All language subtitles for 3. About this course - Copy

af Afrikaans
ak Akan
sq Albanian
am Amharic
ar Arabic
hy Armenian
az Azerbaijani
eu Basque
be Belarusian
bem Bemba
bn Bengali
bh Bihari
bs Bosnian
br Breton
bg Bulgarian
km Cambodian
ca Catalan
chr Cherokee
ny Chichewa
zh-CN Chinese (Simplified)
zh-TW Chinese (Traditional)
co Corsican
hr Croatian
cs Czech
da Danish
nl Dutch
en English
eo Esperanto
et Estonian
ee Ewe
fo Faroese
tl Filipino
fi Finnish
fr French
fy Frisian
gaa Ga
gl Galician
ka Georgian
de German
el Greek
gn Guarani
gu Gujarati
ht Haitian Creole
ha Hausa
haw Hawaiian
iw Hebrew
hi Hindi
hu Hungarian
is Icelandic
ig Igbo
id Indonesian
ia Interlingua
ga Irish
it Italian
ja Japanese
jw Javanese
kn Kannada
kk Kazakh
rw Kinyarwanda
rn Kirundi
kg Kongo
ko Korean
kri Krio (Sierra Leone)
ku Kurdish
ckb Kurdish (Soranî)
ky Kyrgyz
lo Laothian
la Latin
lv Latvian
ln Lingala
lt Lithuanian
loz Lozi
lg Luganda
ach Luo
mk Macedonian
mg Malagasy
ms Malay
ml Malayalam
mt Maltese
mi Maori
mr Marathi
mfe Mauritian Creole
mo Moldavian
mn Mongolian
sr-ME Montenegrin
ne Nepali
pcm Nigerian Pidgin
nso Northern Sotho
no Norwegian
nn Norwegian (Nynorsk)
oc Occitan
or Oriya
om Oromo
ps Pashto
fa Persian Download
pl Polish
pt-BR Portuguese (Brazil)
pt-PT Portuguese (Portugal)
pa Punjabi
qu Quechua
ro Romanian
rm Romansh
nyn Runyakitara
ru Russian
gd Scots Gaelic
sr Serbian
sh Serbo-Croatian
st Sesotho
tn Setswana
crs Seychellois Creole
sn Shona
sd Sindhi
si Sinhalese
sk Slovak
sl Slovenian
so Somali
es Spanish
es-419 Spanish (Latin American)
su Sundanese
sw Swahili
sv Swedish
tg Tajik
ta Tamil
tt Tatar
te Telugu
th Thai
ti Tigrinya
to Tonga
lua Tshiluba
tum Tumbuka
tr Turkish
tk Turkmen
tw Twi
ug Uighur
uk Ukrainian
ur Urdu
uz Uzbek
vi Vietnamese
cy Welsh
wo Wolof
xh Xhosa
yi Yiddish
yo Yoruba
zu Zulu
Would you like to inspect the original subtitles? These are the user uploaded subtitles that are being translated: WEBVTT 00:00.450 --> 00:03.180 So while in the open Zeevi in Python. 00:03.420 --> 00:07.950 Well Python is becoming more and more popular and that's because it's one of the easiest languages for 00:07.950 --> 00:09.000 beginners. 00:09.000 --> 00:14.490 It allows us to focus on building complex computer vision apps without being bogged down by the intricacies 00:14.490 --> 00:15.850 of the language itself. 00:16.100 --> 00:18.270 And I'm looking at you C++. 00:18.270 --> 00:23.370 However Python is still extremely powerful especially for science and machine learning applications 00:23.850 --> 00:27.420 which are an essential part of the can be division world. 00:27.420 --> 00:33.240 Finally it allows us to store images and non-payers which allows us to do some very powerful operations 00:33.240 --> 00:34.490 quite easily. 00:34.950 --> 00:40.380 So let's take a quick look at exactly what you'll be learning on this course so fiercely given the current 00:40.380 --> 00:41.610 state of can be division. 00:41.610 --> 00:47.790 I try to give you an excellent foundation that exposes you to all key areas of computer vision. 00:47.790 --> 00:53.940 We start off by doing the basics where we get into some simple image manipulations and segmentation. 00:53.940 --> 00:59.310 We then implement some basic object detection followed by feature detection and call and people detection 00:59.310 --> 01:00.400 as well. 01:00.450 --> 01:03.740 We don't take a look at this analysis and filters. 01:03.870 --> 01:09.240 After that we go into some basic machine learning in computer vision and then we get into some more 01:09.480 --> 01:12.000 motion analysis and object tracking. 01:12.000 --> 01:17.760 I've also included a short mini project based on competition of photography and then we wrap up the 01:17.760 --> 01:22.590 course where I give you some advice and resources on how to become an expert and can be division. 01:22.800 --> 01:28.530 I also show you some of the latest research areas and also give you some very cool startup ideas that 01:28.530 --> 01:29.470 involve computer. 01:29.490 --> 01:32.990 Computer vision and best of all in discourse. 01:33.000 --> 01:40.620 You get to implement almost 50 different computer vision exercises and implement 12 very fun many projects. 01:40.770 --> 01:45.990 So it should come as no surprise that this is a very practical course where we're going to spend more 01:45.990 --> 01:47.660 than half of our time coding. 01:47.970 --> 01:53.240 However before we dive into it could always teach the theory first before hand unless I'm using the 01:53.240 --> 01:56.310 code to actually teach at that topic as well. 01:56.310 --> 02:01.340 And the is always explained line by line except in cases where it becomes a bit redundant. 02:01.500 --> 02:05.200 Or the theory at hand is a bit too complex for this group of discourse. 02:05.760 --> 02:08.480 So I may have mentioned 12 mini projects before. 02:08.700 --> 02:11.100 So what exactly are these mini projects. 02:11.100 --> 02:12.330 Let's take a look. 02:12.330 --> 02:17.940 So here they are in all their glory all 12 many projects on one slide so fiercely. 02:17.960 --> 02:22.200 You're getting to make an awesome Live sketch of yourself using a webcam. 02:22.200 --> 02:27.210 We don't get to implement is simple ship matching project followed by an app that actually comes a number 02:27.210 --> 02:29.650 of circles and ellipses in an image. 02:29.880 --> 02:30.830 We don't move on to. 02:30.910 --> 02:35.610 We're finding Waldo projec followed by a simple object detection project. 02:35.880 --> 02:40.200 You don't get to implement fi's pedestrian encored detection. 02:40.200 --> 02:45.960 After that you get to implement a very cool life swapping up here where you can play a Donald Trump 02:45.960 --> 02:50.010 or Kim Kardashian's or anyone else's face in real time. 02:50.370 --> 02:55.830 And then we implement a simple human detection up after which you get to make a basic machine learning 02:55.830 --> 02:58.800 app that actually understands handwritten digits. 02:58.800 --> 03:04.410 This is then followed by a face recognition app and you don't get to implement a simple ball trucking 03:04.410 --> 03:07.130 up and I know this isn't a ball it's actually a clock. 03:07.140 --> 03:09.740 I couldn't find my ball but that's OK. 03:10.050 --> 03:15.990 And lastly we get to do a simple photo restoration app we can remove this line from his photo right 03:15.990 --> 03:16.670 here. 03:17.070 --> 03:21.990 So you're definitely getting a lot of practical experience making computer vision applications so I 03:21.990 --> 03:24.550 really hope you enjoy doing these projects. 03:25.710 --> 03:28.550 The requirements for the scores are actually pretty low. 03:28.770 --> 03:32.940 Basic programming would actually be very helpful as well as exposure to non-pay. 03:32.970 --> 03:36.700 However it's not needed as I actually go through the code line by line. 03:37.050 --> 03:41.670 Secondly a high school level math would actually be very good to have to appreciate some of the high 03:41.670 --> 03:43.950 level concepts that we're implementing. 03:43.950 --> 03:49.530 And also you need to have a webcam to implement a lot of for many projects as well as some of the example 03:49.530 --> 03:50.650 code. 03:50.670 --> 03:55.140 Now we're going to install Pitre an open C.V right after in the next section. 03:55.230 --> 04:00.570 However I'll just point out that I used the Anaconda package solution and that allows me to use Pitre 04:00.570 --> 04:05.670 notebooks which are excellent for teaching since it since it allows us to use and or uncovered in court 04:05.670 --> 04:07.320 blocks. 04:07.320 --> 04:12.360 Now there is some unfortunate news regarding the latest version of Open C-v which is true point 1. 04:12.600 --> 04:18.360 It unfortunately no longer support some important functions such as swift and Souf which I use for object 04:18.360 --> 04:19.380 detection. 04:19.500 --> 04:23.380 So I would recommend you install 2.4 one tree instead. 04:23.670 --> 04:29.300 However that said there are some object tracking techniques that aren't supported in 2.4 and tree. 04:29.340 --> 04:34.510 So depending on what's your priority you can choose which version you would like to install accordingly. 04:35.630 --> 04:37.870 So who exactly is this course for. 04:38.090 --> 04:42.830 Well I've designed this course to suit a wide number of people starting from beginners who just have 04:42.830 --> 04:47.690 an interest in computer vision or even software developers and engineers looking to strengthen their 04:47.690 --> 04:53.360 job skills as well as college and university students looking to get a head start in the computer vision 04:53.360 --> 04:59.600 projects and research also startup founders who wish to use law as some sort of computer vision component 04:59.600 --> 05:00.930 to their companies. 05:01.070 --> 05:07.700 And finally hobbyists who just want to build some fun computer vision project using a Raspberry Pi perhaps. 05:07.700 --> 05:13.440 So let's begin our exciting journey into the world of computer vision using open C-v in Python. 7289

Can't find what you're looking for?
Get subtitles in any language from opensubtitles.com, and translate them here.