1 00:00:00,510 --> 00:00:05,620 Hi and welcome the chapter to ten point one where we actually start building Linnett and Paris. 2 00:00:05,670 --> 00:00:08,880 So before we jump into Karris Let's actually talk a bit about that. 3 00:00:08,970 --> 00:00:13,980 That is quite old is quite an old CNN it was actually developed by lican. 4 00:00:13,980 --> 00:00:14,930 That's all it gets its name. 5 00:00:14,940 --> 00:00:15,930 Ellie. 6 00:00:16,090 --> 00:00:16,470 ELLIE. 7 00:00:16,470 --> 00:00:17,150 Sorry. 8 00:00:17,280 --> 00:00:18,830 And it was built in 1980. 9 00:00:18,870 --> 00:00:23,290 That's over 20 years ago and it was actually very very effective. 10 00:00:23,330 --> 00:00:27,540 On the amnestied a set in round writing didn't digit recognition. 11 00:00:27,930 --> 00:00:31,110 And you can visit this website to learn more about Lynette's. 12 00:00:31,230 --> 00:00:35,490 This is basically a sample example of how the net was constructed. 13 00:00:35,610 --> 00:00:42,900 This was the input convolutional is feature NOPs generated a convolutional layer some more maps and 14 00:00:43,040 --> 00:00:44,790 Celine's here as well. 15 00:00:44,830 --> 00:00:47,360 Disheartens was a map Max building here and building here. 16 00:00:47,700 --> 00:00:51,110 So let's look at Linette here in action. 17 00:00:51,120 --> 00:00:52,520 This was of. 18 00:00:52,590 --> 00:00:58,250 It's like a 20 year old program where we're actually using an inch and CNN and justifying these digits. 19 00:00:58,620 --> 00:01:00,780 So it is quite cool to see. 20 00:01:00,800 --> 00:01:07,480 So now let's jump into cameras and actually start building that hi and welcome to. 21 00:01:07,540 --> 00:01:08,690 I buy them a book. 22 00:01:08,740 --> 00:01:12,480 We're actually going to build Linnett and tested on that. 23 00:01:12,700 --> 00:01:17,070 So let's bring this up and I'm already seeing small Tipler right here. 24 00:01:17,410 --> 00:01:19,490 This should be in list. 25 00:01:19,510 --> 00:01:27,100 So basically we do our usual imposing images transforming all the It's a blah blah categorical one and 26 00:01:27,100 --> 00:01:31,010 puts all these things defining classes number of pixels. 27 00:01:31,330 --> 00:01:34,010 And now this here is Linette. 28 00:01:34,160 --> 00:01:35,680 It doesn't look like much does it. 29 00:01:35,680 --> 00:01:39,200 However this is the actual model that I just showed you those lights. 30 00:01:39,370 --> 00:01:45,670 Basically it has two sets of CERP which is basically convolution relo and pulling Dunhill then has a 31 00:01:45,670 --> 00:01:52,930 fully created layers and then basically we have all soft Max and all the usual optimizer. 32 00:01:53,170 --> 00:02:01,270 However we are using it a delta and create to model his only 1.2 million premises again and we train 33 00:02:01,270 --> 00:02:03,480 ticket Trinite on edness and see if it doesn't. 34 00:02:03,540 --> 00:02:06,590 And this latest model and these are the results. 35 00:02:06,610 --> 00:02:12,670 After 10 ebox and we can see it actually got quite good at ninety nine point twenty one percent that 36 00:02:12,670 --> 00:02:14,190 is actually really impressive. 37 00:02:14,440 --> 00:02:20,960 And if we go back to our presentation him this was let's bring this up right here. 38 00:02:21,320 --> 00:02:22,860 This was Lynley dance performance here. 39 00:02:22,860 --> 00:02:29,320 And I if you point one on this now that is CNN we used before of this what did he get. 40 00:02:29,330 --> 00:02:31,460 He got very close to Tony Potts as well. 41 00:02:31,470 --> 00:02:32,920 Nine nine point one five. 42 00:02:33,230 --> 00:02:41,790 But you can see 20 years ago CNN was designed this simple CNN here actually got such good results. 43 00:02:42,170 --> 00:02:46,790 I could imagine 20 years on hardware 20 years ago how long this would have taken to train do. 44 00:02:47,060 --> 00:02:52,060 And now it is we're using this on a tiny laptop but just you of course keep you and the trains within 45 00:02:52,550 --> 00:02:54,080 maybe half a mile. 46 00:02:54,740 --> 00:02:55,710 So that's quite impressive. 47 00:02:55,800 --> 00:03:00,680 Now let's move on to Alex net which is a lot more complicated than Linette.