difference and when you look at the the docs of lower case

um and because we have lri here um

then we want to install D upgrade PP and after that we will install the

the learning rate i mean and we achieve it about 2.3 now

but I have no idea how it does it and I'm okay with that level of abstraction.

like a sigmoid or 10h or something like that so as an example

kind of uh you know it sounds a little like me but you it sounds horrible

so basically we're taking e and we're raising it to the power of 100 and that

encourage you if there are some things here which you didn't like in the um

and those examples are summarized here when the input to the neural network is

uh basically using a different network how much of any candidate

okay we're at 2.32 let's continue training

else the history is empty so here again we load the chat history in the session

your audio database it's more related to the textual

Security can't just be a bolt-on that you do at the end. In fact, it needs to be something that we're doing throughout, pervasively.

uh approach for sequential data so in our case it's text to speech task so we

line ends with a period if you don't if it doesn't end with a period it's gonna

So use the messaging API to use submitted photos and also create a web-based API that matches this expected response.

Now remember that is going to be using my Auth token and my account SID, and it's on this context,

they should have a switch there to make it more clear and this is also or

function has all kinds of structure and we only know about the very local

at that precise point if we nudge in a positive direction the function doesn't

to be doing is actually taking a look at the dependencies for this project the

total number of words we expect this to be roughly 32 000

and this will be end.label and so now

make sure uh you don't proceed to The Next Step until you get this message