Merge branch 'master' of https://github.com/talkpython/talk-python-transcripts

Author: mikeckennedy

transcripts/031_scikit-learn_and_machine_learning.txt

@@ -1,8 +1,8 @@
 00:00:00 Machine learning allows computers to find hidden insights without being explicitly programmed where to look or what to look for.
 
-00:00:06 Thanks to the work of some dedicated developers, Python has one of the best machine learning platforms out there called Scikit-Learn.
+00:00:06 Thanks to the work of some dedicated developers, Python has one of the best machine learning platforms out there called scikit-learn.
 
-00:00:14 In this episode, Alexander Gramfort is here to tell us about Scikit-Learn and machine learning.
+00:00:14 In this episode, Alexander Gramfort is here to tell us about scikit-learn and machine learning.
 
 00:00:19 This is Talk Python To Me, number 31, recorded Friday, September 25, 2015.
 
@@ -696,9 +696,9 @@
 
 00:30:41 I guess it's hard to summarize.
 
-00:30:44 The hundreds of hundreds of pages that you have in Scikit-Learn in the documentation, I'm trying to give you a big picture without too much technical detail to tell you when these algorithms are useful and what they are useful for, and what are the hypotheses and what kind of output you can hope to get.
+00:30:44 The hundreds of hundreds of pages that you have in scikit-learn in the documentation, I'm trying to give you a big picture without too much technical detail to tell you when these algorithms are useful and what they are useful for, and what are the hypotheses and what kind of output you can hope to get.
 
-00:31:02 It's one of the strengths of the Scikit-Learn documentation, by the way.
+00:31:02 It's one of the strengths of the scikit-learn documentation, by the way.
 
 00:31:05 And so to answer your question, dimensionality reduction, I would say like the 101 way of doing it is the principal component analysis, where you're trying to extract subspace that captures the most variance in the data.
 
@@ -778,7 +778,7 @@
 
 00:34:20 Yes.
 
-00:34:21 So Scikit-Learn has support for neural networks as well?
+00:34:21 So scikit-learn has support for neural networks as well?
 
 00:34:23 Well, you have a multilayer perception, which is like the basic neural network.
 

transcripts/031_scikit-learn_and_machine_learning.vtt

@@ -4,10 +4,10 @@ WEBVTT
 Machine learning allows computers to find hidden insights without being explicitly programmed where to look or what to look for.
 
 00:00:06.840 --> 00:00:14.060
-Thanks to the work of some dedicated developers, Python has one of the best machine learning platforms out there called Scikit-Learn.
+Thanks to the work of some dedicated developers, Python has one of the best machine learning platforms out there called scikit-learn.
 
 00:00:14.060 --> 00:00:19.180
-In this episode, Alexander Gramfort is here to tell us about Scikit-Learn and machine learning.
+In this episode, Alexander Gramfort is here to tell us about scikit-learn and machine learning.
 
 00:00:19.180 --> 00:00:25.460
 This is Talk Python To Me, number 31, recorded Friday, September 25, 2015.
@@ -1048,10 +1048,10 @@ Like, what kind of problems would I bring that in for?
 I guess it's hard to summarize.
 
 00:30:44.060 --> 00:31:02.060
-The hundreds of hundreds of pages that you have in Scikit-Learn in the documentation, I'm trying to give you a big picture without too much technical detail to tell you when these algorithms are useful and what they are useful for, and what are the hypotheses and what kind of output you can hope to get.
+The hundreds of hundreds of pages that you have in scikit-learn in the documentation, I'm trying to give you a big picture without too much technical detail to tell you when these algorithms are useful and what they are useful for, and what are the hypotheses and what kind of output you can hope to get.
 
 00:31:02.800 --> 00:31:05.760
-It's one of the strengths of the Scikit-Learn documentation, by the way.
+It's one of the strengths of the scikit-learn documentation, by the way.
 
 00:31:05.760 --> 00:31:22.280
 And so to answer your question, dimensionality reduction, I would say like the 101 way of doing it is the principal component analysis, where you're trying to extract subspace that captures the most variance in the data.
@@ -1171,7 +1171,7 @@ So you mentioned neural networks.
 Yes.
 
 00:34:21.040 --> 00:34:23.380
-So Scikit-Learn has support for neural networks as well?
+So scikit-learn has support for neural networks as well?
 
 00:34:23.380 --> 00:34:29.560
 Well, you have a multilayer perception, which is like the basic neural network.

transcripts/051_sigopt_optimizing_everything_with_python.txt

@@ -822,7 +822,7 @@
 
 00:21:46 They're, you know, really great tools for that, that we love, both in the kind of prototyping phase where we want to try something new on some data set or something like that.
 
-00:21:53 Using SciPy, Scikit-Learn, NumPy are fantastic.
+00:21:53 Using SciPy, scikit-learn, NumPy are fantastic.
 
 00:21:55 And then also, you know, they have the performance we need to actually put them in production when we're appropriate.
 

transcripts/051_sigopt_optimizing_everything_with_python.vtt

@@ -1246,7 +1246,7 @@ So we use a lot of that for kind of, yeah, high capacity computation and optimiz
 They're, you know, really great tools for that, that we love, both in the kind of prototyping phase where we want to try something new on some data set or something like that.
 
 00:21:53.500 --> 00:21:55.920
-Using SciPy, Scikit-Learn, NumPy are fantastic.
+Using SciPy, scikit-learn, NumPy are fantastic.
 
 00:21:55.920 --> 00:22:01.000
 And then also, you know, they have the performance we need to actually put them in production when we're appropriate.

transcripts/056_data_science_from_scratch.txt

@@ -882,7 +882,7 @@
 
 00:39:05 libraries are really built on top of it. So kind of the most broadly accessible machine learning
 
-00:39:11 library for Python is called Scikit Learn. And it has really nice documentation and really nice tutorials
+00:39:11 library for Python is called scikit-learn. And it has really nice documentation and really nice tutorials
 
 00:39:16 and a fairly standard API for building machine learning models. Anything you want to build a
 

transcripts/056_data_science_from_scratch.vtt

@@ -1333,7 +1333,7 @@ like that, you don't get just built in. And you might not use it directly, but a
 libraries are really built on top of it. So kind of the most broadly accessible machine learning
 
 00:39:11.320 --> 00:39:16.520
-library for Python is called Scikit Learn. And it has really nice documentation and really nice tutorials
+library for Python is called scikit-learn. And it has really nice documentation and really nice tutorials
 
 00:39:16.520 --> 00:39:21.320
 and a fairly standard API for building machine learning models. Anything you want to build a

transcripts/103-compiling-python-through-pyllvm-and-mongodb-for-data-scientists.txt

@@ -626,7 +626,7 @@
 
 00:18:47 Those are basically the most important things.
 
-00:18:49 This portion of Talk Pythonry is brought to you by us.
+00:18:49 This portion of Talk Python To Me is brought to you by us.
 
 00:18:53 As many of you know, I have a growing set of courses to help you go from Python beginner to novice to Python expert.
 

transcripts/103-compiling-python-through-pyllvm-and-mongodb-for-data-scientists.vtt

@@ -955,7 +955,7 @@ You would need to have conditionals, loops, and arithmetic.
 Those are basically the most important things.
 
 00:18:49.780 --> 00:18:53.640
-This portion of Talk Pythonry is brought to you by us.
+This portion of Talk Python To Me is brought to you by us.
 
 00:18:53.640 --> 00:18:59.540
 As many of you know, I have a growing set of courses to help you go from Python beginner to novice to Python expert.

transcripts/113-dedicated-ai-chips-and-running-old-python-faster-at-intel.txt

@@ -92,7 +92,7 @@
 
 00:06:38 Wow, that's really cool. And Sergey, how about your involvement?
 
-00:06:40 They have solved a really critical problem in making an interpreter or GTing really fast on Intel architecture. Intel distribution for Python, it also solves the problem of making numerical and machine learning running faster. And Python is known and loved for really nice numerical packages, NumPy, SciPy, Scikit-Learn.
+00:06:40 They have solved a really critical problem in making an interpreter or GTing really fast on Intel architecture. Intel distribution for Python, it also solves the problem of making numerical and machine learning running faster. And Python is known and loved for really nice numerical packages, NumPy, SciPy, scikit-learn.
 
 00:07:06 All the stuff that we saw in the keynote today.
 
@@ -102,7 +102,7 @@
 
 00:07:18 So yeah, absolutely. Those groups of people, the scientists, data scientists, it's great, right?
 
-00:07:24 That's why we focus on this area and we optimize these numerical packages, not interpreter itself, but rather the packages. And for that, we rely on high performance libraries, native libraries that Intel develops for decades, Intel mass kernel library, Intel MPI, Intel data analytics creation library. These all good high performance libraries are used underneath to accelerate NumPy, SciPy, Scikit-Learn.
+00:07:24 That's why we focus on this area and we optimize these numerical packages, not interpreter itself, but rather the packages. And for that, we rely on high performance libraries, native libraries that Intel develops for decades, Intel mass kernel library, Intel MPI, Intel data analytics creation library. These all good high performance libraries are used underneath to accelerate NumPy, SciPy, scikit-learn.
 
 00:07:52 I see. So you take, let's say, NumPy. You take NumPy and you recompile it against these high performance Intel libraries. And that, because the foundation is faster, basically makes NumPy itself faster.
 

transcripts/113-dedicated-ai-chips-and-running-old-python-faster-at-intel.vtt

@@ -142,7 +142,7 @@ With CPython. And the PyPy, the PyPerformance project, that is how they're measu
 Wow, that's really cool. And Sergey, how about your involvement?
 
 00:06:40.520 --> 00:07:06.520
-They have solved a really critical problem in making an interpreter or GTing really fast on Intel architecture. Intel distribution for Python, it also solves the problem of making numerical and machine learning running faster. And Python is known and loved for really nice numerical packages, NumPy, SciPy, Scikit-Learn.
+They have solved a really critical problem in making an interpreter or GTing really fast on Intel architecture. Intel distribution for Python, it also solves the problem of making numerical and machine learning running faster. And Python is known and loved for really nice numerical packages, NumPy, SciPy, scikit-learn.
 
 00:07:06.520 --> 00:07:08.520
 All the stuff that we saw in the keynote today.
@@ -157,7 +157,7 @@ It's just like, here's why people that do numerical analysis love and use Python
 So yeah, absolutely. Those groups of people, the scientists, data scientists, it's great, right?
 
 00:07:24.520 --> 00:07:52.520
-That's why we focus on this area and we optimize these numerical packages, not interpreter itself, but rather the packages. And for that, we rely on high performance libraries, native libraries that Intel develops for decades, Intel mass kernel library, Intel MPI, Intel data analytics creation library. These all good high performance libraries are used underneath to accelerate NumPy, SciPy, Scikit-Learn.
+That's why we focus on this area and we optimize these numerical packages, not interpreter itself, but rather the packages. And for that, we rely on high performance libraries, native libraries that Intel develops for decades, Intel mass kernel library, Intel MPI, Intel data analytics creation library. These all good high performance libraries are used underneath to accelerate NumPy, SciPy, scikit-learn.
 
 00:07:52.520 --> 00:08:04.520
 I see. So you take, let's say, NumPy. You take NumPy and you recompile it against these high performance Intel libraries. And that, because the foundation is faster, basically makes NumPy itself faster.