1
00:00:00,420 --> 00:00:00,860
Hello.

2
00:00:00,900 --> 00:00:01,500
Welcome back.

3
00:00:02,160 --> 00:00:08,520
So this arrangement over here represents the single input single output in Europe network.

4
00:00:08,520 --> 00:00:15,480
We have the input the weight and the predicted value the input interact with the weight to produce the

5
00:00:15,510 --> 00:00:18,970
predicted value.

6
00:00:19,560 --> 00:00:25,710
Let's say this is a simple neural network to predict whether a person is sad or happy given a particular

7
00:00:25,710 --> 00:00:27,100
temperature.

8
00:00:27,270 --> 00:00:32,930
In this very simplified example we can consider sad and happy to be a numerical value.

9
00:00:33,060 --> 00:00:39,460
We could say a number above 10 is happy and a number below 10 is sad.

10
00:00:39,570 --> 00:00:45,210
We shall see how to encode words into numerical values later on in the course.

11
00:00:45,420 --> 00:00:51,120
If we are to write The pseudocode for this very simplified neural network this is what it would look

12
00:00:51,120 --> 00:00:54,030
like to find a predicted value.

13
00:00:54,030 --> 00:00:57,270
We simply multiply the input by the weight.

14
00:00:57,270 --> 00:01:02,910
Very simple very straightforward predicted value equals input times.

15
00:01:02,920 --> 00:01:05,760
Wait let's see how this works in could.
