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Yassmen
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TTS_English_Technical_data

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way this works now is we can have an x which is say like 2.0 3.0 then we can
so here we see grad zero so as a base case we need to set both.grad to 1.0
All right, so I have now should have been sent an email. So let me make sure that I got that email and I did,
and the output of this is now 5 by 27 so we can see that the shape of this
to questions with other questions it might ignore your question it might just
we can actually create that using mp not mp sorry torch dot range of 5
So we're saying of all of the messages, we want to list them, right?
now for every one of those 32 by 3 integers we've retrieved the embedding
so let's create w2 and v2
train the model so first what I'm going to do is I'm going to create a pyour
All right, so just like I just consumed my burrito, we are going to consume that API that we just created.
so that's telling us about the gradient information and we can use this gradient
document and from uh M chains import uh load Vector DB and also load embeddings
I guess let's go ahead and so we'll I can explore products. There's way more products that
the exact number of slope will be exact amount of slope is negative 3.
if a car travels twice as fast as bicycle and the bicycle is four times as
because if the session key is not new session then it's already a Tim stamp so
two columns right so each 20 each one of 27
that the model which used for the first pass decoding that makes sense but but
parameters and we need to make sure that p dot grad
one file defines the GPT model the Transformer and one file trains it on
value also what I want to add here is with streamlit do spinner basically uh
then in the next 100 000 we're using a learning rate of 0.01
practice uh this works quite well so let's now continue the optimization
building framework you can see the encoder and decoder for those of you who
input data are not that useful to us and that's because the input data seems
integers so we need some kind of a lookup table from characters to integers
careful because um okay the learning rate we set to 0.1
plot it so draw dot of n gives us x1 times w1 x2 times w2
ensemble of sub networks and then at test time everything is fully enabled
using this English models that we have here but if you'd like to use other
is a much more advanced and popular Optimizer and it works extremely well
And so part of the message that we can see, we can see who it was to,
and so if you pass in very positive inputs we're gonna cap it smoothly at
l with respect to a because a is the one that we bumped a little bit by h
maybe there's an album that could be created. There's an album resource that contains photos
just paddings these are not something important just paddings to like fix the
if it has changed since a certain time. This technique allows for caching,
first here we have the causal self attention block and all of this should
this style of string interpolation. So I have the event object, and on it
so let's now scroll back down to this is much larger reinitialize the
and so by symmetry also d d by d
Data via a shared encoder decoder structure so you can see again it's the
and we could go and query this ourselves, but we don't need to because the API has wrapped up for us.
and now let's do one learning rate decay what this means is we're going to take
remember n Ed is 32 instead of having one Communication
hit rename I'm going to call this one first first
100% sure if it would work right now maybe there will be another small error
the element for example 3 comma 13 is giving us the firing rate of the 13th
messages and there we can see it's a list with human message AI message then
so multiply i think it won't surprise you will be fairly similar
And it's this one here, it's a lightweight REST API client for a VS code.
into this expression here so let me copy paste this
we calculate the loss we're not actually reinventing the wheel
speech using your text welcome back everybody so as you can see that the
conversational retrieval chain so conversation retrieval chain okay what
to be useful later during inference because while we're sampling we can
it's running out to the screen and there's different levels that you can do and error is one of those.
a list and retrieving, we're going to create. So we're gonna go under body and for the body
so that plucks out the probabilities of that the neural network assigns to the
So we now have our app here locally and we can change things.
engine autograd is short for automatic gradient and really what it does is it
the account SID showed up there and then it automatically did a two URL encoded and if you look it put
propagate through your function and then you can use this as a lego block in a
and this shape is the same but now this is uh it doesn't matter if
web Text data set which is a fairly large data set of web pages then I
derivative to all the leaf nodes sorry to all the children nodes of it
this data will have changed a little bit so now this neuron
But know that this exists. One of the really cool things that Postman does is
here so thef chat chain is running what else do we want let's keep it at that
fashion and then the elements here in the lower triangular part are telling
State and the stream chat message history takes the chat history also from
So you can see here, there's this call to action and that's what this is and this is why I can change this
and then I'm gonna bring this back a little bit here so you can kind of see, let's change
to a tensor of values that are very close to zero then we're going to get a
each other might end up in a very similar part of the space and conversely
is shared one and this is a it's all aggressive all right so I
of them and uh whatever it says you have to write it right next to it use this uh
core module we're going to import from the prompts module we're going to
And it's kind of really a quick little look at this. I don't actually even need to do a collection.
have this uh residual pathway and you are free to Fork off from the residual
That's what we're trying to get. And that's what the old model and the new model of security were designed to do.
chain rule here is d l by d e which we see here is
I love Twilio, I mean, I love it so much, I even applied for a job with them and I got it.
in our app we have to account for that we can't use our normal chain if you
a little bit here, you'll see My Twilio Number. So go ahead and send a text to your number.
me decode B BYT from idx do shape and then here we're also going to
a little bit more and then you'll see that there's always this Spotify URL that allows you to see what it was.
will emit two vectors it will emit a query and it will emit a
this exercise is number one we got to practice a few more operations and uh
everything and uh let's continue with the next part this part is to discard
do not lr not the learning rate but the exponent
completely abstracted away from me. I'm still in control of what has
o is 10 h of n
I think was 2.07 so it went from 2.07 all the way down to 1.48 just by scaling
here line by line so for example like um text Page would be PDF file get page no
something meaningful or understandable by human beings like us uh actually this
so in this case the correct thing will be happening because the same bias
There are more http verbs, also known as request methods besides get, most common scenario you see is when you
Who runs the world? Girls. But I don't remember where, what album it's on.