Many transcript corrections.

Author: mikeckennedy

transcripts/003-Pyramid-Web Framework-Chris-McDonough.txt

@@ -718,7 +718,7 @@
 
 00:27:59 So so if you if you look inside of easy install dot PTH thing, Philip Eby took serious advantage of that, you know, when he wrote setup tools.
 
-00:28:08 And it's got it's got some some some Python, inscrutable Python in there that does some fun stuff.
+00:28:08 And it's got it's got some some Python, inscrutable Python in there that does some fun stuff.
 
 00:28:16 I think it sets up package resources on there.
 
@@ -774,7 +774,7 @@
 
 00:29:29 And I mean, I think I would like to take credit for most of that.
 
-00:29:33 But the the original version of that was a blatant ripoff of something that was in Flask, actually.
+00:29:33 But the original version of that was a blatant ripoff of something that was in Flask, actually.
 
 00:29:41 And in turn, that thing that was in Flask was a blatant ripoff of something that was in Django.
 
@@ -900,7 +900,7 @@
 
 00:34:42 You know what I mean?
 
-00:34:43 It was just something, you know, just some some amount of money I could say went towards that.
+00:34:43 It was just something, you know, just some amount of money I could say went towards that.
 
 00:34:47 And I could I could be paid.
 
@@ -936,7 +936,7 @@
 
 00:35:29 But but I think it's getting a lot better.
 
-00:35:32 And I think, you know, with these frameworks, you know, the the web stuff, at least at least
+00:35:32 And I think, you know, with these frameworks, you know, the web stuff, at least at least
 
 00:35:37 the web portion of the world, I think, is pretty much OK on Python 3.
 
@@ -968,7 +968,7 @@
 
 00:36:14 And so I think just from from both the standpoint of deployment, like you just said, and the
 
-00:36:21 standpoint of development where you need some library, you know, some some library at this
+00:36:21 standpoint of development where you need some library, you know, some library at this
 
 00:36:26 point, you you're going to you're going to run it.
 
@@ -978,7 +978,7 @@
 
 00:36:34 But maybe not, you know, maybe not if you're if it's really vanilla.
 
-00:36:40 You know, you don't need some some Excel import libraries.
+00:36:40 You know, you don't need some Excel import libraries.
 
 00:36:44 I mean, but people need that stuff.
 
@@ -1306,7 +1306,7 @@
 
 00:44:33 That's great.
 
-00:44:33 So some people are coming along to pick up the the effort.
+00:44:33 So some people are coming along to pick up the effort.
 
 00:44:36 Yeah.
 

transcripts/003-Pyramid-Web Framework-Chris-McDonough.vtt

@@ -1087,7 +1087,7 @@ Oh, boy.
 So so if you if you look inside of easy install dot PTH thing, Philip Eby took serious advantage of that, you know, when he wrote setup tools.
 
 00:28:08.700 --> 00:28:16.560
-And it's got it's got some some some Python, inscrutable Python in there that does some fun stuff.
+And it's got it's got some some Python, inscrutable Python in there that does some fun stuff.
 
 00:28:16.560 --> 00:28:19.500
 I think it sets up package resources on there.
@@ -1171,7 +1171,7 @@ There is.
 And I mean, I think I would like to take credit for most of that.
 
 00:29:33.980 --> 00:29:41.800
-But the the original version of that was a blatant ripoff of something that was in Flask, actually.
+But the original version of that was a blatant ripoff of something that was in Flask, actually.
 
 00:29:41.800 --> 00:29:46.500
 And in turn, that thing that was in Flask was a blatant ripoff of something that was in Django.
@@ -1360,7 +1360,7 @@ And I was, you know, it was it really, really wouldn't matter how much it was.
 You know what I mean?
 
 00:34:43.560 --> 00:34:47.880
-It was just something, you know, just some some amount of money I could say went towards that.
+It was just something, you know, just some amount of money I could say went towards that.
 
 00:34:47.880 --> 00:34:49.760
 And I could I could be paid.
@@ -1414,7 +1414,7 @@ So I'm, you know, whatever.
 But but I think it's getting a lot better.
 
 00:35:32.520 --> 00:35:37.280
-And I think, you know, with these frameworks, you know, the the web stuff, at least at least
+And I think, you know, with these frameworks, you know, the web stuff, at least at least
 
 00:35:37.280 --> 00:35:42.260
 the web portion of the world, I think, is pretty much OK on Python 3.
@@ -1462,7 +1462,7 @@ Yeah.
 And so I think just from from both the standpoint of deployment, like you just said, and the
 
 00:36:21.380 --> 00:36:26.760
-standpoint of development where you need some library, you know, some some library at this
+standpoint of development where you need some library, you know, some library at this
 
 00:36:26.760 --> 00:36:29.100
 point, you you're going to you're going to run it.
@@ -1477,7 +1477,7 @@ going to gnash your teeth and and whatever.
 But maybe not, you know, maybe not if you're if it's really vanilla.
 
 00:36:40.460 --> 00:36:44.140
-You know, you don't need some some Excel import libraries.
+You know, you don't need some Excel import libraries.
 
 00:36:44.140 --> 00:36:46.040
 I mean, but people need that stuff.
@@ -1969,7 +1969,7 @@ I don't know.
 That's great.
 
 00:44:33.960 --> 00:44:36.680
-So some people are coming along to pick up the the effort.
+So some people are coming along to pick up the effort.
 
 00:44:36.680 --> 00:44:37.420
 Yeah.

transcripts/011-pyimagesearch.txt

@@ -568,19 +568,19 @@
 
 00:18:06 Right, sure.
 
-00:18:07 And so how much of this exists in external libraries like Scikit-learn or OpenCV or something like this?
+00:18:07 And so how much of this exists in external libraries like scikit-learn or OpenCV or something like this?
 
 00:18:16 And how much of that is like I've got to create that system for myself when I'm getting started based on my application?
 
 00:18:24 So OpenCV does include some machine learning components, but I really don't recommend that people use them just because they're a little finicky and they're not that fun to use.
 
-00:18:35 And especially in the Python ecosystem, you have Scikit-learn.
+00:18:35 And especially in the Python ecosystem, you have scikit-learn.
 
 00:18:38 So you should be defaulting to that.
 
-00:18:42 And to give an example, I wrote my entire dissertation, gathered all the examples using OpenCV and Scikit-learn.
+00:18:42 And to give an example, I wrote my entire dissertation, gathered all the examples using OpenCV and scikit-learn.
 
-00:18:52 I took the results that OpenCV was giving me and I passed them on to the machine learning methods and Scikit-learn.
+00:18:52 I took the results that OpenCV was giving me and I passed them on to the machine learning methods and scikit-learn.
 
 00:18:59 Right.
 
@@ -1374,7 +1374,7 @@
 
 00:45:48 Yeah, so I mentioned two of my favorites already.
 
-00:45:51 The first one is Scikit-learn for machine learning.
+00:45:51 The first one is scikit-learn for machine learning.
 
 00:45:54 The other is Scikit-image for computer vision.
 

transcripts/011-pyimagesearch.vtt

@@ -925,7 +925,7 @@ So if you have a really good quantification of an image, it's a lot easier for t
 Right, sure.
 
 00:18:07.520 --> 00:18:16.340
-And so how much of this exists in external libraries like Scikit-learn or OpenCV or something like this?
+And so how much of this exists in external libraries like scikit-learn or OpenCV or something like this?
 
 00:18:16.340 --> 00:18:23.700
 And how much of that is like I've got to create that system for myself when I'm getting started based on my application?
@@ -934,16 +934,16 @@ And how much of that is like I've got to create that system for myself when I'm
 So OpenCV does include some machine learning components, but I really don't recommend that people use them just because they're a little finicky and they're not that fun to use.
 
 00:18:35.580 --> 00:18:38.820
-And especially in the Python ecosystem, you have Scikit-learn.
+And especially in the Python ecosystem, you have scikit-learn.
 
 00:18:38.820 --> 00:18:41.920
 So you should be defaulting to that.
 
 00:18:42.460 --> 00:18:52.240
-And to give an example, I wrote my entire dissertation, gathered all the examples using OpenCV and Scikit-learn.
+And to give an example, I wrote my entire dissertation, gathered all the examples using OpenCV and scikit-learn.
 
 00:18:52.240 --> 00:18:59.140
-I took the results that OpenCV was giving me and I passed them on to the machine learning methods and Scikit-learn.
+I took the results that OpenCV was giving me and I passed them on to the machine learning methods and scikit-learn.
 
 00:18:59.140 --> 00:18:59.760
 Right.
@@ -2134,7 +2134,7 @@ So, you got any favorites?
 Yeah, so I mentioned two of my favorites already.
 
 00:45:51.000 --> 00:45:54.020
-The first one is Scikit-learn for machine learning.
+The first one is scikit-learn for machine learning.
 
 00:45:54.020 --> 00:45:57.340
 The other is Scikit-image for computer vision.

transcripts/020_interactive_python.txt

@@ -1742,7 +1742,7 @@
 
 00:44:46 PyCharm.
 
-00:44:47 Pie charm.
+00:44:47 PyCharm.
 
 00:44:48 Oh, right on.
 

transcripts/020_interactive_python.vtt

@@ -2647,7 +2647,7 @@ Uh, favorite editor, right?
 Uh, PyCharm.
 
 00:44:47.940 --> 00:44:48.700
-Pie charm.
+PyCharm.
 
 00:44:48.700 --> 00:44:49.260
 Oh, right on.

transcripts/022_cpython.txt

@@ -366,7 +366,7 @@
 
 00:13:54 Yeah.
 
-00:13:55 So on the Web site with all the videos, I actually show the the files that they reference.
+00:13:55 So on the Web site with all the videos, I actually show the files that they reference.
 
 00:14:00 But really, the core file that I keep on going back to what you're saying is in Python slash C eval dot C.
 
@@ -1206,7 +1206,7 @@
 
 00:45:57 So I've seen people with three, four, maybe five people kind of talking about stuff.
 
-00:46:01 So that's sort of the the tool that simulates a a kind of personal interaction with the visualization.
+00:46:01 So that's sort of the tool that simulates a a kind of personal interaction with the visualization.
 
 00:46:10 Then you have another form that is sort of almost a dashboard of many learners, right?
 

transcripts/022_cpython.vtt

@@ -553,7 +553,7 @@ And then there's a bunch of details.
 Yeah.
 
 00:13:55.580 --> 00:14:00.500
-So on the Web site with all the videos, I actually show the the files that they reference.
+So on the Web site with all the videos, I actually show the files that they reference.
 
 00:14:00.500 --> 00:14:08.520
 But really, the core file that I keep on going back to what you're saying is in Python slash C eval dot C.
@@ -1816,7 +1816,7 @@ So because you see everyone's cursor.
 So I've seen people with three, four, maybe five people kind of talking about stuff.
 
 00:46:01.980 --> 00:46:10.780
-So that's sort of the the tool that simulates a a kind of personal interaction with the visualization.
+So that's sort of the tool that simulates a a kind of personal interaction with the visualization.
 
 00:46:10.780 --> 00:46:17.560
 Then you have another form that is sort of almost a dashboard of many learners, right?

transcripts/028_profiling.txt

@@ -350,7 +350,7 @@
 
 00:23:45 pi pi is super interesting to me we're going to talk more about how how you well you chose not to use that but
 
-00:23:51 you know on show 21 we had macha from the the pi pi oh yeah i have it up right now i'm gonna watch it later
+00:23:51 you know on show 21 we had macha from the pi pi oh yeah i have it up right now i'm gonna watch it later
 
 00:23:59 yeah i'm so excited yeah that was super interesting and we talked a little bit about
 
@@ -392,7 +392,7 @@
 
 00:25:54 or maybe you're talking about a file system or you know a web service call something like this
 
-00:26:00 right and you had some good advice on that i thought yeah i definitely uh basically the the c python
+00:26:00 right and you had some good advice on that i thought yeah i definitely uh basically the c python
 
 00:26:07 c python's jill the global interpreter lock is very uh precluding you can't you can't do multiple
 
@@ -412,7 +412,7 @@
 
 00:26:59 the data off a url or things like that that would that's great for python's threading but otherwise
 
-00:27:05 you don't really want to be using it right basically the the built-in functions that wait on io they
+00:27:05 you don't really want to be using it right basically the built-in functions that wait on io they
 
 00:27:12 release the global interpreter lock and so that frees up your other code to keep on running more or less
 
@@ -422,7 +422,7 @@
 
 00:27:28 a lot of cool advances in python 3 around this type of parallelism right and they just added async and
 
-00:27:37 await the the the new keywords to uh is that three five i think it was right yeah just three fives just
+00:27:37 await the the new keywords to uh is that three five i think it was right yeah just three fives just
 
 00:27:43 came out like two days yeah two days ago so yeah i mean that's super new but these are the types
 
@@ -470,7 +470,7 @@
 
 00:29:55 and that way it's much better documented in the code yeah that's great advice so let's maybe talk
 
-00:30:02 about how you solve your problem after you like what was the problem what did you find out the the issue
+00:30:02 about how you solve your problem after you like what was the problem what did you find out the issue
 
 00:30:08 to be and then how do you solve it yeah so what we were doing in this code is we were taking gigabytes
 
@@ -516,7 +516,7 @@
 
 00:32:34 pro program ran orders of magnitude faster that's awesome and you know what i think i really love about
 
-00:32:43 this two things i love about it one is you know you talked a little bit about the the solution on the
+00:32:43 this two things i love about it one is you know you talked a little bit about the solution on the
 
 00:32:49 blog and it's like i don't know nine lines of code no yes that's it's python yeah it's so awesome and
 
@@ -582,7 +582,7 @@
 
 00:36:26 something right and it was doing crazy math like wavelet decomposition and all sorts of stuff okay this is
 
-00:36:33 like like i was saying earlier it's the verge of understanding what we're doing right yeah and it
+00:36:33 like i was saying earlier it's the verge of understanding what we're doing right yeah and it
 
 00:36:39 was too slow the first time we ran like oh no please don't make me try to optimize a wavelet
 

transcripts/028_profiling.vtt

@@ -820,7 +820,7 @@ that you talked about that was interesting was io like i gave you my example of
 or maybe you're talking about a file system or you know a web service call something like this
 
 00:26:00.600 --> 00:26:07.780
-right and you had some good advice on that i thought yeah i definitely uh basically the the c python
+right and you had some good advice on that i thought yeah i definitely uh basically the c python
 
 00:26:07.780 --> 00:26:16.020
 c python's jill the global interpreter lock is very uh precluding you can't you can't do multiple
@@ -850,7 +850,7 @@ threading to do things like you know file system you know expensive io tasks lik
 the data off a url or things like that that would that's great for python's threading but otherwise
 
 00:27:05.200 --> 00:27:12.740
-you don't really want to be using it right basically the the built-in functions that wait on io they
+you don't really want to be using it right basically the built-in functions that wait on io they
 
 00:27:12.740 --> 00:27:18.620
 release the global interpreter lock and so that frees up your other code to keep on running more or less
@@ -865,7 +865,7 @@ i mean as long as everything else is not the bottleneck right and you know we've
 a lot of cool advances in python 3 around this type of parallelism right and they just added async and
 
 00:27:37.600 --> 00:27:43.960
-await the the the new keywords to uh is that three five i think it was right yeah just three fives just
+await the the new keywords to uh is that three five i think it was right yeah just three fives just
 
 00:27:43.960 --> 00:27:49.100
 came out like two days yeah two days ago so yeah i mean that's super new but these are the types
@@ -937,7 +937,7 @@ support for i'm greatly annotating the regex so if you have to use it you can be
 and that way it's much better documented in the code yeah that's great advice so let's maybe talk
 
 00:30:02.260 --> 00:30:08.220
-about how you solve your problem after you like what was the problem what did you find out the the issue
+about how you solve your problem after you like what was the problem what did you find out the issue
 
 00:30:08.220 --> 00:30:15.940
 to be and then how do you solve it yeah so what we were doing in this code is we were taking gigabytes
@@ -1006,7 +1006,7 @@ like you know like 30 000 and i was like it was an immediate you know these thes
 pro program ran orders of magnitude faster that's awesome and you know what i think i really love about
 
 00:32:43.000 --> 00:32:49.600
-this two things i love about it one is you know you talked a little bit about the the solution on the
+this two things i love about it one is you know you talked a little bit about the solution on the
 
 00:32:49.600 --> 00:32:57.620
 blog and it's like i don't know nine lines of code no yes that's it's python yeah it's so awesome and
@@ -1111,7 +1111,7 @@ it couldn't keep up well then you can't write a real time system or you need som
 something right and it was doing crazy math like wavelet decomposition and all sorts of stuff okay this is
 
 00:36:33.740 --> 00:36:39.160
-like like i was saying earlier it's the verge of understanding what we're doing right yeah and it
+like i was saying earlier it's the verge of understanding what we're doing right yeah and it
 
 00:36:39.160 --> 00:36:43.620
 was too slow the first time we ran like oh no please don't make me try to optimize a wavelet