Hello again guys. 1
in this tutorial we are going to learn 2
How does deep learning work. 3
So let's say that we have these inputs x1 x2 x3 till xn each of these inputs has a weight we call 4
the weight of x1w1 the weight of x2w2 and the weight of xnwn and the value add and the weight is the 5
level of the effect that this input will be contributed to the sum of wixi 6
And here this is the hidden layer where data are analyzed before we get our output layer let's just think 7
about the line again let's say that the first feature or input is the color of the lion's body starts 8
with something like yellow and when this weight go up the yellow star turning to be light brown until 9
dark brown. 10
So based on this weight the optimal color of the line will be forwarded to this hidden layer and let's 11
imagine this is some other feature or input and this is another feature. 12
Let's take this example to understand what I mean by the weights we have three inputs or three features 13
about homes and based on these three features we need to predict the price of that house the first input 14
is area the second the number of rooms and the third is the age of that home. 15
Let's first start with some initial values for each weight of these inputs. 16
This is something completely random to learn how the deep learning works. 17
So we assume that the area 4 and the rooms 2 and the age 3 and we put this information. 18
So we need now to calculate the sum of wixi 19
This is the wi and this is the xi 20
So let's take the first example here in this data. 21
Let's say that the area of this house is twelve hundred. 22
So we are going to multiply 4 which is the weight of the area by twelve hundred plus rooms two multiplied 23
by two which is the weight that we put for rooms plus three which is the weight of x3 of the 24
age by the age of that home which is 15 years. 25
And we found that the final number is four thousand eight hundred forty nine. 26
The actual value of that house is one hundred thousand not four thousand. 27
So you see there is a big gap between these two numbers. 28
What will happen is we will have the difference between the actual value and the predicted value which 29
is one hundred thousand minus four thousand eight hundred forty nine equal ninety five thousand one 30
hundred fifty one and this number we call it the cost function. 31
So what is the cost function. 32
The cost function is a function that measures the performance of a machine learning model for a given 33
data. 34
It quantifies the error between the predicted values and expected values and presents it in the form 35
of a single real number. 36
The cost function is about ninety five thousand plus. 37
This information will be fed back to the hidden layers and we call it back propagation. 38
So we call the results that go from the hidden layers to the output layer we call it forward propagation 39
and the opposite direction. 40
We call it backward propagation. 41
So now the hidden layer should make adjustment for the value of the four thousand eight hundred and 42
try to find the value which is closer to the hundred thousand. 43
And we need to make it the minimum we need to make the y minus y hat as minimum. 44
And when data goes back here again it will go back to this inputs where these weights should be adjusted 45
again and we can say here that because the value that we obtained is very low compared to the actual 46
value we say that these weights are very low. 47
So go and increase each of them. 48
Then again the sum of wi will be calculated then again will be forwarded here. 49
And this process will continue until we reach to the optimal value. 50
in the next tutorial I'm going to explain this process in details. 51
And after you understand it you can build your deep learning algorithm.