transcripts

Author: mikeckennedy

transcripts/090_data_wrangling_with_python.txt

@@ -1836,11 +1836,11 @@
 
 00:57:49 What one would you recommend to people that maybe they don't know about or haven't heard about?
 
-00:57:54 Well, we didn't get to talk much about NLP, but I really, really love Gensum and Spacey.
+00:57:54 Well, we didn't get to talk much about NLP, but I really, really love Gensum and spaCy.
 
 00:58:00 Both of them are really changing and pushing the space of kind of where academia is with natural language processing and giving it and making it available for us mere mortals.
 
-00:58:12 So I really recommend if you want to take a look at how to use neural networks with natural language processing, that you check out both Gensum and Spacey.
+00:58:12 So I really recommend if you want to take a look at how to use neural networks with natural language processing, that you check out both Gensum and spaCy.
 
 00:58:21 Okay.
 

transcripts/090_data_wrangling_with_python.vtt

@@ -2788,13 +2788,13 @@ And the first is we already enumerated quite the list, but there's over 90,000 p
 What one would you recommend to people that maybe they don't know about or haven't heard about?
 
 00:57:54.200 --> 00:58:00.040
-Well, we didn't get to talk much about NLP, but I really, really love Gensum and Spacey.
+Well, we didn't get to talk much about NLP, but I really, really love Gensum and spaCy.
 
 00:58:00.040 --> 00:58:11.760
 Both of them are really changing and pushing the space of kind of where academia is with natural language processing and giving it and making it available for us mere mortals.
 
 00:58:12.160 --> 00:58:21.000
-So I really recommend if you want to take a look at how to use neural networks with natural language processing, that you check out both Gensum and Spacey.
+So I really recommend if you want to take a look at how to use neural networks with natural language processing, that you check out both Gensum and spaCy.
 
 00:58:21.000 --> 00:58:21.560
 Okay.

transcripts/154-python-in-genomics.txt

@@ -680,13 +680,13 @@
 
 00:27:08 And of course, it's a constant treadmill sort of thing.
 
-00:27:12 So one of the tools that you're using that didn't surprise me but I think is interesting and I want to hear more about is Spacey.
+00:27:12 So one of the tools that you're using that didn't surprise me but I think is interesting and I want to hear more about is spaCy.
 
-00:27:17 So I don't even think I've mentioned Spacey on the podcast before.
+00:27:17 So I don't even think I've mentioned spaCy on the podcast before.
 
 00:27:20 Tell us about that.
 
-00:27:21 What's Spacey?
+00:27:21 What's spaCy?
 
 00:27:22 Yeah.
 
@@ -708,7 +708,7 @@
 
 00:27:58 We can't spend billions of dollars.
 
-00:27:59 But what we're doing is we're taking Spacey and using that to parse some of these free text files and basically make recommendations to people.
+00:27:59 But what we're doing is we're taking spaCy and using that to parse some of these free text files and basically make recommendations to people.
 
 00:28:09 So doing things like what are called entity recognition.
 
@@ -720,7 +720,7 @@
 
 00:28:23 And this is a great tool for extracting out named entities like that.
 
-00:28:27 We've trained Spacey to find named entities based on our ontologies, our data within our KMS.
+00:28:27 We've trained spaCy to find named entities based on our ontologies, our data within our KMS.
 
 00:28:35 Right.
 
@@ -732,7 +732,7 @@
 
 00:28:39 Exactly right.
 
-00:28:40 So there's a pattern matching framework that's within Spacey that's really very easy to use.
+00:28:40 So there's a pattern matching framework that's within spaCy that's really very easy to use.
 
 00:28:44 And then the other thing we'd use it for is for classification.
 
@@ -778,13 +778,13 @@
 
 00:30:05 It's pretty amazing.
 
-00:30:06 We like Spacey a lot.
+00:30:06 We like spaCy a lot.
 
 00:30:07 I originally tried playing with NLTK a few years ago and actually kind of ran into some barriers.
 
 00:30:11 It's an old that's an older project.
 
-00:30:13 Spacey is really modern and that it's, you know, kind of does some of the best practices with Python.
+00:30:13 spaCy is really modern and that it's, you know, kind of does some of the best practices with Python.
 
 00:30:18 I highly recommend it.
 
@@ -800,7 +800,7 @@
 
 00:30:29 It definitely, they have it lined up to, when you go to visit spacey.io, it really looks appealing and polished.
 
-00:30:37 I was wondering why you didn't choose, what the difference or what made you choose Spacey over NLTK?
+00:30:37 I was wondering why you didn't choose, what the difference or what made you choose spaCy over NLTK?
 
 00:30:42 It's actually pretty obvious straight away, isn't it?
 

transcripts/154-python-in-genomics.vtt

@@ -1027,16 +1027,16 @@ Maybe I should do that.
 And of course, it's a constant treadmill sort of thing.
 
 00:27:12.500 --> 00:27:17.060
-So one of the tools that you're using that didn't surprise me but I think is interesting and I want to hear more about is Spacey.
+So one of the tools that you're using that didn't surprise me but I think is interesting and I want to hear more about is spaCy.
 
 00:27:17.060 --> 00:27:20.260
-So I don't even think I've mentioned Spacey on the podcast before.
+So I don't even think I've mentioned spaCy on the podcast before.
 
 00:27:20.260 --> 00:27:21.300
 Tell us about that.
 
 00:27:21.300 --> 00:27:22.000
-What's Spacey?
+What's spaCy?
 
 00:27:22.000 --> 00:27:22.280
 Yeah.
@@ -1069,7 +1069,7 @@ We're a small company.
 We can't spend billions of dollars.
 
 00:27:59.920 --> 00:28:09.520
-But what we're doing is we're taking Spacey and using that to parse some of these free text files and basically make recommendations to people.
+But what we're doing is we're taking spaCy and using that to parse some of these free text files and basically make recommendations to people.
 
 00:28:09.520 --> 00:28:13.620
 So doing things like what are called entity recognition.
@@ -1087,7 +1087,7 @@ Those would all be proper nouns.
 And this is a great tool for extracting out named entities like that.
 
 00:28:27.880 --> 00:28:35.580
-We've trained Spacey to find named entities based on our ontologies, our data within our KMS.
+We've trained spaCy to find named entities based on our ontologies, our data within our KMS.
 
 00:28:35.580 --> 00:28:35.900
 Right.
@@ -1105,7 +1105,7 @@ Something like that.
 Exactly right.
 
 00:28:40.040 --> 00:28:44.300
-So there's a pattern matching framework that's within Spacey that's really very easy to use.
+So there's a pattern matching framework that's within spaCy that's really very easy to use.
 
 00:28:44.300 --> 00:28:47.140
 And then the other thing we'd use it for is for classification.
@@ -1174,7 +1174,7 @@ Yeah.
 It's pretty amazing.
 
 00:30:06.320 --> 00:30:07.620
-We like Spacey a lot.
+We like spaCy a lot.
 
 00:30:07.620 --> 00:30:11.740
 I originally tried playing with NLTK a few years ago and actually kind of ran into some barriers.
@@ -1183,7 +1183,7 @@ I originally tried playing with NLTK a few years ago and actually kind of ran in
 It's an old that's an older project.
 
 00:30:13.380 --> 00:30:18.200
-Spacey is really modern and that it's, you know, kind of does some of the best practices with Python.
+spaCy is really modern and that it's, you know, kind of does some of the best practices with Python.
 
 00:30:18.200 --> 00:30:19.500
 I highly recommend it.
@@ -1207,7 +1207,7 @@ Looks really cool.
 It definitely, they have it lined up to, when you go to visit spacey.io, it really looks appealing and polished.
 
 00:30:37.160 --> 00:30:42.440
-I was wondering why you didn't choose, what the difference or what made you choose Spacey over NLTK?
+I was wondering why you didn't choose, what the difference or what made you choose spaCy over NLTK?
 
 00:30:42.560 --> 00:30:44.880
 It's actually pretty obvious straight away, isn't it?

transcripts/181-thirty-python-projects-of-2018.txt

@@ -416,13 +416,13 @@
 
 00:11:55 Yeah.
 
-00:11:55 So going back to machine learning, our next one is Spacey.
+00:11:55 So going back to machine learning, our next one is spaCy.
 
 00:12:00 When you think, when I think, at least when I think of machine learning and like text understanding, natural language understanding, I always thought of NLTP.
 
 00:12:09 That seemed like the way to go.
 
-00:12:11 But Spacey is the new fancy way to do that apparently.
+00:12:11 But spaCy is the new fancy way to do that apparently.
 
 00:12:15 And it's written in Python and Cython.
 
@@ -438,7 +438,7 @@
 
 00:12:31 If you say wanted to, maybe you were an algorithmic trader and you were studying the live flow of data on Twitter and trying to look for sentiment analysis around a particular stock and then having automatic trading happen on that.
 
-00:12:44 You probably would put Spacey in there.
+00:12:44 You probably would put spaCy in there.
 
 00:12:46 Yeah, sure.
 

transcripts/181-thirty-python-projects-of-2018.vtt

@@ -628,7 +628,7 @@ It's probably pretty good, pretty nicely put together.
 Yeah.
 
 00:11:55.820 --> 00:12:00.780
-So going back to machine learning, our next one is Spacey.
+So going back to machine learning, our next one is spaCy.
 
 00:12:00.780 --> 00:12:09.080
 When you think, when I think, at least when I think of machine learning and like text understanding, natural language understanding, I always thought of NLTP.
@@ -637,7 +637,7 @@ When you think, when I think, at least when I think of machine learning and like
 That seemed like the way to go.
 
 00:12:11.640 --> 00:12:15.160
-But Spacey is the new fancy way to do that apparently.
+But spaCy is the new fancy way to do that apparently.
 
 00:12:15.160 --> 00:12:17.920
 And it's written in Python and Cython.
@@ -661,7 +661,7 @@ Anytime you want to take text and understand what the words mean, this is it, ri
 If you say wanted to, maybe you were an algorithmic trader and you were studying the live flow of data on Twitter and trying to look for sentiment analysis around a particular stock and then having automatic trading happen on that.
 
 00:12:44.960 --> 00:12:46.820
-You probably would put Spacey in there.
+You probably would put spaCy in there.
 
 00:12:46.820 --> 00:12:47.080
 Yeah, sure.

transcripts/202-software-biz.txt

@@ -8,7 +8,7 @@
 
 00:00:12 You could take things up a notch and change the equation and exchange value for money.
 
-00:00:17 That's what Ines Montani and her co-founder did when they started Explosion.ai with Spacey as a foundation.
+00:00:17 That's what Ines Montani and her co-founder did when they started Explosion.ai with spaCy as a foundation.
 
 00:00:23 Listen to her story about building a sustainable software business on open source and Python.
 
@@ -82,9 +82,9 @@
 
 00:02:11 But I guess that ties more into your linguistics side of things, right?
 
-00:02:14 Yeah, and Spacey, especially.
+00:02:14 Yeah, and spaCy, especially.
 
-00:02:15 Like Spacey, when I started working on Spacey, I realized that, wow, I can really combine what I liked.
+00:02:15 Like spaCy, when I started working on spaCy, I realized that, wow, I can really combine what I liked.
 
 00:02:20 For example, front-end development with a bit more programming, other types of stuff with language
 
@@ -108,15 +108,15 @@
 
 00:02:46 Yeah, so our company is called Explosion AI, and we mostly develop a lot of open source software.
 
-00:02:50 One of those projects is Spacey, which is a popular library for NLP and Python.
+00:02:50 One of those projects is spaCy, which is a popular library for NLP and Python.
 
 00:02:55 We also publish other products and other tools for software development developers.
 
 00:03:01 So we're a developer tools company.
 
 00:03:03 It really sounds like such an interesting project.
 
-00:03:04 And I love how you have Spacey and you kind of built a proper business on top of it,
+00:03:04 And I love how you have spaCy and you kind of built a proper business on top of it,
 
 00:03:11 not, you know, just throw like a PayPal donate button on there and hope that that does something right.
 
@@ -222,7 +222,7 @@
 
 00:06:05 So let's talk about your journey and your project, your company, Explosion AI.
 
-00:06:11 So I guess maybe just set the background, like how does Explosion AI and Spacey,
+00:06:11 So I guess maybe just set the background, like how does Explosion AI and spaCy,
 
 00:06:16 and we'll talk about some of the products you have, how does that whole history fit together and whatnot?
 
@@ -234,7 +234,7 @@
 
 00:06:30 We're finally getting to a point where this can actually be used in like commercial projects and help companies.
 
-00:06:35 So he left academia and started writing Spacey.
+00:06:35 So he left academia and started writing spaCy.
 
 00:06:38 So he basically, he was in Australia, he moved to Berlin.
 
@@ -248,7 +248,7 @@
 
 00:06:56 And that's also something I was very interested in.
 
-00:06:58 So we started working on Spacey and this worked very well.
+00:06:58 So we started working on spaCy and this worked very well.
 
 00:07:02 So yeah, we founded Explosion AI together.
 
@@ -260,7 +260,7 @@
 
 00:07:15 So we really, there were lots of companies who were interested in that.
 
-00:07:18 So we did have the advantage that we had Spacey.
+00:07:18 So we did have the advantage that we had spaCy.
 
 00:07:20 People liked our work, wanted to implement this better.
 
@@ -378,7 +378,7 @@
 
 00:10:10 for machine learning projects and run experiments because the idea is, okay,
 
-00:10:15 if you're using a library like Spacey or any other machine learning solution,
+00:10:15 if you're using a library like spaCy or any other machine learning solution,
 
 00:10:18 you ultimately, once it's really giving you a lot of value, you want to train
 
@@ -428,13 +428,13 @@
 
 00:11:26 If you can write it in Python, you can use it in Prodigy.
 
-00:11:28 Yeah, it's really cool because most people doing something with Spacey or other machine learning
+00:11:28 Yeah, it's really cool because most people doing something with spaCy or other machine learning
 
 00:11:34 are, chances are they're already somewhat competent with Python anyway, right?
 
 00:11:38 And so you might as well just leverage that and that sounds really cool.
 
-00:11:42 And also a big reason people are using Spacey and open source tools like that
+00:11:42 And also a big reason people are using spaCy and open source tools like that
 
 00:11:45 is because they want to build their own systems and implement them themselves
 
@@ -660,11 +660,11 @@
 
 00:17:30 So if you make it, like we've always wanted to have great docs, wanted it to be easy
 
-00:17:33 for people to use Spacey.
+00:17:33 for people to use spaCy.
 
-00:17:34 So if it's too easy to use Spacey, people don't need our services and we make less money.
+00:17:34 So if it's too easy to use spaCy, people don't need our services and we make less money.
 
-00:17:38 But if Spacey is shit, we're losing business that way because people are not
+00:17:38 But if spaCy is shit, we're losing business that way because people are not
 
 00:17:43 coming to us anymore and the library loses adoption.
 
@@ -680,7 +680,7 @@
 
 00:17:54 and actually, in fact, there are lots of consultants who are somewhat specializing
 
-00:17:58 on building stuff with Spacey and it works well for them and we think that's much better
+00:17:58 on building stuff with spaCy and it works well for them and we think that's much better
 
 00:18:02 for us than if we did all of that.
 
@@ -852,7 +852,7 @@
 
 00:22:34 that were actually built by pretty small teams, right?
 
-00:22:37 there's not that many people behind Spacey and that's super successful.
+00:22:37 there's not that many people behind spaCy and that's super successful.
 
 00:22:39 There's not that many people behind Flask, right?