Highlight, how about some of the important hubs when come with neural network, deep learning, machine
learning and I so you will meet them.
A lot of time now let's talk about so the first one is Adam.
Optimizer.
So what are they?
A un.
Will.
For for first order.
And.
By.
Optimization are.
Stochastic.
Objective function.
Based on.
Dr..
Estimates are lower order.
Momani.
The next one I want to talk about is men underscore to underscore value.
Error, I mean.
And this a lost function.
Sharmeen.
Square.
Error usually, right, shot at him as a.
Majles.
The average.
The squares is.
How the arrow is.
That is the average square difference.
pitchIN.
The.
Let me make a new life for the show a little bit long, so between the.
Actual.
Estimated value.
And what's to be estimated?
MSA, a measure of the a quality.
An estimated.
It's always.
Non-negative.
And, uh.
Clothes are.
The value add to zero.
They are better, so, of course, we want our role to be zero.
So out of it, we have a big our model is going to be very worse.
And the next one I want to talk about is I could raise a metric.
So metric.
Is our focus on that issue to evaluate the performance of the war or the.
Training and testing.
Now, let's talk about more.
On the outer perimeter, we will saw.
Fred, underscore train.
So this is on a real.
Predictors.
Training data.
RESPA underscore a train, so this a un arri.
Responded to.
Epoch.
Equal 1000.
This is the number of epochs, the.
Trained model and EPOP is an iteration over the entire.
X and Y.
Data provided.
And.
The Post.
Equal one, please, God, please, not that you need to keep this, but while we train the model.
So this is an in danger.
So zero, one and two.
Here, the.
He earns.
For shirt, they might represent the.
Following.
Zero is silane, one ACOA Progress Bar and two equal, one lie per epoch.
So far, worse, boss.
Wise forward, boss.
So.
Follow us by.
Implies.
For propagating.
From the.
In July.
Today.
I would like to.
Backwords.
But.
A.
Might work, but.
Ms.
Bligh's.
Propp.
Well, getting.
So.
Back propagating.
From the.
I would like to the in Paul.
They are sort of our it is far more important I would hand back is from the elbow to the elbow.
So let.
Put a dot in here.
And now that horrible number of iteration.
So no iteration.
A.
There are a number of iteration.
Implies no number of buses where one bus equals one four bus.
Plus one.
That was Basil Dinamo iteration as the number of passes where one was equal, one for spies, plus one
backwards, Bizo, the total of the forward and was.
And now, for example, now that resembles order, it will have 1200, I say 12000 example, so it will
have 12000 training Sambos.
And that our Boschi is and our.
Boschi is
six thousand, so it will take us to iteration, so it will have two iterations to complete.
To complete one epoch, so that is in the literature that is in the front is the reason we bought the
first six thousand symbols and both from a forward base and a bicycle pass.
In the second iteration, we bought the ninety thousand Sambos and both form of our bus and a bus.
So after two iterations, our neural network will see the how trought how that resembles which it one
a box.
Now, that hobel too positive and too negative, so.
That's right, and thanks for the.
So to.
Positive is an outcome.
Where the model.
Correctly predict.
The.
So.
Let's say a true narrative is an outcome where the model correctly predict.
The negative plus name that he last.
So a false positive is an outcome where the model.
Predict.
The.
Fose.
Positive is an outcome where the model incorrectly, the model predicts incorrectly.
The.
Positive class.
Next one, we were talk about a fall
nagati.
Is an.
I'll come.
The model predicts.
In.
Correct, lay the glass.
So negative for what are they, a false negative and.
The last one will be a true.
Bossidy, so how come where the model correctly predict the positive cost?
So later we will meet.
A lot of positive or negative for a true negative and positive, shall we?
I hope you will take this note.
And you can understand it easier, much easier when we come to do the real world project.
And the last one is how we talk about a force that to train.
Neural network.
So what does that.
The first time a.
Import.
Sequential glass from Carus Dot model.
So the second step is.
Layers.
Using DOT at.
Method.
That's just a.
Can figure.
The London.
Process using not.
Kombai.
Made hard and the last time a grand model on 28, I said you did not fit.
Method, and that is on in this video and this note as well, so this explain everything for you.
And I hope when you build a real model, you can bring out next to you so you can understand it.
And.
So if you take this note during and we train the model on the real goal now, it's much easier to understand
why we do this and what do they mean.
And that is the end of this video.
I hope you enjoy it.
And I will see you in the next video.