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These are the user uploaded subtitles that are being translated: 1 00:00:00,360 --> 00:00:02,890 Hello and welcome to this new tutorial. 2 00:00:02,910 --> 00:00:08,790 The second big step of this implementation we have already created one brain. 3 00:00:08,850 --> 00:00:10,470 The brain of the generator. 4 00:00:10,590 --> 00:00:14,620 And now we're about to create the second brain of our deep convolutional Ganns. 5 00:00:14,790 --> 00:00:20,640 I'm talking of course about the brain of the discriminator and we're going to proceed exactly the same 6 00:00:20,640 --> 00:00:23,690 way as we proceeded for the generator. 7 00:00:23,820 --> 00:00:30,300 We're first going to define a class that will define the architecture of the neural network for the 8 00:00:30,300 --> 00:00:31,430 discriminator. 9 00:00:31,560 --> 00:00:38,220 Then this class will also include a forward function to forward propagate the signal inside this new 10 00:00:38,250 --> 00:00:38,960 network. 11 00:00:39,150 --> 00:00:45,720 And then once the class is made we'll create a discriminator object that is will create the brain of 12 00:00:45,720 --> 00:00:46,860 the discriminator. 13 00:00:46,860 --> 00:00:52,840 So let's do this let's tackle the second big step of our deep convolutional gets. 14 00:00:52,920 --> 00:01:01,620 So we are starting with Class Of course then we need to give a name to this new class that will define 15 00:01:01,620 --> 00:01:02,690 the discriminator. 16 00:01:02,820 --> 00:01:09,700 And since we called the class of our generated G We're going to call this one the thing that makes sense. 17 00:01:09,810 --> 00:01:19,030 And so d again is going to inherit from the end in module and so inside we use again and then that module. 18 00:01:19,470 --> 00:01:26,820 All right then Kallen and then we go inside the class to start with the init function that will define 19 00:01:27,000 --> 00:01:29,250 the architecture of the neural network. 20 00:01:29,580 --> 00:01:37,050 So that's the same we start with Def and then double underscore in it that will underscore again and 21 00:01:37,050 --> 00:01:38,880 some parenthesis. 22 00:01:38,880 --> 00:01:46,320 And inside we only put self which will refer to the future instances of our class that will be created. 23 00:01:46,320 --> 00:01:52,650 That is the object and that will allow us to associate the specific variables that are attached to the 24 00:01:52,650 --> 00:01:56,930 object that is I'm talking about the properties of the object. 25 00:01:57,090 --> 00:02:00,330 The object will be the neural network of discriminator. 26 00:02:00,360 --> 00:02:07,830 So these properties and variables will be the different modules composing the architecture of the discriminator. 27 00:02:07,830 --> 00:02:16,980 All right so now Collen we go inside the function and same we start by activating the inheritance using 28 00:02:16,980 --> 00:02:26,360 the super function which takes as arguments are discriminator class and our object self. 29 00:02:26,490 --> 00:02:32,780 Then don't forget we had a done here double underscore in it double underscore again. 30 00:02:32,910 --> 00:02:34,710 And some parenthesis. 31 00:02:34,710 --> 00:02:41,320 Perfect inheritance activated all right and now things are going to get interesting. 32 00:02:41,340 --> 00:02:44,520 I'm going to try to make you guess what we're going to start with. 33 00:02:44,610 --> 00:02:50,700 We have to start with the first module of this architecture of the neural network of the discriminator. 34 00:02:50,700 --> 00:02:56,940 And so according to you what is going to be this first module is it going to be an inverse convolution 35 00:02:56,940 --> 00:03:03,190 like before or is it going to be a convolution or is it going to be a fool connection. 36 00:03:03,570 --> 00:03:05,670 Well think about this for a second. 37 00:03:05,850 --> 00:03:13,620 And until then I will get my main again which represents the metal module that will contain the different 38 00:03:13,890 --> 00:03:16,940 modules composing the architecture of the neural network. 39 00:03:16,980 --> 00:03:18,500 So exactly like before. 40 00:03:18,510 --> 00:03:24,210 So cells that main and this module will be a big sequence of layers. 41 00:03:24,270 --> 00:03:25,980 That is a big sequence of modules. 42 00:03:26,190 --> 00:03:31,980 And therefore I'm setting this main metal module to be an object of the. 43 00:03:32,130 --> 00:03:36,990 And in that sequential class parenthesis. 44 00:03:37,200 --> 00:03:38,030 And here we go. 45 00:03:38,160 --> 00:03:40,140 Let's see if you got this right. 46 00:03:40,140 --> 00:03:43,360 For this first module of this architecture. 47 00:03:43,650 --> 00:03:44,880 Let's see. 48 00:03:44,880 --> 00:03:47,310 All right so to answer this question right. 49 00:03:47,400 --> 00:03:51,080 Well we need to understand what the discriminator is going to do. 50 00:03:51,210 --> 00:03:59,040 It's going to take as inputs a generated image coming from the generator and return as output a discriminating 51 00:03:59,040 --> 00:04:02,190 number which will be of value between 0 and 1. 52 00:04:02,250 --> 00:04:08,940 Therefore since it takes as inputs a generated image the generated image created by the generator. 53 00:04:09,060 --> 00:04:16,200 Well that means it takes as input an image and therefore the first module of our architecture is simply 54 00:04:16,200 --> 00:04:19,680 going to be a convolution not an inverse convolution. 55 00:04:19,680 --> 00:04:21,330 That's only for the generator. 56 00:04:21,330 --> 00:04:28,350 But this time a convolution it's going to take as input an image an image created by the generator and 57 00:04:28,440 --> 00:04:29,880 through the convolution. 58 00:04:30,030 --> 00:04:35,940 And then of course some more convolutions we will get in the end a simple vector of one element that 59 00:04:35,940 --> 00:04:40,080 will contain the discriminating number a value between 0 and 1. 60 00:04:40,110 --> 00:04:41,010 So there we go. 61 00:04:41,040 --> 00:04:44,140 You have the idea and now it's going to be the same as before. 62 00:04:44,220 --> 00:04:50,520 We're going to add a series of different convolutions with different parameters and will best the feature 63 00:04:50,520 --> 00:04:51,030 maps. 64 00:04:51,060 --> 00:04:54,460 And of course we'll apply some rectification. 65 00:04:54,660 --> 00:04:58,490 And we'll let you find out about some surprise regarding the rectification. 66 00:04:58,500 --> 00:05:02,900 It's not going to be a real it's going to be something a little more sophisticated. 67 00:05:03,070 --> 00:05:05,090 We will see that in one or two minutes. 68 00:05:05,280 --> 00:05:10,920 But let's start with our first module that is our convolution. 69 00:05:11,030 --> 00:05:17,210 So to get this first module we're going to create a new object of the comp to the class and therefore 70 00:05:17,240 --> 00:05:23,510 I'm taking my and then module that and then I'm getting the code to the class. 71 00:05:23,510 --> 00:05:25,320 Here it is perfect. 72 00:05:25,370 --> 00:05:28,130 There is now something less easy. 73 00:05:28,220 --> 00:05:30,730 The parameters of this convert to the class. 74 00:05:30,800 --> 00:05:35,630 So it's actually not that hard and I'm going to give you something else to get what do you think is 75 00:05:35,630 --> 00:05:38,530 going to be the parameter we have to improve here. 76 00:05:38,570 --> 00:05:41,400 For the first argument of this comes to the class. 77 00:05:41,510 --> 00:05:46,200 Remember that corresponds to in channels that is the dimensions of the inputs. 78 00:05:46,400 --> 00:05:53,810 So as we just said the input of our discriminator and therefore of this first module that is the conclusion 79 00:05:54,200 --> 00:05:57,320 is an image an image created by the generator. 80 00:05:57,320 --> 00:06:01,720 So to answer the question what is going to be the inputs of this first convolution. 81 00:06:01,850 --> 00:06:07,260 We simply need to look at the output of the generator and the output of the generator. 82 00:06:07,290 --> 00:06:11,830 Remember is these three channels here of the image. 83 00:06:11,900 --> 00:06:19,340 So the input of our discriminator is nothing else than three to three channels of the generated image 84 00:06:19,490 --> 00:06:21,150 created by the generator. 85 00:06:21,500 --> 00:06:22,210 Perfect. 86 00:06:22,370 --> 00:06:23,700 And then it's going to be the same. 87 00:06:23,720 --> 00:06:30,120 We're going to specify a number of feature maps kernel size astride a padding and choosing true or false 88 00:06:30,120 --> 00:06:31,490 for the bias. 89 00:06:31,520 --> 00:06:32,900 So here we go let's do it. 90 00:06:32,900 --> 00:06:40,370 We're going to choose 64 feature maps then a size of 4 for the Colonel's so. 91 00:06:40,420 --> 00:06:48,450 So these are going to be squares of size four by four then astride of 2 and abetting of one. 92 00:06:48,620 --> 00:06:56,140 And eventually Last but not least we choose to have no bias and therefore like before I'm choosing by 93 00:06:56,140 --> 00:06:58,080 as equals force. 94 00:06:58,100 --> 00:06:58,790 All right. 95 00:06:58,850 --> 00:07:02,150 First Mudgal done congratulations. 96 00:07:02,180 --> 00:07:05,730 Now second module What do you think the second. 97 00:07:05,840 --> 00:07:11,130 Well operation is going to be we're not going to apply a budget yet. 98 00:07:11,210 --> 00:07:13,130 We will leave that for the next convolution. 99 00:07:13,220 --> 00:07:19,340 However we always need to apply rectification and that's the surprise I wanted to keep from you but 100 00:07:19,340 --> 00:07:24,680 I'll tell you it right now we're not going to use a rectifier activation function. 101 00:07:24,710 --> 00:07:26,560 We're not going to use the release function. 102 00:07:26,560 --> 00:07:29,790 We're going to use a leaky reglue. 103 00:07:30,200 --> 00:07:33,530 So what I'm going to do now I'm going to go to Google 104 00:07:36,220 --> 00:07:37,180 here is Google. 105 00:07:37,240 --> 00:07:42,880 And here is the leaky really leaky Leakey who is very close to the loo. 106 00:07:43,000 --> 00:07:50,320 But besides having the max of zero and X which actually is the formula for the reglue we have this addition 107 00:07:50,320 --> 00:07:54,780 of the negative slope multiplied by the men of zero and x. 108 00:07:54,850 --> 00:07:57,630 So that's a slight change in the formula. 109 00:07:57,790 --> 00:08:01,030 And if you put that into a graph you can see the difference. 110 00:08:01,180 --> 00:08:08,620 There really is a horizontal line equal to zero here for the negative values of x and then the straight 111 00:08:08,620 --> 00:08:11,680 line y equals x y for the leak. 112 00:08:11,860 --> 00:08:18,290 We have this slight change here which gives some negative values for the negative values of x. 113 00:08:18,520 --> 00:08:20,350 So that's the slight difference. 114 00:08:20,350 --> 00:08:26,210 I thought it was good to show you this and now why do we choose to use a leaky review instead of a clue. 115 00:08:26,380 --> 00:08:28,870 Well again that's an artist's job. 116 00:08:28,870 --> 00:08:35,570 It comes from experimentation and research basically for the convolutions of the discriminator. 117 00:08:35,620 --> 00:08:41,380 It works better with the leaky reglue than a simple really. 118 00:08:41,420 --> 00:08:43,600 So I'm getting back to by then. 119 00:08:43,670 --> 00:08:48,180 And therefore if you all agree with the leaky Renu Let's apply a leaky really. 120 00:08:48,770 --> 00:08:56,030 So to a player like you we take our end in Mudgal and then that and then we will find very easily the 121 00:08:56,030 --> 00:08:56,630 like really. 122 00:08:56,650 --> 00:08:57,480 Here it is. 123 00:08:57,770 --> 00:09:04,460 And we need to put several arguments of course the negative slope and we'll choose not 0 point zero 124 00:09:04,490 --> 00:09:06,930 one but 0 point two. 125 00:09:07,070 --> 00:09:10,880 Again a value chosen from experimentation and research. 126 00:09:11,020 --> 00:09:12,230 So 0.2. 127 00:09:12,530 --> 00:09:18,350 And then we have a second argument in place because false but why not set the value of in place to false 128 00:09:18,430 --> 00:09:26,990 will set it to true again if we go back to the PI torch stuck documentation by the way yes this is the 129 00:09:26,990 --> 00:09:34,270 PI torch documentation that I highly recommend to go through while watching the tutorials or even any 130 00:09:34,270 --> 00:09:34,770 time. 131 00:09:34,850 --> 00:09:37,640 That's a great documentation it's very clear. 132 00:09:37,640 --> 00:09:43,880 And so if we look at this second argument in place well you can see that's an argument that if set to 133 00:09:43,880 --> 00:09:49,010 true can actually do the operation in place the default value is false. 134 00:09:49,040 --> 00:09:52,890 But we chose to set it to true to benefit from this option. 135 00:09:52,890 --> 00:09:53,560 All right. 136 00:09:53,630 --> 00:09:56,090 So let's go back to Python. 137 00:09:56,150 --> 00:10:00,260 There we are and we are done now with the leaky well. 138 00:10:00,500 --> 00:10:01,020 All right. 139 00:10:01,040 --> 00:10:05,450 Let's not forget the comma and let's move on to the next module. 140 00:10:05,480 --> 00:10:09,220 So the next module is going to be another convolution. 141 00:10:09,290 --> 00:10:15,440 Rest assured we'll get many convolutions as we got many inversed convolutions. 142 00:10:15,440 --> 00:10:18,970 So we have a long way to go before making the architecture. 143 00:10:19,070 --> 00:10:27,720 So let's do it let's add the second convolution and then that comes to the third is. 144 00:10:27,770 --> 00:10:30,560 And let's specify the arguments. 145 00:10:30,590 --> 00:10:37,070 So now you understand clearly the logic the input of this new convolution is the output of the previous 146 00:10:37,070 --> 00:10:42,020 convolution and therefore it is going to be the 64 feature maps. 147 00:10:42,020 --> 00:10:44,260 And now we need to choose a now output. 148 00:10:44,270 --> 00:10:50,450 So since it's actually the inverse operation as the previous one which was inverse convolution. 149 00:10:50,630 --> 00:10:56,480 Well this time we're not going to reduce the number of feature maps each time for each new convolution 150 00:10:56,930 --> 00:10:59,430 as you might have guessed we're going to do the exact opposite. 151 00:10:59,470 --> 00:11:00,740 We are going to increase. 152 00:11:00,800 --> 00:11:07,170 And more specifically multiply by 2 the number of feature maps in each new convolution. 153 00:11:07,280 --> 00:11:12,590 Therefore the size of outputs here that is the new number of feature maps we're getting as the output 154 00:11:12,590 --> 00:11:17,320 of this convolution is going to be one hundred and twenty years. 155 00:11:17,390 --> 00:11:18,510 There we go. 156 00:11:18,830 --> 00:11:23,060 I think now you can almost finish it yourself but we'll never know. 157 00:11:23,060 --> 00:11:24,500 Please stay with me. 158 00:11:24,650 --> 00:11:33,800 Then we're going to choose a size of four for the kernel strive to a padding of one and no bias bicycle's 159 00:11:33,950 --> 00:11:36,210 false right. 160 00:11:36,290 --> 00:11:38,770 Second convolution done. 161 00:11:38,900 --> 00:11:39,680 Perfect. 162 00:11:39,680 --> 00:11:41,390 Next step next step. 163 00:11:41,510 --> 00:11:49,880 As I just gave you a hint is to apply a batch normalization to batched normalize each of the 128 new 164 00:11:49,880 --> 00:11:53,260 feature maps and so that's exactly the same as before. 165 00:11:53,270 --> 00:11:59,720 We're going to take our end in Mudgal than dirt and then we're going to apply a batch norm to D. 166 00:11:59,900 --> 00:12:07,030 And since we're Bache normalizing the 128 feature maps will be exactly two inputs. 167 00:12:07,090 --> 00:12:10,370 One hundred and 28 exactly like before. 168 00:12:10,370 --> 00:12:14,220 No new mystery come the next step. 169 00:12:14,280 --> 00:12:14,700 All right. 170 00:12:14,760 --> 00:12:19,190 Now that's going to be the same we're going to apply a new leaky reglue. 171 00:12:19,250 --> 00:12:20,930 So I'm copying this. 172 00:12:21,200 --> 00:12:23,890 I am pasting it right here. 173 00:12:24,080 --> 00:12:25,580 And good news. 174 00:12:25,580 --> 00:12:27,350 We have the exact same arguments. 175 00:12:27,350 --> 00:12:29,080 We don't have to replace anything. 176 00:12:29,330 --> 00:12:33,260 We choose a slope of 0.2 and in place. 177 00:12:33,530 --> 00:12:35,270 Next step the next step. 178 00:12:35,300 --> 00:12:42,170 As you might guess and I strongly recommend this game to try to type the next module in the architecture 179 00:12:42,440 --> 00:12:53,260 before me the next module is going to be end that and a new convolution of course comes to the parenthesis. 180 00:12:53,330 --> 00:12:57,140 And now of course you guess what we're going to input and what we're going to output. 181 00:12:57,230 --> 00:13:04,730 We're going to input what was output by the previous convolution which was 128 feature maps. 182 00:13:04,730 --> 00:13:12,020 That becomes the new input of this new convolution and for the output Well we increase this time. 183 00:13:12,050 --> 00:13:14,800 The number of feature maps multiplied by two. 184 00:13:14,960 --> 00:13:22,330 So we're going to get the new output of two hundred and fifty six feature maps are right. 185 00:13:22,490 --> 00:13:33,080 And then again we choose a kernel of size 4 as tried to and a padding of one and again no absolutely 186 00:13:33,080 --> 00:13:37,700 no bias bicycle's false perfect come up. 187 00:13:37,730 --> 00:13:43,320 Next step the next step is to guess what well to apply a budget normalization. 188 00:13:43,370 --> 00:13:48,320 So we take our N and module that batch norm to D. 189 00:13:48,560 --> 00:13:53,990 And as arguments we are going to input the number of new feature maps that we got and that we want to 190 00:13:53,990 --> 00:13:55,540 batch normalize. 191 00:13:55,670 --> 00:13:59,500 And since what we've got is 256 new feature maps. 192 00:13:59,570 --> 00:14:08,840 Well we're going to input here 256 to batch normalize these new 256 feature maps perfect next step the 193 00:14:08,840 --> 00:14:10,690 leaky renew again. 194 00:14:10,730 --> 00:14:15,330 So in that leaky reglue there it is. 195 00:14:15,500 --> 00:14:19,140 And that's the same I don't know why I'm typing this again. 196 00:14:19,280 --> 00:14:24,200 I just need to copy and paste it and there we go. 197 00:14:24,200 --> 00:14:26,140 Now another convolution again. 198 00:14:26,180 --> 00:14:31,150 So this time let's not miss it and copy this copy. 199 00:14:32,240 --> 00:14:41,800 Based So this time we have 256 feature maps for the input and we double it for the output. 200 00:14:41,870 --> 00:14:48,980 So we get five hundred and twelve new feature maps in the output of this new convolution and then same 201 00:14:49,090 --> 00:14:54,420 kernel size for a stride of two and a pairing of 1 and No bias. 202 00:14:54,500 --> 00:14:59,420 Great then our batched on again and copying this. 203 00:14:59,430 --> 00:15:02,200 I'm basing it just below. 204 00:15:02,240 --> 00:15:11,270 I am not forgetting to replace the 256 previous feature maps by the new 512 new feature maps that we 205 00:15:11,270 --> 00:15:12,650 have to batch norm. 206 00:15:12,680 --> 00:15:18,940 Then we apply our leaky reglue with the same parameters. 207 00:15:19,040 --> 00:15:22,410 So I just need to copy this pasted here. 208 00:15:22,570 --> 00:15:25,250 I hope you are doing all this faster than me. 209 00:15:25,330 --> 00:15:28,290 All right then almost over. 210 00:15:28,300 --> 00:15:32,010 We have one final convolution to add. 211 00:15:32,050 --> 00:15:34,660 This is the last one and pasting it here. 212 00:15:34,960 --> 00:15:42,880 And so we're going to get now 512 feature maps of the input and now the question is since this is the 213 00:15:42,880 --> 00:15:47,160 last convolution what is going to be the output. 214 00:15:47,170 --> 00:15:49,860 Please don't answer 1024. 215 00:15:49,910 --> 00:15:51,750 We're done with the convolutions here. 216 00:15:51,850 --> 00:15:54,700 We stop playing the game of doubling the feature maps. 217 00:15:54,700 --> 00:15:59,190 No this time we have to specify the final output. 218 00:15:59,230 --> 00:16:01,060 Therefore the discriminator. 219 00:16:01,130 --> 00:16:06,680 And so if you understood the role of the discriminator you should be able to guess the output here. 220 00:16:06,850 --> 00:16:10,940 Well I'm going to tell you right now it is actually one. 221 00:16:11,210 --> 00:16:11,780 One. 222 00:16:11,800 --> 00:16:17,990 That's because the discriminator is returning a discriminating number and number between 0 and 1. 223 00:16:18,150 --> 00:16:23,910 So that's a simple vector of one dimension containing this number and therefore that's why we have a 224 00:16:23,920 --> 00:16:29,920 1 here and then just a little trap you can not really guess that but we're going to keep a curl size 225 00:16:29,920 --> 00:16:37,600 of 4 but then we're going to choose a stride of one and a padding of 0 and then that is the same no 226 00:16:37,600 --> 00:16:38,890 bias. 227 00:16:38,890 --> 00:16:41,140 All right we're almost done. 228 00:16:41,170 --> 00:16:45,670 We have one final thing to do for this architecture. 229 00:16:45,880 --> 00:16:49,040 If you look at what we did for the generator. 230 00:16:49,150 --> 00:16:52,850 Well you can see that after the last inverse convolution. 231 00:16:52,960 --> 00:16:58,480 Well we applied an activation function again to break the new narrative and we going to do the same 232 00:16:58,480 --> 00:17:00,030 for the discriminator. 233 00:17:00,040 --> 00:17:03,540 But this time it's not going to be a hyperbolic tangent. 234 00:17:03,850 --> 00:17:06,050 According to you what is it going to be. 235 00:17:06,070 --> 00:17:10,920 It's actually a very classic activation function to use at the end of a neural network. 236 00:17:10,920 --> 00:17:18,010 It is the sigmoid function and if you went deep into the theory of the deep convolutional Ganns and 237 00:17:18,010 --> 00:17:20,520 especially the part related to the discriminator. 238 00:17:20,650 --> 00:17:26,170 Well you would understand that the sigmoid function is the best choice because it returns some values 239 00:17:26,170 --> 00:17:32,550 between 0 and 1 and for the discriminator we actually want to be between 0 and 1. 240 00:17:32,590 --> 00:17:33,590 Why is that. 241 00:17:33,790 --> 00:17:40,590 Well the answer of that question is in the name of the discriminator it's for discriminating reason. 242 00:17:40,840 --> 00:17:48,550 The principle of the discriminator is that zero corresponds to the rejection of the image and one corresponds 243 00:17:48,550 --> 00:17:50,420 to the acceptance of the image. 244 00:17:50,560 --> 00:17:54,880 So 0 means we reject the image and one means we accept the image. 245 00:17:54,880 --> 00:18:02,100 Therefore the name discriminator and so basically we want to be between 0 and 1 to do some discrimination. 246 00:18:02,650 --> 00:18:05,660 And that's why the output is actually a value between 0 and 1. 247 00:18:05,800 --> 00:18:11,980 Because then using classic threshold technique well for the values below 0.5 we will consider it as 248 00:18:11,980 --> 00:18:16,790 zeros who will reject the image and put the values above 0.5. 249 00:18:16,800 --> 00:18:20,730 We will consider it as one and we will accept the image. 250 00:18:20,890 --> 00:18:27,790 So that's very important to understand and therefore the sigmoid function that not only break down in 251 00:18:27,790 --> 00:18:35,260 the area but also returns a value between 0 and 1 is the optimal choice for the activation function 252 00:18:35,410 --> 00:18:38,580 at the level of the output of the discriminator. 253 00:18:38,610 --> 00:18:45,130 All right so if you're convinced we're going to get R and then module and then that and then we get 254 00:18:45,250 --> 00:18:47,900 our sigmoid activation function. 255 00:18:48,070 --> 00:18:54,700 So it's an object of the sigmoid class which will represent the sigmoid activation function itself and 256 00:18:54,970 --> 00:18:58,800 we don't have any argument to input. 257 00:18:58,840 --> 00:19:04,210 So congratulations you are done with the architecture of the second brain. 258 00:19:04,240 --> 00:19:06,490 You have to create for this sequence. 259 00:19:06,560 --> 00:19:08,850 So now are DC again had the two brains. 260 00:19:08,860 --> 00:19:13,310 Let's make the forward function to propagate the signal inside the second brain. 261 00:19:13,390 --> 00:19:17,200 We'll make it in the next tutorial and until then enjoy computer vision. 27213