1
00:00:00,570 --> 00:00:00,980
Hello.

2
00:00:00,990 --> 00:00:01,730
Welcome back.

3
00:00:01,950 --> 00:00:07,060
And this lesson we are going to examine that convolution or neural network with a pool layer.

4
00:00:07,260 --> 00:00:12,750
In this arrangement over here we see a combination of a convolution or layer an appallingly denoted

5
00:00:12,870 --> 00:00:23,580
as Layer 1 and that a combination of a convolution layer and appalling layer denoted as Layer 2.

6
00:00:23,620 --> 00:00:27,430
Also we see three f C's.

7
00:00:27,430 --> 00:00:35,860
Over here we have FC FC FC FC stands for firmly connected layer like we mentioned earlier we use the

8
00:00:35,860 --> 00:00:41,660
polling data to reduce the size of the inputs in order to speed up the computation.

9
00:00:41,900 --> 00:00:47,260
A formal connected layout is a simple layer perception.

10
00:00:47,260 --> 00:00:49,820
This is like the normal neural network.

11
00:00:49,850 --> 00:00:58,960
Examples we saw earlier in this convolution on neural network arrangement is similar to the one we saw

12
00:00:59,020 --> 00:00:59,410
earlier.

13
00:00:59,440 --> 00:01:08,200
The two differences over here is the introduction of the pooling layer and the two extra fully connected

14
00:01:08,200 --> 00:01:14,920
layers and the soft max function and the fully connected layer is your standard normal neural network

15
00:01:15,670 --> 00:01:16,100
right.

16
00:01:16,120 --> 00:01:19,420
If you have any questions regarding this just send me a message.

17
00:01:19,510 --> 00:01:23,770
So that's one of the for this lesson and I shall see you in the next lesson.