Would you like to inspect the original subtitles? These are the user uploaded subtitles that are being translated: 1 00:00:00,420 --> 00:00:05,070 Hello and welcome back to the course on computer vision in today's tutorial We'll talk about the plan 2 00:00:05,160 --> 00:00:06,490 of attack. 3 00:00:06,510 --> 00:00:12,780 So what we will learn in the section about Ganns will first talk about the idea behind Janz and who 4 00:00:12,780 --> 00:00:19,800 came up with the idea and why they're generally even necessary in the world why the world needed them 5 00:00:20,340 --> 00:00:27,180 and how they have helped in the space of neural networks and computer vision progressed forward what 6 00:00:27,180 --> 00:00:28,600 purpose they serve. 7 00:00:28,620 --> 00:00:32,550 I think this will be a very important tutorial to lay the foundation of why we're going to be doing 8 00:00:32,550 --> 00:00:36,700 what we're doing in the section why it's important to learn about the guns. 9 00:00:36,860 --> 00:00:40,270 They all talk about how guns actually work. 10 00:00:40,380 --> 00:00:48,690 We'll get into the detail into the nitty gritty of Gannes and we'll actually look at three steps. 11 00:00:48,690 --> 00:00:53,490 This was a very long tutorial so I've broken down into three tutorials to represent the three steps 12 00:00:53,490 --> 00:00:54,260 that we take. 13 00:00:54,270 --> 00:01:00,600 So those are just three iterations of the training process which we'll look at in detail and watch our 14 00:01:00,990 --> 00:01:04,330 network grow is going to be very exciting to observe. 15 00:01:04,440 --> 00:01:10,350 But also keep in mind that these are just three steps out of the hundreds and thousands that actually 16 00:01:10,350 --> 00:01:17,290 happened during the training so you can imagine how you know what a small portion of the trained person 17 00:01:17,320 --> 00:01:18,230 we're going to be looking at. 18 00:01:18,300 --> 00:01:23,940 But at the same time just those three steps they will help us lay that intuition foundation. 19 00:01:23,930 --> 00:01:29,820 So when you get to the practical tutorials coding of online you will know exactly what is going on and 20 00:01:29,820 --> 00:01:31,530 exactly what's happening. 21 00:01:31,530 --> 00:01:39,420 Also we'll talk about applications all fudge of the guns and how they are changing the world how they 22 00:01:39,420 --> 00:01:44,520 are applied in so many different spaces and hopefully this tutorial will inspire you to call up your 23 00:01:44,520 --> 00:01:49,890 own applications and see how you can use Ganns in either your work or your hobbies or maybe your side 24 00:01:49,890 --> 00:01:57,390 projects that you might be working on a radio or might start after this course and this is quite a technical 25 00:01:57,390 --> 00:01:58,260 section. 26 00:01:58,270 --> 00:02:07,590 So Ari we have included some annexes to the Course to the bottom artificial neural networks and convolutional 27 00:02:07,590 --> 00:02:08,680 neural networks. 28 00:02:08,850 --> 00:02:14,310 Make sure to check them out and you will need them especially in this section because we'll be talking 29 00:02:14,310 --> 00:02:16,910 about things like back propagation and grey. 30 00:02:17,100 --> 00:02:23,880 We won't be actually talking a lot about backwardation gradient descent but they will be underpinning 31 00:02:23,880 --> 00:02:27,600 concepts in the process of training a neural network. 32 00:02:27,600 --> 00:02:32,790 So if you're not familiar aware of those concepts and highly encourage you to check out the annexes 33 00:02:32,790 --> 00:02:40,380 for the course before proceeding with the section because it will be helpful knowledge for you to really 34 00:02:40,770 --> 00:02:46,300 soak in the concepts that we're going to be talking about in the upcoming tutorials. 35 00:02:46,560 --> 00:02:51,500 And on that note I can't wait to get started with the first tutorial in this section and I'll look for 36 00:02:51,560 --> 00:02:52,220 it there. 37 00:02:52,370 --> 00:02:54,860 And until next time enjoy computer vision. 4328