| 1 | |
| 00:00:00,530 --> 00:00:01,090 | |
| I guess. | |
| 2 | |
| 00:00:01,110 --> 00:00:05,220 | |
| And welcome to chapter eight point five where we talk about what one encoding. | |
| 3 | |
| 00:00:05,520 --> 00:00:11,900 | |
| So as we saw before we did some transmissions on the extreme and X deciliter that's so image data. | |
| 4 | |
| 00:00:11,970 --> 00:00:15,520 | |
| Now what about our label little white green and white test. | |
| 5 | |
| 00:00:15,780 --> 00:00:16,530 | |
| Well let's find out. | |
| 6 | |
| 00:00:16,530 --> 00:00:19,790 | |
| So let's recap what we did on the image data. | |
| 7 | |
| 00:00:19,860 --> 00:00:23,880 | |
| We added a four dimension to go from this to this. | |
| 8 | |
| 00:00:23,880 --> 00:00:32,070 | |
| We changed it to flow to two data type and we normalized it between 0 and 1 by dividing by 255. | |
| 9 | |
| 00:00:32,450 --> 00:00:33,960 | |
| But what do we do to label that. | |
| 10 | |
| 00:00:33,960 --> 00:00:35,450 | |
| Now that's true. | |
| 11 | |
| 00:00:35,940 --> 00:00:39,730 | |
| So the label is basically in this form for white train. | |
| 12 | |
| 00:00:39,750 --> 00:00:47,850 | |
| It's going to be a matrix that is 60000 that has 60000 elements and each element indicates a class label. | |
| 13 | |
| 00:00:47,890 --> 00:00:57,360 | |
| So for x for y treatment which is a 28 if this element in white and extreme is going to be 28 by 28 | |
| 14 | |
| 00:00:57,840 --> 00:01:00,900 | |
| and this zero corresponds to its label here. | |
| 15 | |
| 00:01:01,150 --> 00:01:04,710 | |
| However Harris does not use label data like this. | |
| 16 | |
| 00:01:04,890 --> 00:01:06,820 | |
| It needs it to be a hot one encoded. | |
| 17 | |
| 00:01:06,900 --> 00:01:09,850 | |
| And what does that look like that looks like this. | |
| 18 | |
| 00:01:10,080 --> 00:01:11,640 | |
| So we have labels here. | |
| 19 | |
| 00:01:12,030 --> 00:01:20,840 | |
| And instead of having a for being represented here it's basically a matrix to has 10 columns now. | |
| 20 | |
| 00:01:21,270 --> 00:01:24,900 | |
| So instead of having no call them so effectively want to call them. | |
| 21 | |
| 00:01:24,930 --> 00:01:26,890 | |
| Sorry sixty dozen columns. | |
| 22 | |
| 00:01:27,000 --> 00:01:34,580 | |
| It has 10 columns here and 60000 rows and each column is a row. | |
| 23 | |
| 00:01:34,590 --> 00:01:39,390 | |
| Sorry has basically a 1 0 0 indicating which label it is. | |
| 24 | |
| 00:01:39,690 --> 00:01:42,900 | |
| So imagine we have this being transformed. | |
| 25 | |
| 00:01:42,900 --> 00:01:44,090 | |
| Sorry let's look at this. | |
| 26 | |
| 00:01:44,090 --> 00:01:45,520 | |
| This is a td rule here. | |
| 27 | |
| 00:01:45,810 --> 00:01:48,340 | |
| Being transformed into this. | |
| 28 | |
| 00:01:48,370 --> 00:01:55,520 | |
| So instead of having this rule before what one coding makes it into this I hope you understand clearly | |
| 29 | |
| 00:01:55,530 --> 00:01:56,600 | |
| so we're going to do this now. | |
| 30 | |
| 00:01:56,640 --> 00:01:58,770 | |
| You know I write in my book. | |
| 31 | |
| 00:01:59,650 --> 00:01:59,960 | |
| OK. | |
| 32 | |
| 00:01:59,970 --> 00:02:06,310 | |
| So Step three is a hot one including a full y labels and to do this we basically use any utilities that | |
| 33 | |
| 00:02:06,310 --> 00:02:10,280 | |
| are imported from the utilities and all this stuff here. | |
| 34 | |
| 00:02:10,290 --> 00:02:12,600 | |
| It just sends it to categorical. | |
| 35 | |
| 00:02:12,600 --> 00:02:17,210 | |
| That is how Cara's calls dysfunction Hotpoint including two categorical. | |
| 36 | |
| 00:02:17,460 --> 00:02:18,550 | |
| So we have Whitopia. | |
| 37 | |
| 00:02:18,620 --> 00:02:23,160 | |
| It's equal to utilities not to categorical and just put the wager in here. | |
| 38 | |
| 00:02:23,310 --> 00:02:25,140 | |
| And that transforms it. | |
| 39 | |
| 00:02:25,140 --> 00:02:27,220 | |
| So let's take a look at how this actually looks. | |
| 40 | |
| 00:02:27,220 --> 00:02:28,510 | |
| So let's run this here. | |
| 41 | |
| 00:02:28,820 --> 00:02:30,120 | |
| So no why train. | |
| 42 | |
| 00:02:30,270 --> 00:02:32,420 | |
| Let's look at the first rule in waitron. | |
| 43 | |
| 00:02:32,830 --> 00:02:40,700 | |
| It's this and basically can see one two three four five six seven eight nine ten elements. | |
| 44 | |
| 00:02:40,920 --> 00:02:45,270 | |
| And with this one this looks like the nine and fifth element here. | |
| 45 | |
| 00:02:45,300 --> 00:02:46,920 | |
| So this is number five. | |
| 46 | |
| 00:02:46,920 --> 00:02:51,720 | |
| So the first element in overtreating data is number five. | |
| 47 | |
| 00:02:52,110 --> 00:02:54,710 | |
| So now let's move on to creating a model. | |