transcripts fixes

Author: mikeckennedy

transcripts/181.txt

@@ -126,7 +126,7 @@
 
 00:12:19 Brian Okken: Yeah this looks looks very interesting and natural language processing is actually getting used quite a bit.
 
-00:12:25 Michael Kennedy: Yeah any time you want to take text and understand what the words mean this is it right? 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, you probably would put Spacy in there right?
+00:12:25 Michael Kennedy: Yeah any time you want to take text and understand what the words mean this is it right? 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, you probably would put spaCy in there right?
 
 00:12:47 Brian Okken: Yeah, sure right. Or if you want to...
 

transcripts/463-running-on-rust-granian-web-server.txt

@@ -54,19 +54,19 @@
 
 00:01:44 In fact, I want to tell you about our latest course we just released last week,
 
-00:01:48 Getting Started with NLP and Spacy.
+00:01:48 Getting Started with NLP and spaCy.
 
 00:01:51 This one is written by Vincent Wormerdam.
 
 00:01:53 You may know him from many of his educational projects and channels, but
 
-00:01:58 he also worked at Explosion AI, the makers of Spacy, so it's safe to say he knows
+00:01:58 he also worked at Explosion AI, the makers of spaCy, so it's safe to say he knows
 
-00:02:03 his stuff when it comes to NLP and Spacy.
+00:02:03 his stuff when it comes to NLP and spaCy.
 
 00:02:05 If you have text you need to analyze, pull entities from, understand the
 
-00:02:10 sentiment, and so much more, then Spacy is one of the best frameworks out there for
+00:02:10 sentiment, and so much more, then spaCy is one of the best frameworks out there for
 
 00:02:14 this.
 
@@ -76,7 +76,7 @@
 
 00:02:22 Well, Vincent uses the past nine years of Talk Python transcripts, along with a few
 
-00:02:27 data science programming bits of magic, to process them all with Spacy and ask
+00:02:27 data science programming bits of magic, to process them all with spaCy and ask
 
 00:02:33 awesome questions like, "Which frameworks are we talking about over the years?"
 

transcripts/463-running-on-rust-granian-web-server.vtt

@@ -91,7 +91,7 @@ Check them out at talkpython.fm/courses.
 In fact, I want to tell you about our latest course we just released last week,
 
 00:01:48.500 --> 00:01:51.260
-Getting Started with NLP and Spacy.
+Getting Started with NLP and spaCy.
 
 00:01:51.620 --> 00:01:53.720
 This one is written by Vincent Wormerdam.
@@ -100,16 +100,16 @@ This one is written by Vincent Wormerdam.
 You may know him from many of his educational projects and channels, but
 
 00:01:58.100 --> 00:02:03.540
-he also worked at Explosion AI, the makers of Spacy, so it's safe to say he knows
+he also worked at Explosion AI, the makers of spaCy, so it's safe to say he knows
 
 00:02:03.540 --> 00:02:05.800
-his stuff when it comes to NLP and Spacy.
+his stuff when it comes to NLP and spaCy.
 
 00:02:05.800 --> 00:02:10.860
 If you have text you need to analyze, pull entities from, understand the
 
 00:02:10.860 --> 00:02:14.840
-sentiment, and so much more, then Spacy is one of the best frameworks out there for
+sentiment, and so much more, then spaCy is one of the best frameworks out there for
 
 00:02:14.840 --> 00:02:15.160
 this.
@@ -124,7 +124,7 @@ During the course, you need a fun project, right?
 Well, Vincent uses the past nine years of Talk Python transcripts, along with a few
 
 00:02:27.440 --> 00:02:33.340
-data science programming bits of magic, to process them all with Spacy and ask
+data science programming bits of magic, to process them all with spaCy and ask
 
 00:02:33.340 --> 00:02:37.380
 awesome questions like, "Which frameworks are we talking about over the years?"

transcripts/465-the-ai-revolution-wont-be-monopolized.txt

@@ -54,13 +54,13 @@
 
 00:01:39 topic.
 
-00:01:40 The course is called Getting Started with NLP and Spacy.
+00:01:40 The course is called Getting Started with NLP and spaCy.
 
-00:01:44 It was created by Vincent Vormerdam, who has spent time working directly on Spacy at Explosion
+00:01:44 It was created by Vincent Vormerdam, who has spent time working directly on spaCy at Explosion
 
 00:01:49 AI.
 
-00:01:50 The course is a really fun exploration of what you can do with Spacy for processing
+00:01:50 The course is a really fun exploration of what you can do with spaCy for processing
 
 00:01:53 and understanding text data.
 
@@ -72,7 +72,7 @@
 
 00:02:07 The link is in your podcast player show notes.
 
-00:02:09 Now on to more AI and Spacy with Ines.
+00:02:09 Now on to more AI and spaCy with Ines.
 
 00:02:12 Ines, welcome back to Talk Python with me.
 
@@ -90,7 +90,7 @@
 
 00:02:22 We have some really cool things to talk about.
 
-00:02:24 Spacy, some of course, but also more broadly, we're going to talk about just LLMs and AIs
+00:02:24 spaCy, some of course, but also more broadly, we're going to talk about just LLMs and AIs
 
 00:02:32 and open source and business models and even monopolies.
 
@@ -192,7 +192,7 @@
 
 00:04:49 That's kind of where we started and what we've always been focusing on.
 
-00:04:53 So Spacy is probably what we're mostly known for, which is a popular open source library
+00:04:53 So spaCy is probably what we're mostly known for, which is a popular open source library
 
 00:04:58 for really what we call industrial strength NLP.
 
@@ -250,9 +250,9 @@
 
 00:06:30 tech posts up, but are completely bullshit and completely wrong.
 
-00:06:34 Like I saw with something on Spacy LLM, which is our extension for integrating large language
+00:06:34 Like I saw with something on spaCy LLM, which is our extension for integrating large language
 
-00:06:40 models into Spacy.
+00:06:40 models into spaCy.
 
 00:06:41 And they're like some blog posts that look like they're tech blog posts, but they're
 
@@ -1152,7 +1152,7 @@
 
 00:30:27 It's kind of what we distribute for spacey.
 
-00:30:30 There's also a lot of really cool community projects like SciSpacy for scientific biomedical
+00:30:30 There's also a lot of really cool community projects like ScispaCy for scientific biomedical
 
 00:30:37 techs.
 
@@ -1250,11 +1250,11 @@
 
 00:33:25 Thank you to Porkbun for supporting the show.
 
-00:33:28 Another one in this category of task specific models is SciSpacy, which is kind of cool.
+00:33:28 Another one in this category of task specific models is ScispaCy, which is kind of cool.
 
-00:33:34 What's SciSpacy?
+00:33:34 What's ScispaCy?
 
-00:33:35 Yeah, so SciSpacy, that's for scientific biomedical text that was published by Allen AI researchers.
+00:33:35 Yeah, so ScispaCy, that's for scientific biomedical text that was published by Allen AI researchers.
 
 00:33:42 And yeah, it's really, it has like components specific for working with that sort of data.
 

transcripts/465-the-ai-revolution-wont-be-monopolized.vtt

@@ -85,16 +85,16 @@ Over at Talk Python, we just launched a new course, and it's super relevant to t
 topic.
 
 00:01:40.620 --> 00:01:44.040
-The course is called Getting Started with NLP and Spacy.
+The course is called Getting Started with NLP and spaCy.
 
 00:01:44.040 --> 00:01:49.120
-It was created by Vincent Vormerdam, who has spent time working directly on Spacy at Explosion
+It was created by Vincent Vormerdam, who has spent time working directly on spaCy at Explosion
 
 00:01:49.120 --> 00:01:50.120
 AI.
 
 00:01:50.120 --> 00:01:53.480
-The course is a really fun exploration of what you can do with Spacy for processing
+The course is a really fun exploration of what you can do with spaCy for processing
 
 00:01:53.480 --> 00:01:55.880
 and understanding text data.
@@ -112,7 +112,7 @@ If you have text data you need to understand, check out the course today at talk
 The link is in your podcast player show notes.
 
 00:02:09.480 --> 00:02:12.560
-Now on to more AI and Spacy with Ines.
+Now on to more AI and spaCy with Ines.
 
 00:02:12.560 --> 00:02:14.920
 Ines, welcome back to Talk Python with me.
@@ -139,7 +139,7 @@ Thank you.
 We have some really cool things to talk about.
 
 00:02:24.960 --> 00:02:32.440
-Spacy, some of course, but also more broadly, we're going to talk about just LLMs and AIs
+spaCy, some of course, but also more broadly, we're going to talk about just LLMs and AIs
 
 00:02:32.440 --> 00:02:35.600
 and open source and business models and even monopolies.
@@ -1879,13 +1879,13 @@ The link is in your podcast player's show notes.
 Thank you to Porkbun for supporting the show.
 
 00:33:28.680 --> 00:33:34.340
-Another one in this category of task specific models is SciSpacy, which is kind of cool.
+Another one in this category of task specific models is ScispaCy, which is kind of cool.
 
 00:33:34.340 --> 00:33:35.340
-What's SciSpacy?
+What's ScispaCy?
 
 00:33:35.340 --> 00:33:42.860
-Yeah, so SciSpacy, that's for scientific biomedical text that was published by Allen AI researchers.
+Yeah, so ScispaCy, that's for scientific biomedical text that was published by Allen AI researchers.
 
 00:33:42.860 --> 00:33:49.220
 And yeah, it's really, it has like components specific for working with that sort of data.

youtube_transcripts/351-eu-ml-law-and-ethics.vtt

@@ -525,7 +525,7 @@ We're probably most known for our open source library,
 
 
 00:05:50.500 --> 00:05:52.700
-Spacy, which is an open source library
+spaCy, which is an open source library
 
 
 00:05:52.700 --> 00:05:54.100

youtube_transcripts/465-the-ai-revolution-wont-be-monopolized.vtt

@@ -910,10 +910,10 @@ And that was before we even, you know, used much transfer learning and transform
 before. So yeah.
 
 00:32:36.000 --> 00:32:44.000
-Another one in this category of task specific models is SciSpacy, which is kind of cool. What's SciSpacy?
+Another one in this category of task specific models is ScispaCy, which is kind of cool. What's ScispaCy?
 
 00:32:44.000 --> 00:33:12.000
-Yeah, so SciSpacy, that's for scientific biomedical text that was published by Allen AI researchers. And yeah, it's really, it has like components specific for working with that sort of data. And it's actually, it's definitely if that's kind of the ___domain, yeah, any listeners are working with, definitely check it out.
+Yeah, so ScispaCy, that's for scientific biomedical text that was published by Allen AI researchers. And yeah, it's really, it has like components specific for working with that sort of data. And it's actually, it's definitely if that's kind of the ___domain, yeah, any listeners are working with, definitely check it out.
 
 00:33:12.000 --> 00:33:26.000
 They've also done some pretty smart work around like a training components, but also implementing, like hybrid role based things for say, acronym expansion.

youtube_transcripts/477-spacy-nlp.vtt

@@ -397,13 +397,13 @@ which is a cool project that you've got going on.
 We'll talk about in just a moment through the Python byte stuff.
 
 00:05:05.280 --> 00:05:10.200
-Then through Explosion AI and Spacy and all that,
+Then through Explosion AI and spaCy and all that,
 
 00:05:10.200 --> 00:05:13.160
 we actually teamed up to do a course that you wrote called,
 
 00:05:13.160 --> 00:05:15.360
-Getting Started with NLP and Spacy,
+Getting Started with NLP and spaCy,
 
 00:05:15.360 --> 00:05:18.400
 which is over at Talk Python, which is awesome.