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+WEBVTT
+
+00:00:03.879 --> 00:00:07.480
+cool um so this time I'm going to talk
+
+00:00:05.480 --> 00:00:08.880
+about word representation and text
+
+00:00:07.480 --> 00:00:11.480
+classifiers these are kind of the
+
+00:00:08.880 --> 00:00:14.080
+foundations that you need to know uh in
+
+00:00:11.480 --> 00:00:15.640
+order to move on to the more complex
+
+00:00:14.080 --> 00:00:17.920
+things that we'll be talking in future
+
+00:00:15.640 --> 00:00:19.640
+classes uh but actually the in
+
+00:00:17.920 --> 00:00:22.760
+particular the word representation part
+
+00:00:19.640 --> 00:00:25.439
+is pretty important it's a major uh
+
+00:00:22.760 --> 00:00:31.800
+thing that we need to do for all NLP
+
+00:00:25.439 --> 00:00:34.239
+models so uh let's go into it
+
+00:00:31.800 --> 00:00:38.200
+so last class I talked about the bag of
+
+00:00:34.239 --> 00:00:40.239
+words model um and just to review this
+
+00:00:38.200 --> 00:00:43.920
+was a model where basically we take each
+
+00:00:40.239 --> 00:00:45.520
+word we represent it as a one hot Vector
+
+00:00:43.920 --> 00:00:48.760
+uh like
+
+00:00:45.520 --> 00:00:51.120
+this and we add all of these vectors
+
+00:00:48.760 --> 00:00:53.160
+together we multiply the resulting
+
+00:00:51.120 --> 00:00:55.160
+frequency vector by some weights and we
+
+00:00:53.160 --> 00:00:57.239
+get a score out of this and we can use
+
+00:00:55.160 --> 00:00:58.559
+this score for binary classification or
+
+00:00:57.239 --> 00:01:00.239
+if we want to do multiclass
+
+00:00:58.559 --> 00:01:02.519
+classification we get you know multiple
+
+00:01:00.239 --> 00:01:05.720
+scores for each
+
+00:01:02.519 --> 00:01:08.040
+class and the features F were just based
+
+00:01:05.720 --> 00:01:08.920
+on our word identities and the weights
+
+00:01:08.040 --> 00:01:12.159
+were
+
+00:01:08.920 --> 00:01:14.680
+learned and um if we look at what's
+
+00:01:12.159 --> 00:01:17.520
+missing in bag of words
+
+00:01:14.680 --> 00:01:19.600
+models um we talked about handling of
+
+00:01:17.520 --> 00:01:23.280
+conjugated or compound
+
+00:01:19.600 --> 00:01:25.439
+words we talked about handling of word
+
+00:01:23.280 --> 00:01:27.880
+similarity and we talked about handling
+
+00:01:25.439 --> 00:01:30.240
+of combination features and handling of
+
+00:01:27.880 --> 00:01:33.280
+sentence structure and so all of these
+
+00:01:30.240 --> 00:01:35.000
+are are tricky problems uh we saw that
+
+00:01:33.280 --> 00:01:37.000
+you know creating a rule-based system to
+
+00:01:35.000 --> 00:01:39.000
+solve these problems is non-trivial and
+
+00:01:37.000 --> 00:01:41.399
+at the very least would take a lot of
+
+00:01:39.000 --> 00:01:44.079
+time and so now I want to talk about
+
+00:01:41.399 --> 00:01:47.119
+some solutions to the problems in this
+
+00:01:44.079 --> 00:01:49.280
+class so the first the solution to the
+
+00:01:47.119 --> 00:01:52.240
+first problem or a solution to the first
+
+00:01:49.280 --> 00:01:54.880
+problem is uh subword or character based
+
+00:01:52.240 --> 00:01:57.520
+models and that's what I'll talk about
+
+00:01:54.880 --> 00:02:00.719
+first handling of word similarity this
+
+00:01:57.520 --> 00:02:02.960
+can be handled uh using Word edings
+
+00:02:00.719 --> 00:02:05.079
+and the word embeddings uh will be
+
+00:02:02.960 --> 00:02:07.159
+another thing we'll talk about this time
+
+00:02:05.079 --> 00:02:08.879
+handling of combination features uh we
+
+00:02:07.159 --> 00:02:11.039
+can handle through neural networks which
+
+00:02:08.879 --> 00:02:14.040
+we'll also talk about this time and then
+
+00:02:11.039 --> 00:02:15.560
+handling of sentence structure uh the
+
+00:02:14.040 --> 00:02:17.720
+kind of standard way of handling this
+
+00:02:15.560 --> 00:02:20.120
+now is through sequence-based models and
+
+00:02:17.720 --> 00:02:24.879
+that will be uh starting in a few
+
+00:02:20.120 --> 00:02:28.080
+classes so uh let's jump into
+
+00:02:24.879 --> 00:02:30.000
+it so subword models uh as I mentioned
+
+00:02:28.080 --> 00:02:31.840
+this is a really really important part
+
+00:02:30.000 --> 00:02:33.360
+all of the models that we're building
+
+00:02:31.840 --> 00:02:35.480
+nowadays including you know
+
+00:02:33.360 --> 00:02:38.239
+state-of-the-art language models and and
+
+00:02:35.480 --> 00:02:42.200
+things like this and the basic idea
+
+00:02:38.239 --> 00:02:44.720
+behind this is that we want to split uh
+
+00:02:42.200 --> 00:02:48.040
+in particular split less common words up
+
+00:02:44.720 --> 00:02:50.200
+into multiple subboard tokens so to give
+
+00:02:48.040 --> 00:02:52.200
+an example of this uh if we have
+
+00:02:50.200 --> 00:02:55.040
+something like the companies are
+
+00:02:52.200 --> 00:02:57.000
+expanding uh it might split companies
+
+00:02:55.040 --> 00:03:02.120
+into compan
+
+00:02:57.000 --> 00:03:05.000
+e and expand in like this and there are
+
+00:03:02.120 --> 00:03:08.480
+a few benefits of this uh the first
+
+00:03:05.000 --> 00:03:10.760
+benefit is that this allows you to
+
+00:03:08.480 --> 00:03:13.360
+parameters between word varieties or
+
+00:03:10.760 --> 00:03:15.200
+compound words and the other one is to
+
+00:03:13.360 --> 00:03:17.400
+reduce parameter size and save compute
+
+00:03:15.200 --> 00:03:19.720
+and meming and both of these are kind of
+
+00:03:17.400 --> 00:03:23.239
+like equally important things that we
+
+00:03:19.720 --> 00:03:25.519
+need to be uh we need to be considering
+
+00:03:23.239 --> 00:03:26.440
+so does anyone know how many words there
+
+00:03:25.519 --> 00:03:28.680
+are in
+
+00:03:26.440 --> 00:03:31.680
+English any
+
+00:03:28.680 --> 00:03:31.680
+ideas
+
+00:03:36.799 --> 00:03:43.400
+yeah two
+
+00:03:38.599 --> 00:03:45.560
+million pretty good um any other
+
+00:03:43.400 --> 00:03:47.159
+ideas
+
+00:03:45.560 --> 00:03:50.360
+yeah
+
+00:03:47.159 --> 00:03:53.599
+60,000 some models use 60,000 I I think
+
+00:03:50.360 --> 00:03:56.200
+60,000 is probably these subword models
+
+00:03:53.599 --> 00:03:58.079
+uh when you're talking about this so
+
+00:03:56.200 --> 00:03:59.319
+they can use sub models to take the 2
+
+00:03:58.079 --> 00:04:03.480
+million which I think is a reasonable
+
+00:03:59.319 --> 00:04:07.400
+guess to 6 60,000 any other
+
+00:04:03.480 --> 00:04:08.840
+ideas 700,000 okay pretty good um so
+
+00:04:07.400 --> 00:04:11.799
+this was a per question it doesn't
+
+00:04:08.840 --> 00:04:14.760
+really have a good answer um but two 200
+
+00:04:11.799 --> 00:04:17.479
+million's probably pretty good six uh
+
+00:04:14.760 --> 00:04:19.160
+700,000 is pretty good the reason why
+
+00:04:17.479 --> 00:04:21.360
+this is a trick question is because are
+
+00:04:19.160 --> 00:04:24.440
+company and companies different
+
+00:04:21.360 --> 00:04:26.840
+words uh maybe maybe not right because
+
+00:04:24.440 --> 00:04:30.120
+if we know the word company we can you
+
+00:04:26.840 --> 00:04:32.520
+know guess what the word companies means
+
+00:04:30.120 --> 00:04:35.720
+um what about automobile is that a
+
+00:04:32.520 --> 00:04:37.400
+different word well maybe if we know
+
+00:04:35.720 --> 00:04:39.400
+Auto and mobile we can kind of guess
+
+00:04:37.400 --> 00:04:41.160
+what automobile means but not really so
+
+00:04:39.400 --> 00:04:43.479
+maybe that's a different word there's
+
+00:04:41.160 --> 00:04:45.960
+all kinds of Shades of Gray there and
+
+00:04:43.479 --> 00:04:48.120
+also we have really frequent words that
+
+00:04:45.960 --> 00:04:50.360
+everybody can probably acknowledge our
+
+00:04:48.120 --> 00:04:52.320
+words like
+
+00:04:50.360 --> 00:04:55.639
+the and
+
+00:04:52.320 --> 00:04:58.520
+a and um maybe
+
+00:04:55.639 --> 00:05:00.680
+car and then we have words down here
+
+00:04:58.520 --> 00:05:02.320
+which are like Miss spellings or
+
+00:05:00.680 --> 00:05:04.160
+something like that misspellings of
+
+00:05:02.320 --> 00:05:06.520
+actual correct words or
+
+00:05:04.160 --> 00:05:09.199
+slay uh or other things like that and
+
+00:05:06.520 --> 00:05:12.520
+then it's questionable whether those are
+
+00:05:09.199 --> 00:05:17.199
+actual words or not so um there's a
+
+00:05:12.520 --> 00:05:19.520
+famous uh law called Zip's
+
+00:05:17.199 --> 00:05:21.280
+law um which probably a lot of people
+
+00:05:19.520 --> 00:05:23.360
+have heard of it's also the source of
+
+00:05:21.280 --> 00:05:26.919
+your zip
+
+00:05:23.360 --> 00:05:30.160
+file um which is using Zip's law to
+
+00:05:26.919 --> 00:05:32.400
+compress uh compress output by making
+
+00:05:30.160 --> 00:05:34.880
+the uh more frequent words have shorter
+
+00:05:32.400 --> 00:05:37.520
+bite strings and less frequent words
+
+00:05:34.880 --> 00:05:38.800
+have uh you know less frequent bite
+
+00:05:37.520 --> 00:05:43.120
+strings but basically like we're going
+
+00:05:38.800 --> 00:05:45.120
+to have an infinite number of words or
+
+00:05:43.120 --> 00:05:46.360
+at least strings that are separated by
+
+00:05:45.120 --> 00:05:49.280
+white space so we need to handle this
+
+00:05:46.360 --> 00:05:53.199
+somehow and that's what subword units
+
+00:05:49.280 --> 00:05:54.560
+do so um 60,000 was a good guess for the
+
+00:05:53.199 --> 00:05:57.160
+number of subword units you might use in
+
+00:05:54.560 --> 00:06:00.759
+a model and so uh by using subw units we
+
+00:05:57.160 --> 00:06:04.840
+can limit to about that much
+
+00:06:00.759 --> 00:06:08.160
+so there's a couple of common uh ways to
+
+00:06:04.840 --> 00:06:10.440
+create these subword units and basically
+
+00:06:08.160 --> 00:06:14.560
+all of them rely on the fact that you
+
+00:06:10.440 --> 00:06:16.039
+want more common strings to become
+
+00:06:14.560 --> 00:06:19.599
+subword
+
+00:06:16.039 --> 00:06:22.199
+units um or actually sorry I realize
+
+00:06:19.599 --> 00:06:24.280
+maybe before doing that I could explain
+
+00:06:22.199 --> 00:06:26.360
+an alternative to creating subword units
+
+00:06:24.280 --> 00:06:29.639
+so the alternative to creating subword
+
+00:06:26.360 --> 00:06:33.560
+units is to treat every character or
+
+00:06:29.639 --> 00:06:36.919
+maybe every bite in a string as a single
+
+00:06:33.560 --> 00:06:38.560
+thing that you encode in forent so in
+
+00:06:36.919 --> 00:06:42.520
+other words instead of trying to model
+
+00:06:38.560 --> 00:06:47.919
+the companies are expanding we Model T h
+
+00:06:42.520 --> 00:06:50.199
+e space c o m uh etc etc can anyone
+
+00:06:47.919 --> 00:06:53.199
+think of any downsides of
+
+00:06:50.199 --> 00:06:53.199
+this
+
+00:06:57.039 --> 00:07:01.879
+yeah yeah the set of these will be very
+
+00:07:00.080 --> 00:07:05.000
+will be very small but that's not
+
+00:07:01.879 --> 00:07:05.000
+necessarily a problem
+
+00:07:08.560 --> 00:07:15.599
+right yeah um and any other
+
+00:07:12.599 --> 00:07:15.599
+ideas
+
+00:07:19.520 --> 00:07:24.360
+yeah yeah the resulting sequences will
+
+00:07:22.080 --> 00:07:25.520
+be very long um and when you say
+
+00:07:24.360 --> 00:07:27.160
+difficult to use it could be difficult
+
+00:07:25.520 --> 00:07:29.560
+to use for a couple of reasons there's
+
+00:07:27.160 --> 00:07:31.840
+mainly two reasons actually any any IDE
+
+00:07:29.560 --> 00:07:31.840
+about
+
+00:07:33.479 --> 00:07:37.800
+this any
+
+00:07:46.280 --> 00:07:50.599
+yeah yeah that's a little bit of a
+
+00:07:49.000 --> 00:07:52.319
+separate problem than the character
+
+00:07:50.599 --> 00:07:53.919
+based model so let me get back to that
+
+00:07:52.319 --> 00:07:56.400
+but uh let let's finish the discussion
+
+00:07:53.919 --> 00:07:58.360
+of the character based models so if it's
+
+00:07:56.400 --> 00:08:00.120
+really if it's really long maybe a
+
+00:07:58.360 --> 00:08:01.879
+simple thing like uh let's say you have
+
+00:08:00.120 --> 00:08:06.560
+a big neural network and it's processing
+
+00:08:01.879 --> 00:08:06.560
+a really long sequence any ideas what
+
+00:08:06.919 --> 00:08:10.879
+happens basically you run out of memory
+
+00:08:09.280 --> 00:08:13.440
+or it takes a really long time right so
+
+00:08:10.879 --> 00:08:16.840
+you have computational problems another
+
+00:08:13.440 --> 00:08:18.479
+reason why is um think of what a bag of
+
+00:08:16.840 --> 00:08:21.400
+words model would look like if it was a
+
+00:08:18.479 --> 00:08:21.400
+bag of characters
+
+00:08:21.800 --> 00:08:25.919
+model it wouldn't be very informative
+
+00:08:24.199 --> 00:08:27.599
+about whether like a sentence is
+
+00:08:25.919 --> 00:08:30.919
+positive sentiment or negative sentiment
+
+00:08:27.599 --> 00:08:32.959
+right because instead of having uh go o
+
+00:08:30.919 --> 00:08:35.039
+you would have uh instead of having good
+
+00:08:32.959 --> 00:08:36.360
+you would have go o and that doesn't
+
+00:08:35.039 --> 00:08:38.560
+really directly tell you whether it's
+
+00:08:36.360 --> 00:08:41.719
+positive sentiment or not so those are
+
+00:08:38.560 --> 00:08:43.680
+basically the two problems um compute
+
+00:08:41.719 --> 00:08:45.320
+and lack of expressiveness in the
+
+00:08:43.680 --> 00:08:50.720
+underlying representations so you need
+
+00:08:45.320 --> 00:08:52.080
+to handle both of those yes so if we uh
+
+00:08:50.720 --> 00:08:54.480
+move from
+
+00:08:52.080 --> 00:08:56.440
+character better expressiveness and we
+
+00:08:54.480 --> 00:08:58.920
+assume that if we just get the bigger
+
+00:08:56.440 --> 00:09:00.120
+and bigger paragraphs we'll get even
+
+00:08:58.920 --> 00:09:02.760
+better
+
+00:09:00.120 --> 00:09:05.120
+yeah so a very good question I'll repeat
+
+00:09:02.760 --> 00:09:06.560
+it um and actually this also goes back
+
+00:09:05.120 --> 00:09:08.040
+to the other question you asked about
+
+00:09:06.560 --> 00:09:09.519
+words that look the same but are
+
+00:09:08.040 --> 00:09:12.160
+pronounced differently or have different
+
+00:09:09.519 --> 00:09:14.360
+meanings and so like let's say we just
+
+00:09:12.160 --> 00:09:15.920
+remembered this whole sentence right the
+
+00:09:14.360 --> 00:09:18.279
+companies are
+
+00:09:15.920 --> 00:09:21.600
+expanding um and that was like a single
+
+00:09:18.279 --> 00:09:22.680
+embedding and we somehow embedded it the
+
+00:09:21.600 --> 00:09:25.720
+problem would be we're never going to
+
+00:09:22.680 --> 00:09:27.120
+see that sentence again um or if we go
+
+00:09:25.720 --> 00:09:29.480
+to longer sentences we're never going to
+
+00:09:27.120 --> 00:09:31.839
+see the longer sentences again so it
+
+00:09:29.480 --> 00:09:34.320
+becomes too sparse so there's kind of a
+
+00:09:31.839 --> 00:09:37.240
+sweet spot between
+
+00:09:34.320 --> 00:09:40.279
+like long enough to be expressive and
+
+00:09:37.240 --> 00:09:42.480
+short enough to occur many times so that
+
+00:09:40.279 --> 00:09:43.959
+you can learn appropriately and that's
+
+00:09:42.480 --> 00:09:47.120
+kind of what subword models are aiming
+
+00:09:43.959 --> 00:09:48.360
+for and if you get longer subwords then
+
+00:09:47.120 --> 00:09:50.200
+you'll get things that are more
+
+00:09:48.360 --> 00:09:52.959
+expressive but more sparse in shorter
+
+00:09:50.200 --> 00:09:55.440
+subwords you'll get things that are like
+
+00:09:52.959 --> 00:09:57.279
+uh less expressive but less spice so you
+
+00:09:55.440 --> 00:09:59.120
+need to balance between them and then
+
+00:09:57.279 --> 00:10:00.600
+once we get into sequence modeling they
+
+00:09:59.120 --> 00:10:02.600
+start being able to model like which
+
+00:10:00.600 --> 00:10:04.120
+words are next to each other uh which
+
+00:10:02.600 --> 00:10:06.040
+tokens are next to each other and stuff
+
+00:10:04.120 --> 00:10:07.800
+like that so even if they are less
+
+00:10:06.040 --> 00:10:11.279
+expressive the combination between them
+
+00:10:07.800 --> 00:10:12.600
+can be expressive so um yeah that's kind
+
+00:10:11.279 --> 00:10:13.440
+of a preview of what we're going to be
+
+00:10:12.600 --> 00:10:17.320
+doing
+
+00:10:13.440 --> 00:10:19.279
+next okay so um let's assume that we
+
+00:10:17.320 --> 00:10:21.320
+want to have some subwords that are
+
+00:10:19.279 --> 00:10:23.000
+longer than characters but shorter than
+
+00:10:21.320 --> 00:10:26.240
+tokens how do we make these in a
+
+00:10:23.000 --> 00:10:28.680
+consistent way there's two major ways of
+
+00:10:26.240 --> 00:10:31.480
+doing this uh the first one is bite pair
+
+00:10:28.680 --> 00:10:32.839
+encoding and this is uh very very simple
+
+00:10:31.480 --> 00:10:35.839
+in fact it's so
+
+00:10:32.839 --> 00:10:35.839
+simple
+
+00:10:36.600 --> 00:10:40.839
+that we can implement
+
+00:10:41.839 --> 00:10:47.240
+it in this notebook here which you can
+
+00:10:44.600 --> 00:10:51.720
+click through to on the
+
+00:10:47.240 --> 00:10:55.440
+slides and it's uh
+
+00:10:51.720 --> 00:10:58.040
+about 10 lines of code um and so
+
+00:10:55.440 --> 00:11:01.040
+basically what B pair encoding
+
+00:10:58.040 --> 00:11:01.040
+does
+
+00:11:04.600 --> 00:11:09.560
+is that you start out with um all of the
+
+00:11:07.000 --> 00:11:14.360
+vocabulary that you want to process
+
+00:11:09.560 --> 00:11:17.560
+where each vocabulary item is split into
+
+00:11:14.360 --> 00:11:21.240
+uh the characters and an end of word
+
+00:11:17.560 --> 00:11:23.360
+symbol and you have a corresponding
+
+00:11:21.240 --> 00:11:27.519
+frequency of
+
+00:11:23.360 --> 00:11:31.120
+this you then uh get statistics about
+
+00:11:27.519 --> 00:11:33.279
+the most common pairs of tokens that
+
+00:11:31.120 --> 00:11:34.880
+occur next to each other and so here the
+
+00:11:33.279 --> 00:11:38.240
+most common pairs of tokens that occur
+
+00:11:34.880 --> 00:11:41.920
+next to each other are e s because it
+
+00:11:38.240 --> 00:11:46.560
+occurs nine times because it occurs in
+
+00:11:41.920 --> 00:11:48.279
+newest and wildest also s and t w
+
+00:11:46.560 --> 00:11:51.440
+because those occur there too and then
+
+00:11:48.279 --> 00:11:53.519
+you have we and other things like that
+
+00:11:51.440 --> 00:11:56.000
+so out of all the most frequent ones you
+
+00:11:53.519 --> 00:11:59.920
+just merge them together and that gives
+
+00:11:56.000 --> 00:12:02.720
+you uh new s new
+
+00:11:59.920 --> 00:12:05.200
+EST and wide
+
+00:12:02.720 --> 00:12:09.360
+EST and then you do the same thing this
+
+00:12:05.200 --> 00:12:12.519
+time now you get EST so now you get this
+
+00:12:09.360 --> 00:12:14.279
+uh suffix EST and that looks pretty
+
+00:12:12.519 --> 00:12:16.399
+reasonable for English right you know
+
+00:12:14.279 --> 00:12:19.040
+EST is a common suffix that we use it
+
+00:12:16.399 --> 00:12:22.399
+seems like it should be a single token
+
+00:12:19.040 --> 00:12:25.880
+and um so you just do this over and over
+
+00:12:22.399 --> 00:12:29.279
+again if you want a vocabulary of 60,000
+
+00:12:25.880 --> 00:12:31.120
+for example you would do um 60,000 minus
+
+00:12:29.279 --> 00:12:33.079
+number of characters merge operations
+
+00:12:31.120 --> 00:12:37.160
+and eventually you would get a B of
+
+00:12:33.079 --> 00:12:41.920
+60,000 um and yeah very very simple
+
+00:12:37.160 --> 00:12:41.920
+method to do this um any questions about
+
+00:12:43.160 --> 00:12:46.160
+that
+
+00:12:57.839 --> 00:13:00.839
+yeah
+
+00:13:15.600 --> 00:13:20.959
+yeah so uh just to repeat the the
+
+00:13:18.040 --> 00:13:23.560
+comment uh this seems like a greedy
+
+00:13:20.959 --> 00:13:25.320
+version of Huffman encoding which is a
+
+00:13:23.560 --> 00:13:28.839
+you know similar to what you're using in
+
+00:13:25.320 --> 00:13:32.000
+your zip file a way to shorten things by
+
+00:13:28.839 --> 00:13:36.560
+getting longer uh more frequent things
+
+00:13:32.000 --> 00:13:39.120
+being inced as a single token um I think
+
+00:13:36.560 --> 00:13:40.760
+B pair encoding did originally start
+
+00:13:39.120 --> 00:13:43.720
+like that that's part of the reason why
+
+00:13:40.760 --> 00:13:45.760
+the encoding uh thing is here I think it
+
+00:13:43.720 --> 00:13:47.360
+originally started there I haven't read
+
+00:13:45.760 --> 00:13:49.360
+really deeply into this but I can talk
+
+00:13:47.360 --> 00:13:53.240
+more about how the next one corresponds
+
+00:13:49.360 --> 00:13:54.440
+to information Theory and Tuesday I'm
+
+00:13:53.240 --> 00:13:55.720
+going to talk even more about how
+
+00:13:54.440 --> 00:13:57.720
+language models correspond to
+
+00:13:55.720 --> 00:14:00.040
+information theories so we can uh we can
+
+00:13:57.720 --> 00:14:04.519
+discuss maybe in more detail
+
+00:14:00.040 --> 00:14:07.639
+to um so the the alternative option is
+
+00:14:04.519 --> 00:14:10.000
+to use unigram models and unigram models
+
+00:14:07.639 --> 00:14:12.240
+are the simplest type of language model
+
+00:14:10.000 --> 00:14:15.079
+I'm going to talk more in detail about
+
+00:14:12.240 --> 00:14:18.279
+them next time but basically uh the way
+
+00:14:15.079 --> 00:14:20.759
+it works is you create a model that
+
+00:14:18.279 --> 00:14:23.600
+generates all word uh words in the
+
+00:14:20.759 --> 00:14:26.199
+sequence independently sorry I thought I
+
+00:14:23.600 --> 00:14:26.199
+had a
+
+00:14:26.320 --> 00:14:31.800
+um I thought I had an equation but
+
+00:14:28.800 --> 00:14:31.800
+basically the
+
+00:14:32.240 --> 00:14:35.759
+equation looks
+
+00:14:38.079 --> 00:14:41.079
+like
+
+00:14:47.720 --> 00:14:52.120
+this so you say the probability of the
+
+00:14:50.360 --> 00:14:53.440
+sequence is the product of the
+
+00:14:52.120 --> 00:14:54.279
+probabilities of each of the words in
+
+00:14:53.440 --> 00:14:55.959
+the
+
+00:14:54.279 --> 00:15:00.079
+sequence
+
+00:14:55.959 --> 00:15:04.079
+and uh then you try to pick a vocabulary
+
+00:15:00.079 --> 00:15:06.839
+that maximizes the probability of the
+
+00:15:04.079 --> 00:15:09.320
+Corpus given a fixed vocabulary size so
+
+00:15:06.839 --> 00:15:10.320
+you try to say okay you get a vocabulary
+
+00:15:09.320 --> 00:15:14.440
+size of
+
+00:15:10.320 --> 00:15:16.920
+60,000 how do you um how do you pick the
+
+00:15:14.440 --> 00:15:19.680
+best 60,000 vocabulary to maximize the
+
+00:15:16.920 --> 00:15:22.440
+probability of the the Corpus and that
+
+00:15:19.680 --> 00:15:25.959
+will result in something very similar uh
+
+00:15:22.440 --> 00:15:27.920
+it will also try to give longer uh
+
+00:15:25.959 --> 00:15:29.880
+vocabulary uh sorry more common
+
+00:15:27.920 --> 00:15:32.240
+vocabulary long sequences because that
+
+00:15:29.880 --> 00:15:35.560
+allows you to to maximize this
+
+00:15:32.240 --> 00:15:36.959
+objective um the optimization for this
+
+00:15:35.560 --> 00:15:40.040
+is performed using something called the
+
+00:15:36.959 --> 00:15:44.440
+EM algorithm where basically you uh
+
+00:15:40.040 --> 00:15:48.560
+predict the uh the probability of each
+
+00:15:44.440 --> 00:15:51.600
+token showing up and uh then select the
+
+00:15:48.560 --> 00:15:53.279
+most common tokens and then trim off the
+
+00:15:51.600 --> 00:15:54.759
+ones that are less common and then just
+
+00:15:53.279 --> 00:15:58.120
+do this over and over again until you
+
+00:15:54.759 --> 00:15:59.839
+drop down to the 60,000 token lat so the
+
+00:15:58.120 --> 00:16:02.040
+details for this are not important for
+
+00:15:59.839 --> 00:16:04.160
+most people in this class uh because
+
+00:16:02.040 --> 00:16:07.480
+you're going to just be using a toolkit
+
+00:16:04.160 --> 00:16:08.880
+that implements this for you um but if
+
+00:16:07.480 --> 00:16:10.759
+you're interested in this I'm happy to
+
+00:16:08.880 --> 00:16:14.199
+talk to you about it
+
+00:16:10.759 --> 00:16:14.199
+yeah is there
+
+00:16:14.680 --> 00:16:18.959
+problem Oh in unigram models there's a
+
+00:16:17.199 --> 00:16:20.959
+huge problem with assuming Independence
+
+00:16:18.959 --> 00:16:22.720
+in language models because then you
+
+00:16:20.959 --> 00:16:25.120
+could rearrange the order of words in
+
+00:16:22.720 --> 00:16:26.600
+sentences um that that's something we're
+
+00:16:25.120 --> 00:16:27.519
+going to talk about in language model
+
+00:16:26.600 --> 00:16:30.560
+next
+
+00:16:27.519 --> 00:16:32.839
+time but the the good thing about this
+
+00:16:30.560 --> 00:16:34.519
+is the EM algorithm requires dynamic
+
+00:16:32.839 --> 00:16:36.079
+programming in this case and you can't
+
+00:16:34.519 --> 00:16:37.800
+easily do dynamic programming if you
+
+00:16:36.079 --> 00:16:40.160
+don't make that
+
+00:16:37.800 --> 00:16:41.880
+assumptions um and then finally after
+
+00:16:40.160 --> 00:16:43.560
+you've picked your vocabulary and you've
+
+00:16:41.880 --> 00:16:45.720
+assigned a probability to each word in
+
+00:16:43.560 --> 00:16:47.800
+the vocabulary you then find a
+
+00:16:45.720 --> 00:16:49.639
+segmentation of the input that maximizes
+
+00:16:47.800 --> 00:16:52.600
+the unigram
+
+00:16:49.639 --> 00:16:54.880
+probabilities um so this is basically
+
+00:16:52.600 --> 00:16:56.519
+the idea of what's going on here um I'm
+
+00:16:54.880 --> 00:16:58.120
+not going to go into a lot of detail
+
+00:16:56.519 --> 00:17:00.560
+about this because most people are just
+
+00:16:58.120 --> 00:17:02.279
+going to be users of this algorithm so
+
+00:17:00.560 --> 00:17:06.240
+it's not super super
+
+00:17:02.279 --> 00:17:09.400
+important um the one important thing
+
+00:17:06.240 --> 00:17:11.240
+about this is that there's a library
+
+00:17:09.400 --> 00:17:15.520
+called sentence piece that's used very
+
+00:17:11.240 --> 00:17:19.199
+widely in order to build these um in
+
+00:17:15.520 --> 00:17:22.000
+order to build these subword units and
+
+00:17:19.199 --> 00:17:23.720
+uh basically what you do is you run the
+
+00:17:22.000 --> 00:17:27.600
+sentence piece
+
+00:17:23.720 --> 00:17:30.200
+train uh model or sorry uh program and
+
+00:17:27.600 --> 00:17:32.640
+that gives you uh you select your vocab
+
+00:17:30.200 --> 00:17:34.240
+size uh this also this character
+
+00:17:32.640 --> 00:17:36.120
+coverage is basically how well do you
+
+00:17:34.240 --> 00:17:39.760
+need to cover all of the characters in
+
+00:17:36.120 --> 00:17:41.840
+your vocabulary or in your input text um
+
+00:17:39.760 --> 00:17:45.240
+what model type do you use and then you
+
+00:17:41.840 --> 00:17:48.640
+run this uh sentence piece en code file
+
+00:17:45.240 --> 00:17:51.039
+uh to uh encode the output and split the
+
+00:17:48.640 --> 00:17:54.799
+output and there's also python bindings
+
+00:17:51.039 --> 00:17:56.240
+available for this and by the one thing
+
+00:17:54.799 --> 00:17:57.919
+that you should know is by default it
+
+00:17:56.240 --> 00:18:00.600
+uses the unigram model but it also
+
+00:17:57.919 --> 00:18:01.960
+supports EP in my experience it doesn't
+
+00:18:00.600 --> 00:18:05.159
+make a huge difference about which one
+
+00:18:01.960 --> 00:18:07.640
+you use the bigger thing is how um how
+
+00:18:05.159 --> 00:18:10.159
+big is your vocabulary size and if your
+
+00:18:07.640 --> 00:18:11.880
+vocabulary size is smaller then things
+
+00:18:10.159 --> 00:18:13.760
+will be more efficient but less
+
+00:18:11.880 --> 00:18:17.480
+expressive if your vocabulary size is
+
+00:18:13.760 --> 00:18:21.280
+bigger things will be um will
+
+00:18:17.480 --> 00:18:23.240
+be more expressive but less efficient
+
+00:18:21.280 --> 00:18:25.360
+and A good rule of thumb is like
+
+00:18:23.240 --> 00:18:26.960
+something like 60,000 to 80,000 is
+
+00:18:25.360 --> 00:18:29.120
+pretty reasonable if you're only doing
+
+00:18:26.960 --> 00:18:31.320
+English if you're spreading out to
+
+00:18:29.120 --> 00:18:32.600
+things that do other languages um which
+
+00:18:31.320 --> 00:18:35.960
+I'll talk about in a second then you
+
+00:18:32.600 --> 00:18:38.720
+need a much bigger B regular
+
+00:18:35.960 --> 00:18:40.559
+say so there's two considerations here
+
+00:18:38.720 --> 00:18:42.440
+two important considerations when using
+
+00:18:40.559 --> 00:18:46.320
+these models uh the first is
+
+00:18:42.440 --> 00:18:48.760
+multilinguality as I said so when you're
+
+00:18:46.320 --> 00:18:50.760
+using um subword
+
+00:18:48.760 --> 00:18:54.710
+models they're hard to use
+
+00:18:50.760 --> 00:18:55.840
+multilingually because as I said before
+
+00:18:54.710 --> 00:18:59.799
+[Music]
+
+00:18:55.840 --> 00:19:03.799
+they give longer strings to more
+
+00:18:59.799 --> 00:19:06.520
+frequent strings basically so then
+
+00:19:03.799 --> 00:19:09.559
+imagine what happens if 50% of your
+
+00:19:06.520 --> 00:19:11.919
+Corpus is English another 30% of your
+
+00:19:09.559 --> 00:19:15.400
+Corpus is
+
+00:19:11.919 --> 00:19:17.200
+other languages written in Latin script
+
+00:19:15.400 --> 00:19:21.720
+10% is
+
+00:19:17.200 --> 00:19:25.480
+Chinese uh 5% is cerlic script languages
+
+00:19:21.720 --> 00:19:27.240
+four 4% is 3% is Japanese and then you
+
+00:19:25.480 --> 00:19:31.080
+have like
+
+00:19:27.240 --> 00:19:33.320
+0.01% written in like burmes or
+
+00:19:31.080 --> 00:19:35.520
+something like that suddenly burmes just
+
+00:19:33.320 --> 00:19:37.400
+gets chunked up really really tiny
+
+00:19:35.520 --> 00:19:38.360
+really long sequences and it doesn't
+
+00:19:37.400 --> 00:19:45.559
+work as
+
+00:19:38.360 --> 00:19:45.559
+well um so one way that people fix this
+
+00:19:45.919 --> 00:19:50.520
+um and actually there's a really nice uh
+
+00:19:48.760 --> 00:19:52.600
+blog post about this called exploring
+
+00:19:50.520 --> 00:19:53.760
+B's vocabulary which I referenced here
+
+00:19:52.600 --> 00:19:58.039
+if you're interested in learning more
+
+00:19:53.760 --> 00:20:02.960
+about that um but one way that people
+
+00:19:58.039 --> 00:20:05.240
+were around this is if your
+
+00:20:02.960 --> 00:20:07.960
+actual uh data
+
+00:20:05.240 --> 00:20:11.559
+distribution looks like this like
+
+00:20:07.960 --> 00:20:11.559
+English uh
+
+00:20:17.039 --> 00:20:23.159
+Ty we actually sorry I took out the
+
+00:20:19.280 --> 00:20:23.159
+Indian languages in my example
+
+00:20:24.960 --> 00:20:30.159
+apologies
+
+00:20:27.159 --> 00:20:30.159
+so
+
+00:20:30.400 --> 00:20:35.919
+um what you do is you essentially create
+
+00:20:33.640 --> 00:20:40.000
+a different distribution that like
+
+00:20:35.919 --> 00:20:43.559
+downweights English a little bit and up
+
+00:20:40.000 --> 00:20:47.000
+weights up weights all of the other
+
+00:20:43.559 --> 00:20:49.480
+languages um so that you get more of
+
+00:20:47.000 --> 00:20:53.159
+other languages when creating so this is
+
+00:20:49.480 --> 00:20:53.159
+a common work around that you can do for
+
+00:20:54.200 --> 00:20:59.960
+this um the
+
+00:20:56.799 --> 00:21:03.000
+second problem with these is
+
+00:20:59.960 --> 00:21:08.000
+arbitrariness so as you saw in my
+
+00:21:03.000 --> 00:21:11.240
+example with bpe e s s and t and of
+
+00:21:08.000 --> 00:21:13.520
+board symbol all have the same probabil
+
+00:21:11.240 --> 00:21:16.960
+or have the same frequency right so if
+
+00:21:13.520 --> 00:21:21.520
+we get to that point do we segment es or
+
+00:21:16.960 --> 00:21:25.039
+do we seg uh EST or do we segment e
+
+00:21:21.520 --> 00:21:26.559
+s and so this is also a problem and it
+
+00:21:25.039 --> 00:21:29.000
+actually can affect your results
+
+00:21:26.559 --> 00:21:30.480
+especially if you like don't have a
+
+00:21:29.000 --> 00:21:31.760
+really strong vocabulary for the
+
+00:21:30.480 --> 00:21:33.279
+language you're working in or you're
+
+00:21:31.760 --> 00:21:37.200
+working in a new
+
+00:21:33.279 --> 00:21:40.159
+domain and so there's a few workarounds
+
+00:21:37.200 --> 00:21:41.520
+for this uh one workaround for this is
+
+00:21:40.159 --> 00:21:44.000
+uh called subword
+
+00:21:41.520 --> 00:21:46.279
+regularization and the way it works is
+
+00:21:44.000 --> 00:21:49.400
+instead
+
+00:21:46.279 --> 00:21:51.640
+of just having a single segmentation and
+
+00:21:49.400 --> 00:21:54.679
+getting the kind of
+
+00:21:51.640 --> 00:21:56.200
+maximally probable segmentation or the
+
+00:21:54.679 --> 00:21:58.480
+one the greedy one that you get out of
+
+00:21:56.200 --> 00:22:01.360
+BP instead you sample different
+
+00:21:58.480 --> 00:22:03.000
+segmentations in training time and use
+
+00:22:01.360 --> 00:22:05.720
+the different segmentations and that
+
+00:22:03.000 --> 00:22:09.200
+makes your model more robust to this
+
+00:22:05.720 --> 00:22:10.840
+kind of variation and that's also
+
+00:22:09.200 --> 00:22:15.679
+actually the reason why sentence piece
+
+00:22:10.840 --> 00:22:17.919
+was released was through this um subword
+
+00:22:15.679 --> 00:22:19.559
+regularization paper so that's also
+
+00:22:17.919 --> 00:22:22.720
+implemented in sentence piece if that's
+
+00:22:19.559 --> 00:22:22.720
+something you're interested in
+
+00:22:24.919 --> 00:22:32.520
+trying cool um are there any questions
+
+00:22:28.480 --> 00:22:32.520
+or discussions about this
+
+00:22:53.279 --> 00:22:56.279
+yeah
+
+00:22:56.960 --> 00:22:59.960
+already
+
+00:23:06.799 --> 00:23:11.080
+yeah so this is a good question um just
+
+00:23:08.960 --> 00:23:12.760
+to repeat the question it was like let's
+
+00:23:11.080 --> 00:23:16.080
+say we have a big
+
+00:23:12.760 --> 00:23:19.640
+multilingual um subword
+
+00:23:16.080 --> 00:23:23.440
+model and we want to add a new language
+
+00:23:19.640 --> 00:23:26.240
+in some way uh how can we reuse the
+
+00:23:23.440 --> 00:23:28.880
+existing model but add a new
+
+00:23:26.240 --> 00:23:31.080
+language it's a good question if you're
+
+00:23:28.880 --> 00:23:33.679
+only using it for subord
+
+00:23:31.080 --> 00:23:36.320
+segmentation um one one nice thing about
+
+00:23:33.679 --> 00:23:36.320
+the unigram
+
+00:23:36.400 --> 00:23:41.799
+model here is this is kind of a
+
+00:23:38.880 --> 00:23:43.679
+probabilistic model so it's very easy to
+
+00:23:41.799 --> 00:23:46.360
+do the kind of standard things that we
+
+00:23:43.679 --> 00:23:48.240
+do with probabilistic models which is
+
+00:23:46.360 --> 00:23:50.559
+like let's say we had an
+
+00:23:48.240 --> 00:23:53.919
+old uh an
+
+00:23:50.559 --> 00:23:56.880
+old vocabulary for
+
+00:23:53.919 --> 00:23:59.880
+this um we could just
+
+00:23:56.880 --> 00:23:59.880
+interpolate
+
+00:24:07.159 --> 00:24:12.320
+um we could interpolate like this and
+
+00:24:09.559 --> 00:24:13.840
+just you know uh combine the
+
+00:24:12.320 --> 00:24:17.080
+probabilities of the two and then use
+
+00:24:13.840 --> 00:24:19.520
+that combine probability in order to
+
+00:24:17.080 --> 00:24:21.320
+segment the new language um things like
+
+00:24:19.520 --> 00:24:24.159
+this have been uh done before but I
+
+00:24:21.320 --> 00:24:26.159
+don't remember the exact preferences uh
+
+00:24:24.159 --> 00:24:30.440
+for them but that that's what I would do
+
+00:24:26.159 --> 00:24:31.960
+here another interesting thing is um
+
+00:24:30.440 --> 00:24:35.399
+this might be getting a little ahead of
+
+00:24:31.960 --> 00:24:35.399
+myself but there's
+
+00:24:48.559 --> 00:24:58.279
+a there's a paper that talks about um
+
+00:24:55.360 --> 00:25:00.159
+how you can take things that or trained
+
+00:24:58.279 --> 00:25:03.360
+with another
+
+00:25:00.159 --> 00:25:05.480
+vocabulary and basically the idea is um
+
+00:25:03.360 --> 00:25:09.320
+you pre-train on whatever languages you
+
+00:25:05.480 --> 00:25:10.679
+have and then uh you learn embeddings in
+
+00:25:09.320 --> 00:25:11.880
+the new language you freeze the body of
+
+00:25:10.679 --> 00:25:14.360
+the model and learn embeddings in the
+
+00:25:11.880 --> 00:25:15.880
+new language so that's another uh method
+
+00:25:14.360 --> 00:25:19.080
+that's used it's called on the cross
+
+00:25:15.880 --> 00:25:19.080
+lingual printability
+
+00:25:21.840 --> 00:25:26.159
+representations and I'll probably talk
+
+00:25:23.840 --> 00:25:28.480
+about that in the last class of this uh
+
+00:25:26.159 --> 00:25:30.720
+thing so you can remember that
+
+00:25:28.480 --> 00:25:33.720
+then cool any other
+
+00:25:30.720 --> 00:25:33.720
+questions
+
+00:25:38.480 --> 00:25:42.640
+yeah is bag of words a first step to
+
+00:25:41.039 --> 00:25:46.640
+process your data if you want to do
+
+00:25:42.640 --> 00:25:49.919
+Generation Um do you mean like
+
+00:25:46.640 --> 00:25:52.440
+uh a word based model or a subword based
+
+00:25:49.919 --> 00:25:52.440
+model
+
+00:25:56.679 --> 00:26:00.480
+or like is
+
+00:26:02.360 --> 00:26:08.000
+this so the subword segmentation is the
+
+00:26:05.919 --> 00:26:10.640
+first step of creating just about any
+
+00:26:08.000 --> 00:26:13.080
+model nowadays like every model every
+
+00:26:10.640 --> 00:26:16.600
+model uses this and they usually use
+
+00:26:13.080 --> 00:26:21.520
+this either to segment characters or
+
+00:26:16.600 --> 00:26:23.559
+byes um characters are like Unicode code
+
+00:26:21.520 --> 00:26:25.799
+points so they actually correspond to an
+
+00:26:23.559 --> 00:26:28.279
+actual visual character and then bites
+
+00:26:25.799 --> 00:26:31.120
+are many unicode characters are like
+
+00:26:28.279 --> 00:26:35.000
+three by like a Chinese character is
+
+00:26:31.120 --> 00:26:37.159
+three byes if I remember correctly so um
+
+00:26:35.000 --> 00:26:38.640
+the bbased segmentation is nice because
+
+00:26:37.159 --> 00:26:41.240
+you don't even need to worry about unic
+
+00:26:38.640 --> 00:26:43.880
+code you can just do the like you can
+
+00:26:41.240 --> 00:26:45.640
+just segment the pile like literally as
+
+00:26:43.880 --> 00:26:49.440
+is and so a lot of people do it that way
+
+00:26:45.640 --> 00:26:53.279
+too uh llama as far as I know is
+
+00:26:49.440 --> 00:26:55.720
+bites I believe GPT is also bites um but
+
+00:26:53.279 --> 00:26:58.799
+pre previous to like three or four years
+
+00:26:55.720 --> 00:27:02.799
+ago people used SCS I
+
+00:26:58.799 --> 00:27:05.000
+cool um okay so this is really really
+
+00:27:02.799 --> 00:27:05.919
+important it's not like super complex
+
+00:27:05.000 --> 00:27:09.760
+and
+
+00:27:05.919 --> 00:27:13.039
+practically uh you will just maybe maybe
+
+00:27:09.760 --> 00:27:15.840
+train or maybe just use a tokenizer um
+
+00:27:13.039 --> 00:27:18.559
+but uh that that's an important thing to
+
+00:27:15.840 --> 00:27:20.760
+me cool uh next I'd like to move on to
+
+00:27:18.559 --> 00:27:24.399
+continuous word eddings
+
+00:27:20.760 --> 00:27:26.720
+so the basic idea is that previously we
+
+00:27:24.399 --> 00:27:28.240
+represented words with a sparse Vector
+
+00:27:26.720 --> 00:27:30.120
+uh with a single one
+
+00:27:28.240 --> 00:27:31.960
+also known as one poot Vector so it
+
+00:27:30.120 --> 00:27:35.720
+looked a little bit like
+
+00:27:31.960 --> 00:27:37.640
+this and instead what continuous word
+
+00:27:35.720 --> 00:27:39.640
+embeddings do is they look up a dense
+
+00:27:37.640 --> 00:27:42.320
+vector and so you get a dense
+
+00:27:39.640 --> 00:27:45.760
+representation where the entire Vector
+
+00:27:42.320 --> 00:27:45.760
+has continuous values in
+
+00:27:46.000 --> 00:27:51.919
+it and I talked about a bag of words
+
+00:27:49.200 --> 00:27:54.320
+model but we could also create a
+
+00:27:51.919 --> 00:27:58.360
+continuous bag of words model and the
+
+00:27:54.320 --> 00:28:01.159
+way this works is you look up the
+
+00:27:58.360 --> 00:28:03.720
+values of each Vector the embeddings of
+
+00:28:01.159 --> 00:28:06.320
+each Vector this gives you an embedding
+
+00:28:03.720 --> 00:28:08.440
+Vector for the entire sequence and then
+
+00:28:06.320 --> 00:28:15.120
+you multiply this by a weight
+
+00:28:08.440 --> 00:28:17.559
+Matrix uh where the so this is column so
+
+00:28:15.120 --> 00:28:19.960
+the rows of the weight Matrix uh
+
+00:28:17.559 --> 00:28:22.919
+correspond to to the size of this
+
+00:28:19.960 --> 00:28:24.760
+continuous embedding and The Columns of
+
+00:28:22.919 --> 00:28:28.320
+the weight Matrix would correspond to
+
+00:28:24.760 --> 00:28:30.919
+the uh overall um
+
+00:28:28.320 --> 00:28:32.559
+to the overall uh number of labels that
+
+00:28:30.919 --> 00:28:36.919
+you would have here and then that would
+
+00:28:32.559 --> 00:28:40.120
+give you sces and so this uh basically
+
+00:28:36.919 --> 00:28:41.679
+what this is saying is each Vector now
+
+00:28:40.120 --> 00:28:43.440
+instead of having a single thing that
+
+00:28:41.679 --> 00:28:46.799
+represents which vocabulary item you're
+
+00:28:43.440 --> 00:28:48.679
+looking at uh you would kind of hope
+
+00:28:46.799 --> 00:28:52.120
+that you would get vectors where words
+
+00:28:48.679 --> 00:28:54.919
+that are similar uh by some mention of
+
+00:28:52.120 --> 00:28:57.760
+by some concept of similar like syntatic
+
+00:28:54.919 --> 00:28:59.679
+uh syntax semantics whether they're in
+
+00:28:57.760 --> 00:29:03.120
+the same language or not are close in
+
+00:28:59.679 --> 00:29:06.679
+the vector space and each Vector element
+
+00:29:03.120 --> 00:29:09.399
+is a feature uh so for example each
+
+00:29:06.679 --> 00:29:11.519
+Vector element corresponds to is this an
+
+00:29:09.399 --> 00:29:14.960
+animate object or is this a positive
+
+00:29:11.519 --> 00:29:17.399
+word or other Vector other things like
+
+00:29:14.960 --> 00:29:19.399
+that so just to give an example here
+
+00:29:17.399 --> 00:29:21.760
+this is totally made up I just made it
+
+00:29:19.399 --> 00:29:24.360
+in keynote so it's not natural Vector
+
+00:29:21.760 --> 00:29:26.279
+space but to Ill illustrate the concept
+
+00:29:24.360 --> 00:29:27.960
+I showed here what if we had a
+
+00:29:26.279 --> 00:29:30.240
+two-dimensional vector
+
+00:29:27.960 --> 00:29:33.399
+space where the two-dimensional Vector
+
+00:29:30.240 --> 00:29:36.240
+space the xais here is corresponding to
+
+00:29:33.399 --> 00:29:38.679
+whether it's animate or not and the the
+
+00:29:36.240 --> 00:29:41.480
+Y AIS here is corresponding to whether
+
+00:29:38.679 --> 00:29:44.080
+it's like positive sentiment or not and
+
+00:29:41.480 --> 00:29:46.399
+so this is kind of like our ideal uh
+
+00:29:44.080 --> 00:29:49.799
+goal
+
+00:29:46.399 --> 00:29:52.279
+here um so why would we want to do this
+
+00:29:49.799 --> 00:29:52.279
+yeah sorry
+
+00:29:56.320 --> 00:30:03.399
+guys what do the like in the one it's
+
+00:30:00.919 --> 00:30:06.399
+one
+
+00:30:03.399 --> 00:30:06.399
+yep
+
+00:30:07.200 --> 00:30:12.519
+like so what would the four entries do
+
+00:30:09.880 --> 00:30:14.799
+here the four entries here are learned
+
+00:30:12.519 --> 00:30:17.039
+so they are um they're learned just
+
+00:30:14.799 --> 00:30:18.519
+together with the model um and I'm going
+
+00:30:17.039 --> 00:30:22.120
+to talk about exactly how we learn them
+
+00:30:18.519 --> 00:30:24.000
+soon but the the final goal is that
+
+00:30:22.120 --> 00:30:25.399
+after learning has happened they look
+
+00:30:24.000 --> 00:30:26.799
+they have these two properties like
+
+00:30:25.399 --> 00:30:28.600
+similar words are close together in the
+
+00:30:26.799 --> 00:30:30.080
+vectorace
+
+00:30:28.600 --> 00:30:32.640
+and
+
+00:30:30.080 --> 00:30:35.679
+um that's like number one that's the
+
+00:30:32.640 --> 00:30:37.600
+most important and then number two is
+
+00:30:35.679 --> 00:30:39.279
+ideally these uh features would have
+
+00:30:37.600 --> 00:30:41.200
+some meaning uh maybe human
+
+00:30:39.279 --> 00:30:44.720
+interpretable meaning maybe not human
+
+00:30:41.200 --> 00:30:47.880
+interpretable meaning but
+
+00:30:44.720 --> 00:30:50.880
+yeah so um one thing that I should
+
+00:30:47.880 --> 00:30:53.159
+mention is I I showed a contrast between
+
+00:30:50.880 --> 00:30:55.159
+the bag of words uh the one hot
+
+00:30:53.159 --> 00:30:57.000
+representations here and the dense
+
+00:30:55.159 --> 00:31:00.880
+representations here and I used this
+
+00:30:57.000 --> 00:31:03.880
+look look up operation for both of them
+
+00:31:00.880 --> 00:31:07.399
+and this this lookup
+
+00:31:03.880 --> 00:31:09.559
+operation actually um can be viewed as
+
+00:31:07.399 --> 00:31:11.799
+grabbing a single Vector from a big
+
+00:31:09.559 --> 00:31:14.919
+Matrix of word
+
+00:31:11.799 --> 00:31:17.760
+embeddings and
+
+00:31:14.919 --> 00:31:19.760
+so the way it can work is like we have
+
+00:31:17.760 --> 00:31:22.919
+this big vector and then we look up word
+
+00:31:19.760 --> 00:31:25.919
+number two in a zero index Matrix and it
+
+00:31:22.919 --> 00:31:27.799
+would just grab this out of that Matrix
+
+00:31:25.919 --> 00:31:29.880
+and that's practically what most like
+
+00:31:27.799 --> 00:31:32.240
+deep learning libraries or or whatever
+
+00:31:29.880 --> 00:31:35.840
+Library you use are going to be
+
+00:31:32.240 --> 00:31:38.000
+doing but another uh way you can view it
+
+00:31:35.840 --> 00:31:40.880
+is you can view it as multiplying by a
+
+00:31:38.000 --> 00:31:43.880
+one hot vector and so you have this
+
+00:31:40.880 --> 00:31:48.679
+Vector uh exactly the same Matrix uh but
+
+00:31:43.880 --> 00:31:50.799
+you just multiply by a vector uh 0 1 z z
+
+00:31:48.679 --> 00:31:55.720
+and that gives you exactly the same
+
+00:31:50.799 --> 00:31:58.200
+things um so the Practical imple
+
+00:31:55.720 --> 00:31:59.720
+implementations of this uh uh tend to be
+
+00:31:58.200 --> 00:32:01.279
+the first one because the first one's a
+
+00:31:59.720 --> 00:32:04.679
+lot faster to implement you don't need
+
+00:32:01.279 --> 00:32:06.760
+to multiply like this big thing by a
+
+00:32:04.679 --> 00:32:11.000
+huge Vector but there
+
+00:32:06.760 --> 00:32:13.880
+are advantages of knowing the second one
+
+00:32:11.000 --> 00:32:15.519
+uh just to give an example what if you
+
+00:32:13.880 --> 00:32:19.600
+for whatever reason you came up with
+
+00:32:15.519 --> 00:32:21.440
+like an a crazy model that predicts a
+
+00:32:19.600 --> 00:32:24.120
+probability distribution over words
+
+00:32:21.440 --> 00:32:25.720
+instead of just words maybe it's a
+
+00:32:24.120 --> 00:32:27.679
+language model that has an idea of what
+
+00:32:25.720 --> 00:32:30.200
+the next word is going to look like
+
+00:32:27.679 --> 00:32:32.159
+and maybe your um maybe your model
+
+00:32:30.200 --> 00:32:35.279
+thinks the next word has a 50%
+
+00:32:32.159 --> 00:32:36.600
+probability of being capped 30%
+
+00:32:35.279 --> 00:32:42.279
+probability of being
+
+00:32:36.600 --> 00:32:44.960
+dog and uh 2% probability uh sorry uh
+
+00:32:42.279 --> 00:32:47.200
+20% probability being
+
+00:32:44.960 --> 00:32:50.000
+bir you can take this vector and
+
+00:32:47.200 --> 00:32:51.480
+multiply it by The Matrix and get like a
+
+00:32:50.000 --> 00:32:53.639
+word embedding that's kind of a mix of
+
+00:32:51.480 --> 00:32:55.639
+all of those word which might be
+
+00:32:53.639 --> 00:32:57.960
+interesting and let you do creative
+
+00:32:55.639 --> 00:33:02.120
+things so um knowing that these two
+
+00:32:57.960 --> 00:33:05.360
+things are the same are the same is kind
+
+00:33:02.120 --> 00:33:05.360
+of useful for that kind of
+
+00:33:05.919 --> 00:33:11.480
+thing um any any questions about this
+
+00:33:09.120 --> 00:33:13.919
+I'm G to talk about how we train next so
+
+00:33:11.480 --> 00:33:18.159
+maybe maybe I can goow into
+
+00:33:13.919 --> 00:33:23.159
+that okay cool so how do we get the
+
+00:33:18.159 --> 00:33:25.840
+vectors uh like the question uh so up
+
+00:33:23.159 --> 00:33:27.519
+until now we trained a bag of words
+
+00:33:25.840 --> 00:33:29.080
+model and the way we trained a bag of
+
+00:33:27.519 --> 00:33:31.159
+words model was using the structured
+
+00:33:29.080 --> 00:33:35.440
+perceptron algorithm where if the model
+
+00:33:31.159 --> 00:33:39.639
+got the answer wrong we would either
+
+00:33:35.440 --> 00:33:42.799
+increment or decrement the embeddings
+
+00:33:39.639 --> 00:33:45.080
+based on whether uh whether the label
+
+00:33:42.799 --> 00:33:46.559
+was positive or negative right so I
+
+00:33:45.080 --> 00:33:48.919
+showed an example of this very simple
+
+00:33:46.559 --> 00:33:51.039
+algorithm you don't even uh need to
+
+00:33:48.919 --> 00:33:52.480
+write any like numpy or anything like
+
+00:33:51.039 --> 00:33:55.919
+that to implement that
+
+00:33:52.480 --> 00:33:59.559
+algorithm uh so here here it is so we
+
+00:33:55.919 --> 00:34:02.320
+have like 4X why in uh data we extract
+
+00:33:59.559 --> 00:34:04.639
+the features we run the classifier uh we
+
+00:34:02.320 --> 00:34:07.440
+have the predicted why and then we
+
+00:34:04.639 --> 00:34:09.480
+increment or decrement
+
+00:34:07.440 --> 00:34:12.679
+features but how do we train more
+
+00:34:09.480 --> 00:34:15.599
+complex models so I think most people
+
+00:34:12.679 --> 00:34:17.079
+here have taken a uh machine learning
+
+00:34:15.599 --> 00:34:19.159
+class of some kind so this will be
+
+00:34:17.079 --> 00:34:21.079
+reviewed for a lot of people uh but
+
+00:34:19.159 --> 00:34:22.280
+basically we do this uh by doing
+
+00:34:21.079 --> 00:34:24.839
+gradient
+
+00:34:22.280 --> 00:34:27.240
+descent and in order to do so we write
+
+00:34:24.839 --> 00:34:29.919
+down a loss function calculate the
+
+00:34:27.240 --> 00:34:30.919
+derivatives of the L function with
+
+00:34:29.919 --> 00:34:35.079
+respect to the
+
+00:34:30.919 --> 00:34:37.320
+parameters and move uh the parameters in
+
+00:34:35.079 --> 00:34:40.839
+the direction that reduces the loss
+
+00:34:37.320 --> 00:34:42.720
+mtion and so specifically for this bag
+
+00:34:40.839 --> 00:34:45.560
+of words or continuous bag of words
+
+00:34:42.720 --> 00:34:48.240
+model um we want this loss of function
+
+00:34:45.560 --> 00:34:50.839
+to be a loss function that gets lower as
+
+00:34:48.240 --> 00:34:52.240
+the model gets better and I'm going to
+
+00:34:50.839 --> 00:34:54.000
+give two examples from binary
+
+00:34:52.240 --> 00:34:57.400
+classification both of these are used in
+
+00:34:54.000 --> 00:34:58.839
+NLP models uh reasonably frequently
+
+00:34:57.400 --> 00:35:01.440
+uh there's a bunch of other loss
+
+00:34:58.839 --> 00:35:02.800
+functions but these are kind of the two
+
+00:35:01.440 --> 00:35:05.480
+major
+
+00:35:02.800 --> 00:35:08.160
+ones so the first one um which is
+
+00:35:05.480 --> 00:35:10.160
+actually less frequent is the hinge loss
+
+00:35:08.160 --> 00:35:13.400
+and then the second one is taking a
+
+00:35:10.160 --> 00:35:15.800
+sigmoid and then doing negative log
+
+00:35:13.400 --> 00:35:19.760
+likelyhood so the hinge loss basically
+
+00:35:15.800 --> 00:35:22.760
+what we do is we uh take the max of the
+
+00:35:19.760 --> 00:35:26.119
+label times the score that is output by
+
+00:35:22.760 --> 00:35:29.200
+the model and zero and what this looks
+
+00:35:26.119 --> 00:35:33.480
+like is we have a hinged loss uh where
+
+00:35:29.200 --> 00:35:36.880
+if Y is equal to one the loss if Y is
+
+00:35:33.480 --> 00:35:39.520
+greater than zero is zero so as long as
+
+00:35:36.880 --> 00:35:42.680
+we get basically as long as we get the
+
+00:35:39.520 --> 00:35:45.079
+answer right there's no loss um as the
+
+00:35:42.680 --> 00:35:47.400
+answer gets more wrong the loss gets
+
+00:35:45.079 --> 00:35:49.880
+worse like this and then similarly if
+
+00:35:47.400 --> 00:35:53.160
+the label is negative if we get a
+
+00:35:49.880 --> 00:35:54.839
+negative score uh then we get zero loss
+
+00:35:53.160 --> 00:35:55.800
+and the loss increases if we have a
+
+00:35:54.839 --> 00:35:58.800
+positive
+
+00:35:55.800 --> 00:36:00.800
+score so the sigmoid plus negative log
+
+00:35:58.800 --> 00:36:05.440
+likelihood the way this works is you
+
+00:36:00.800 --> 00:36:07.400
+multiply y * the score here and um then
+
+00:36:05.440 --> 00:36:09.960
+we have the sigmoid function which is
+
+00:36:07.400 --> 00:36:14.079
+just kind of a nice function that looks
+
+00:36:09.960 --> 00:36:15.440
+like this with zero and one centered
+
+00:36:14.079 --> 00:36:19.480
+around
+
+00:36:15.440 --> 00:36:21.240
+zero and then we take the negative log
+
+00:36:19.480 --> 00:36:22.319
+of this sigmoid function or the negative
+
+00:36:21.240 --> 00:36:27.160
+log
+
+00:36:22.319 --> 00:36:28.520
+likelihood and that gives us a uh L that
+
+00:36:27.160 --> 00:36:30.440
+looks a little bit like this so
+
+00:36:28.520 --> 00:36:32.640
+basically you can see that these look
+
+00:36:30.440 --> 00:36:36.040
+very similar right the difference being
+
+00:36:32.640 --> 00:36:37.760
+that the hinge loss is uh sharp and we
+
+00:36:36.040 --> 00:36:41.119
+get exactly a zero loss if we get the
+
+00:36:37.760 --> 00:36:44.319
+answer right and the sigmoid is smooth
+
+00:36:41.119 --> 00:36:48.440
+uh and we never get a zero
+
+00:36:44.319 --> 00:36:50.680
+loss um so does anyone have an idea of
+
+00:36:48.440 --> 00:36:53.119
+the benefits and disadvantages of
+
+00:36:50.680 --> 00:36:55.680
+these I kind of flashed one on the
+
+00:36:53.119 --> 00:36:57.599
+screen already
+
+00:36:55.680 --> 00:36:59.400
+but
+
+00:36:57.599 --> 00:37:01.359
+so I flash that on the screen so I'll
+
+00:36:59.400 --> 00:37:03.680
+give this one and then I can have a quiz
+
+00:37:01.359 --> 00:37:06.319
+about the sign but the the hinge glass
+
+00:37:03.680 --> 00:37:07.720
+is more closely linked to accuracy and
+
+00:37:06.319 --> 00:37:10.400
+the reason why it's more closely linked
+
+00:37:07.720 --> 00:37:13.640
+to accuracy is because basically we will
+
+00:37:10.400 --> 00:37:16.079
+get a zero loss if the model gets the
+
+00:37:13.640 --> 00:37:18.319
+answer right so when the model gets all
+
+00:37:16.079 --> 00:37:20.240
+of the answers right we will just stop
+
+00:37:18.319 --> 00:37:22.760
+updating our model whatsoever because we
+
+00:37:20.240 --> 00:37:25.440
+never we don't have any loss whatsoever
+
+00:37:22.760 --> 00:37:27.720
+and the gradient of the loss is zero um
+
+00:37:25.440 --> 00:37:29.960
+what about the sigmoid uh a negative log
+
+00:37:27.720 --> 00:37:33.160
+likelihood uh there there's kind of two
+
+00:37:29.960 --> 00:37:36.160
+major advantages of this anyone want to
+
+00:37:33.160 --> 00:37:36.160
+review their machine learning
+
+00:37:38.240 --> 00:37:41.800
+test sorry what was
+
+00:37:43.800 --> 00:37:49.960
+that for for R uh yeah maybe there's a
+
+00:37:48.200 --> 00:37:51.319
+more direct I think I know what you're
+
+00:37:49.960 --> 00:37:54.560
+saying but maybe there's a more direct
+
+00:37:51.319 --> 00:37:54.560
+way to say that um
+
+00:37:54.839 --> 00:38:00.760
+yeah yeah so the gradient is nonzero
+
+00:37:57.560 --> 00:38:04.240
+everywhere and uh the gradient also kind
+
+00:38:00.760 --> 00:38:05.839
+of increases as your score gets worse so
+
+00:38:04.240 --> 00:38:08.440
+those are that's one advantage it makes
+
+00:38:05.839 --> 00:38:11.240
+it easier to optimize models um another
+
+00:38:08.440 --> 00:38:13.839
+one linked to the ROC score but maybe we
+
+00:38:11.240 --> 00:38:13.839
+could say it more
+
+00:38:16.119 --> 00:38:19.400
+directly any
+
+00:38:20.040 --> 00:38:26.920
+ideas okay um basically the sigmoid can
+
+00:38:23.240 --> 00:38:30.160
+be interpreted as a probability so um if
+
+00:38:26.920 --> 00:38:32.839
+the the sigmoid is between Zer and one
+
+00:38:30.160 --> 00:38:34.640
+uh and because it's between zero and one
+
+00:38:32.839 --> 00:38:36.720
+we can say the sigmoid is a
+
+00:38:34.640 --> 00:38:38.640
+probability um and that can be useful
+
+00:38:36.720 --> 00:38:40.119
+for various things like if we want a
+
+00:38:38.640 --> 00:38:41.960
+downstream model or if we want a
+
+00:38:40.119 --> 00:38:45.480
+confidence prediction out of the model
+
+00:38:41.960 --> 00:38:48.200
+so those are two uh advantages of using
+
+00:38:45.480 --> 00:38:49.920
+a s plus negative log likelihood there's
+
+00:38:48.200 --> 00:38:53.160
+no probabilistic interpretation to
+
+00:38:49.920 --> 00:38:56.560
+something transing theas
+
+00:38:53.160 --> 00:38:59.200
+basically cool um so the next thing that
+
+00:38:56.560 --> 00:39:01.240
+that we do is we calculate derivatives
+
+00:38:59.200 --> 00:39:04.040
+and we calculate the derivative of the
+
+00:39:01.240 --> 00:39:05.920
+parameter given the loss function um to
+
+00:39:04.040 --> 00:39:09.839
+give an example of the bag of words
+
+00:39:05.920 --> 00:39:13.480
+model and the hinge loss um the hinge
+
+00:39:09.839 --> 00:39:16.480
+loss as I said is the max of the score
+
+00:39:13.480 --> 00:39:19.359
+and times y in the bag of words model
+
+00:39:16.480 --> 00:39:22.640
+the score was the frequency of that
+
+00:39:19.359 --> 00:39:25.880
+vocabulary item in the input multiplied
+
+00:39:22.640 --> 00:39:27.680
+by the weight here and so if we this is
+
+00:39:25.880 --> 00:39:29.520
+a simple a function that I can just do
+
+00:39:27.680 --> 00:39:34.440
+the derivative by hand and if I do the
+
+00:39:29.520 --> 00:39:36.920
+deriva by hand what comes out is if y *
+
+00:39:34.440 --> 00:39:39.319
+this value is greater than zero so in
+
+00:39:36.920 --> 00:39:44.640
+other words if this Max uh picks this
+
+00:39:39.319 --> 00:39:48.319
+instead of this then the derivative is y
+
+00:39:44.640 --> 00:39:52.359
+* stre and otherwise uh it
+
+00:39:48.319 --> 00:39:52.359
+is in the opposite
+
+00:39:55.400 --> 00:40:00.160
+direction
+
+00:39:56.920 --> 00:40:02.839
+then uh optimizing gradients uh we do
+
+00:40:00.160 --> 00:40:06.200
+standard uh in standard stochastic
+
+00:40:02.839 --> 00:40:07.839
+gradient descent uh which is the most
+
+00:40:06.200 --> 00:40:10.920
+standard optimization algorithm for
+
+00:40:07.839 --> 00:40:14.440
+these models uh we basically have a
+
+00:40:10.920 --> 00:40:17.440
+gradient over uh you take the gradient
+
+00:40:14.440 --> 00:40:20.040
+over the parameter of the loss function
+
+00:40:17.440 --> 00:40:22.480
+and we call it GT so here um sorry I
+
+00:40:20.040 --> 00:40:25.599
+switched my terminology between W and
+
+00:40:22.480 --> 00:40:28.280
+Theta so this could be W uh the previous
+
+00:40:25.599 --> 00:40:31.000
+value of w
+
+00:40:28.280 --> 00:40:35.440
+um and this is the gradient of the loss
+
+00:40:31.000 --> 00:40:37.040
+and then uh we take the previous value
+
+00:40:35.440 --> 00:40:39.680
+and then we subtract out the learning
+
+00:40:37.040 --> 00:40:39.680
+rate times the
+
+00:40:40.680 --> 00:40:45.720
+gradient and uh there are many many
+
+00:40:43.200 --> 00:40:47.280
+other optimization options uh I'll cover
+
+00:40:45.720 --> 00:40:50.960
+the more frequent one called Adam at the
+
+00:40:47.280 --> 00:40:54.319
+end of this uh this lecture but um this
+
+00:40:50.960 --> 00:40:57.160
+is the basic way of optimizing the
+
+00:40:54.319 --> 00:41:00.599
+model so
+
+00:40:57.160 --> 00:41:03.359
+then my question now is what is this
+
+00:41:00.599 --> 00:41:07.000
+algorithm with respect
+
+00:41:03.359 --> 00:41:10.119
+to this is an algorithm that is
+
+00:41:07.000 --> 00:41:12.280
+taking that has a loss function it's
+
+00:41:10.119 --> 00:41:14.079
+calculating derivatives and it's
+
+00:41:12.280 --> 00:41:17.240
+optimizing gradients using stochastic
+
+00:41:14.079 --> 00:41:18.839
+gradient descent so does anyone have a
+
+00:41:17.240 --> 00:41:20.960
+guess about what the loss function is
+
+00:41:18.839 --> 00:41:23.520
+here and maybe what is the learning rate
+
+00:41:20.960 --> 00:41:23.520
+of stas
+
+00:41:24.319 --> 00:41:29.480
+gradient I kind of gave you a hint about
+
+00:41:26.599 --> 00:41:29.480
+the L one
+
+00:41:31.640 --> 00:41:37.839
+actually and just to recap what this is
+
+00:41:34.440 --> 00:41:41.440
+doing here it's um if predicted Y is
+
+00:41:37.839 --> 00:41:44.560
+equal to Y then it is moving the uh the
+
+00:41:41.440 --> 00:41:48.240
+future weights in the direction of Y
+
+00:41:44.560 --> 00:41:48.240
+times the frequency
+
+00:41:52.599 --> 00:41:56.960
+Vector
+
+00:41:55.240 --> 00:41:59.079
+yeah
+
+00:41:56.960 --> 00:42:01.640
+yeah exactly so the loss function is
+
+00:41:59.079 --> 00:42:05.800
+hinge loss and the learning rate is one
+
+00:42:01.640 --> 00:42:07.880
+um and just to show how that you know
+
+00:42:05.800 --> 00:42:12.359
+corresponds we have this if statement
+
+00:42:07.880 --> 00:42:12.359
+here and we have the increment of the
+
+00:42:12.960 --> 00:42:20.240
+features and this is what the um what
+
+00:42:16.920 --> 00:42:21.599
+the L sorry the derivative looked like
+
+00:42:20.240 --> 00:42:24.240
+so we have
+
+00:42:21.599 --> 00:42:26.920
+if this is moving in the right direction
+
+00:42:24.240 --> 00:42:29.520
+for the label uh then we increment
+
+00:42:26.920 --> 00:42:31.599
+otherwise we do nothing so
+
+00:42:29.520 --> 00:42:33.559
+basically you can see that even this
+
+00:42:31.599 --> 00:42:35.200
+really simple algorithm that I you know
+
+00:42:33.559 --> 00:42:37.480
+implemented with a few lines of python
+
+00:42:35.200 --> 00:42:38.839
+is essentially equivalent to this uh
+
+00:42:37.480 --> 00:42:40.760
+stochastic gradient descent that we
+
+00:42:38.839 --> 00:42:44.559
+doing
+
+00:42:40.760 --> 00:42:46.359
+models so the good news about this is
+
+00:42:44.559 --> 00:42:48.359
+you know this this is really simple but
+
+00:42:46.359 --> 00:42:50.599
+it only really works forit like a bag of
+
+00:42:48.359 --> 00:42:55.400
+words model or a simple feature based
+
+00:42:50.599 --> 00:42:57.200
+model uh but it opens up a lot of uh new
+
+00:42:55.400 --> 00:43:00.440
+possibilities for how we can optimize
+
+00:42:57.200 --> 00:43:01.599
+models and in particular I mentioned uh
+
+00:43:00.440 --> 00:43:04.839
+that there was a problem with
+
+00:43:01.599 --> 00:43:08.200
+combination features last class like
+
+00:43:04.839 --> 00:43:11.200
+don't hate and don't love are not just
+
+00:43:08.200 --> 00:43:12.760
+you know hate plus don't and love plus
+
+00:43:11.200 --> 00:43:14.119
+don't it's actually the combination of
+
+00:43:12.760 --> 00:43:17.680
+the two is really
+
+00:43:14.119 --> 00:43:20.160
+important and so um yeah just to give an
+
+00:43:17.680 --> 00:43:23.440
+example we have don't love is maybe bad
+
+00:43:20.160 --> 00:43:26.960
+uh nothing I don't love is very
+
+00:43:23.440 --> 00:43:30.960
+good and so in order
+
+00:43:26.960 --> 00:43:34.040
+to solve this problem we turn to neural
+
+00:43:30.960 --> 00:43:37.160
+networks and the way we do this is we
+
+00:43:34.040 --> 00:43:39.119
+have a lookup of dense embeddings sorry
+
+00:43:37.160 --> 00:43:41.839
+I actually I just realized my coloring
+
+00:43:39.119 --> 00:43:44.119
+is off I was using red to indicate dense
+
+00:43:41.839 --> 00:43:46.480
+embeddings so this should be maybe red
+
+00:43:44.119 --> 00:43:49.319
+instead of blue but um we take these
+
+00:43:46.480 --> 00:43:51.200
+stents embeddings and then we create
+
+00:43:49.319 --> 00:43:53.720
+some complicated function to extract
+
+00:43:51.200 --> 00:43:55.079
+combination features um and then use
+
+00:43:53.720 --> 00:43:57.359
+those to calculate
+
+00:43:55.079 --> 00:44:02.200
+scores
+
+00:43:57.359 --> 00:44:04.480
+um and so we calculate these combination
+
+00:44:02.200 --> 00:44:08.240
+features and what we want to do is we
+
+00:44:04.480 --> 00:44:12.880
+want to extract vectors from the input
+
+00:44:08.240 --> 00:44:12.880
+where each Vector has features
+
+00:44:15.839 --> 00:44:21.040
+um sorry this is in the wrong order so
+
+00:44:18.240 --> 00:44:22.559
+I'll I'll get back to this um so this
+
+00:44:21.040 --> 00:44:25.319
+this was talking about the The
+
+00:44:22.559 --> 00:44:27.200
+Continuous bag of words features so the
+
+00:44:25.319 --> 00:44:30.960
+problem with the continuous bag of words
+
+00:44:27.200 --> 00:44:30.960
+features was we were extracting
+
+00:44:31.359 --> 00:44:36.359
+features
+
+00:44:33.079 --> 00:44:36.359
+um like
+
+00:44:36.839 --> 00:44:41.400
+this but then we were directly using the
+
+00:44:39.760 --> 00:44:43.359
+the feature the dense features that we
+
+00:44:41.400 --> 00:44:45.559
+extracted to make predictions without
+
+00:44:43.359 --> 00:44:48.839
+actually allowing for any interactions
+
+00:44:45.559 --> 00:44:51.839
+between the features um and
+
+00:44:48.839 --> 00:44:55.160
+so uh neural networks the way we fix
+
+00:44:51.839 --> 00:44:57.079
+this is we first extract these features
+
+00:44:55.160 --> 00:44:59.440
+uh we take these these features of each
+
+00:44:57.079 --> 00:45:04.000
+word embedding and then we run them
+
+00:44:59.440 --> 00:45:07.240
+through uh kind of linear transforms in
+
+00:45:04.000 --> 00:45:09.880
+nonlinear uh like linear multiplications
+
+00:45:07.240 --> 00:45:10.880
+and then nonlinear transforms to extract
+
+00:45:09.880 --> 00:45:13.920
+additional
+
+00:45:10.880 --> 00:45:15.839
+features and uh finally run this through
+
+00:45:13.920 --> 00:45:18.640
+several layers and then use the
+
+00:45:15.839 --> 00:45:21.119
+resulting features to make our
+
+00:45:18.640 --> 00:45:23.200
+predictions and when we do this this
+
+00:45:21.119 --> 00:45:25.319
+allows us to do more uh interesting
+
+00:45:23.200 --> 00:45:28.319
+things so like for example we could
+
+00:45:25.319 --> 00:45:30.000
+learn feature combination a node in the
+
+00:45:28.319 --> 00:45:32.599
+second layer might be feature one and
+
+00:45:30.000 --> 00:45:35.240
+feature five are active so that could be
+
+00:45:32.599 --> 00:45:38.680
+like feature one corresponds to negative
+
+00:45:35.240 --> 00:45:43.640
+sentiment words like hate
+
+00:45:38.680 --> 00:45:45.839
+despise um and other things like that so
+
+00:45:43.640 --> 00:45:50.079
+for hate and despise feature one would
+
+00:45:45.839 --> 00:45:53.119
+have a high value like 8.0 and then
+
+00:45:50.079 --> 00:45:55.480
+7.2 and then we also have negation words
+
+00:45:53.119 --> 00:45:57.040
+like don't or not or something like that
+
+00:45:55.480 --> 00:46:00.040
+and those would
+
+00:45:57.040 --> 00:46:00.040
+have
+
+00:46:03.720 --> 00:46:08.640
+don't would have a high value for like 2
+
+00:46:11.880 --> 00:46:15.839
+five and so these would be the word
+
+00:46:14.200 --> 00:46:18.040
+embeddings where each word embedding
+
+00:46:15.839 --> 00:46:20.599
+corresponded to you know features of the
+
+00:46:18.040 --> 00:46:23.480
+words and
+
+00:46:20.599 --> 00:46:25.480
+then um after that we would extract
+
+00:46:23.480 --> 00:46:29.319
+feature combinations in this second
+
+00:46:25.480 --> 00:46:32.079
+layer that say oh we see at least one
+
+00:46:29.319 --> 00:46:33.760
+word where the first feature is active
+
+00:46:32.079 --> 00:46:36.359
+and we see at least one word where the
+
+00:46:33.760 --> 00:46:37.920
+fifth feature is active so now that
+
+00:46:36.359 --> 00:46:40.640
+allows us to capture the fact that we
+
+00:46:37.920 --> 00:46:42.319
+saw like don't hate or don't despise or
+
+00:46:40.640 --> 00:46:44.559
+not hate or not despise or something
+
+00:46:42.319 --> 00:46:44.559
+like
+
+00:46:45.079 --> 00:46:51.760
+that so this is the way uh kind of this
+
+00:46:49.680 --> 00:46:54.839
+is a deep uh continuous bag of words
+
+00:46:51.760 --> 00:46:56.839
+model um this actually was proposed in
+
+00:46:54.839 --> 00:46:58.119
+205 15 I don't think I have the
+
+00:46:56.839 --> 00:47:02.599
+reference on the slide but I think it's
+
+00:46:58.119 --> 00:47:05.040
+in the notes um on the website and
+
+00:47:02.599 --> 00:47:07.200
+actually at that point in time they
+
+00:47:05.040 --> 00:47:09.200
+demon there were several interesting
+
+00:47:07.200 --> 00:47:11.960
+results that showed that even this like
+
+00:47:09.200 --> 00:47:13.960
+really simple model did really well uh
+
+00:47:11.960 --> 00:47:16.319
+at text classification and other simple
+
+00:47:13.960 --> 00:47:18.640
+tasks like that because it was able to
+
+00:47:16.319 --> 00:47:21.720
+you know share features of the words and
+
+00:47:18.640 --> 00:47:23.800
+then extract combinations to the
+
+00:47:21.720 --> 00:47:28.200
+features
+
+00:47:23.800 --> 00:47:29.760
+so um in order order to learn these we
+
+00:47:28.200 --> 00:47:30.920
+need to start turning to neural networks
+
+00:47:29.760 --> 00:47:34.400
+and the reason why we need to start
+
+00:47:30.920 --> 00:47:38.040
+turning to neural networks is
+
+00:47:34.400 --> 00:47:41.920
+because while I can calculate the loss
+
+00:47:38.040 --> 00:47:43.280
+function of the while I can calculate
+
+00:47:41.920 --> 00:47:44.839
+the loss function of the hinged loss for
+
+00:47:43.280 --> 00:47:47.720
+a bag of words model by hand I
+
+00:47:44.839 --> 00:47:49.359
+definitely don't I probably could but
+
+00:47:47.720 --> 00:47:51.240
+don't want to do it for a model that
+
+00:47:49.359 --> 00:47:53.200
+starts become as complicated as this
+
+00:47:51.240 --> 00:47:57.440
+with multiple Matrix multiplications
+
+00:47:53.200 --> 00:48:00.520
+Andes and stuff like that so the way we
+
+00:47:57.440 --> 00:48:05.000
+do this just a very brief uh coverage of
+
+00:48:00.520 --> 00:48:06.200
+this uh for because um I think probably
+
+00:48:05.000 --> 00:48:08.400
+a lot of people have dealt with neural
+
+00:48:06.200 --> 00:48:10.200
+networks before um the original
+
+00:48:08.400 --> 00:48:12.880
+motivation was that we had neurons in
+
+00:48:10.200 --> 00:48:16.160
+the brain uh where
+
+00:48:12.880 --> 00:48:18.839
+the each of the neuron synapses took in
+
+00:48:16.160 --> 00:48:21.480
+an electrical signal and once they got
+
+00:48:18.839 --> 00:48:24.079
+enough electrical signal they would fire
+
+00:48:21.480 --> 00:48:25.960
+um but now the current conception of
+
+00:48:24.079 --> 00:48:28.160
+neural networks or deep learning models
+
+00:48:25.960 --> 00:48:30.440
+is basically computation
+
+00:48:28.160 --> 00:48:32.400
+graphs and the way a computation graph
+
+00:48:30.440 --> 00:48:34.760
+Works um and I'm especially going to
+
+00:48:32.400 --> 00:48:36.240
+talk about the way it works in natural
+
+00:48:34.760 --> 00:48:38.119
+language processing which might be a
+
+00:48:36.240 --> 00:48:42.319
+contrast to the way it works in computer
+
+00:48:38.119 --> 00:48:43.960
+vision is um we have an expression uh
+
+00:48:42.319 --> 00:48:46.480
+that looks like this and maybe maybe
+
+00:48:43.960 --> 00:48:47.640
+it's the expression X corresponding to
+
+00:48:46.480 --> 00:48:51.880
+uh a
+
+00:48:47.640 --> 00:48:53.400
+scal um and each node corresponds to
+
+00:48:51.880 --> 00:48:55.599
+something like a tensor a matrix a
+
+00:48:53.400 --> 00:48:57.599
+vector a scalar so scaler is uh kind
+
+00:48:55.599 --> 00:49:00.480
+kind of Zero Dimensional it's a single
+
+00:48:57.599 --> 00:49:01.720
+value one dimensional two dimensional or
+
+00:49:00.480 --> 00:49:04.200
+arbitrary
+
+00:49:01.720 --> 00:49:06.040
+dimensional um and then we also have
+
+00:49:04.200 --> 00:49:08.000
+nodes that correspond to the result of
+
+00:49:06.040 --> 00:49:11.480
+function applications so if we have X be
+
+00:49:08.000 --> 00:49:14.079
+a vector uh we take the vector transpose
+
+00:49:11.480 --> 00:49:18.160
+and so each Edge represents a function
+
+00:49:14.079 --> 00:49:20.559
+argument and also a data
+
+00:49:18.160 --> 00:49:23.960
+dependency and a node with an incoming
+
+00:49:20.559 --> 00:49:27.000
+Edge is a function of that Edge's tail
+
+00:49:23.960 --> 00:49:29.040
+node and importantly each node knows how
+
+00:49:27.000 --> 00:49:30.640
+to compute its value and the value of
+
+00:49:29.040 --> 00:49:32.640
+its derivative with respect to each
+
+00:49:30.640 --> 00:49:34.440
+argument times the derivative of an
+
+00:49:32.640 --> 00:49:37.920
+arbitrary
+
+00:49:34.440 --> 00:49:41.000
+input and functions could be basically
+
+00:49:37.920 --> 00:49:45.400
+arbitrary functions it can be unary Nary
+
+00:49:41.000 --> 00:49:49.440
+unary binary Nary often unary or binary
+
+00:49:45.400 --> 00:49:52.400
+and computation graphs are directed in
+
+00:49:49.440 --> 00:49:57.040
+cyclic and um one important thing to
+
+00:49:52.400 --> 00:50:00.640
+note is that you can um have multiple
+
+00:49:57.040 --> 00:50:02.559
+ways of expressing the same function so
+
+00:50:00.640 --> 00:50:04.839
+this is actually really important as you
+
+00:50:02.559 --> 00:50:06.920
+start implementing things and the reason
+
+00:50:04.839 --> 00:50:09.359
+why is the left graph and the right
+
+00:50:06.920 --> 00:50:12.960
+graph both express the same thing the
+
+00:50:09.359 --> 00:50:18.640
+left graph expresses X
+
+00:50:12.960 --> 00:50:22.559
+transpose time A Time X where is whereas
+
+00:50:18.640 --> 00:50:27.160
+this one has x a and then it puts it
+
+00:50:22.559 --> 00:50:28.760
+into a node that is X transpose a x
+
+00:50:27.160 --> 00:50:30.319
+and so these Express exactly the same
+
+00:50:28.760 --> 00:50:32.319
+thing but the graph on the left is
+
+00:50:30.319 --> 00:50:33.760
+larger and the reason why this is
+
+00:50:32.319 --> 00:50:38.920
+important is for practical
+
+00:50:33.760 --> 00:50:40.359
+implementation of neural networks um you
+
+00:50:38.920 --> 00:50:43.200
+the larger graphs are going to take more
+
+00:50:40.359 --> 00:50:46.799
+memory and going to be slower usually
+
+00:50:43.200 --> 00:50:48.200
+and so often um in a neural network we
+
+00:50:46.799 --> 00:50:49.559
+look at like pipe part which we're going
+
+00:50:48.200 --> 00:50:52.160
+to look at in a
+
+00:50:49.559 --> 00:50:55.520
+second
+
+00:50:52.160 --> 00:50:57.920
+um you will have something you will be
+
+00:50:55.520 --> 00:50:57.920
+able to
+
+00:50:58.680 --> 00:51:01.680
+do
+
+00:51:03.079 --> 00:51:07.880
+this or you'll be able to do
+
+00:51:18.760 --> 00:51:22.880
+like
+
+00:51:20.359 --> 00:51:24.839
+this so these are two different options
+
+00:51:22.880 --> 00:51:26.920
+this one is using more operations and
+
+00:51:24.839 --> 00:51:29.559
+this one is using using less operations
+
+00:51:26.920 --> 00:51:31.000
+and this is going to be faster because
+
+00:51:29.559 --> 00:51:33.119
+basically the implementation within
+
+00:51:31.000 --> 00:51:34.799
+Pythor will have been optimized for you
+
+00:51:33.119 --> 00:51:36.799
+it will only require one graph node
+
+00:51:34.799 --> 00:51:37.880
+instead of multiple graph nodes and
+
+00:51:36.799 --> 00:51:39.799
+that's even more important when you
+
+00:51:37.880 --> 00:51:41.040
+start talking about like attention or
+
+00:51:39.799 --> 00:51:43.920
+something like that which we're going to
+
+00:51:41.040 --> 00:51:46.079
+be covering very soon um attention is a
+
+00:51:43.920 --> 00:51:47.359
+very multi-head attention or something
+
+00:51:46.079 --> 00:51:49.839
+like that is a very complicated
+
+00:51:47.359 --> 00:51:52.079
+operation so you want to make sure that
+
+00:51:49.839 --> 00:51:54.359
+you're using the operators that are
+
+00:51:52.079 --> 00:51:57.359
+available to you to make this more
+
+00:51:54.359 --> 00:51:57.359
+efficient
+
+00:51:57.440 --> 00:52:00.760
+um and then finally we could like add
+
+00:51:59.280 --> 00:52:01.920
+all of these together at the end we
+
+00:52:00.760 --> 00:52:04.000
+could add a
+
+00:52:01.920 --> 00:52:05.880
+constant um and then we get this
+
+00:52:04.000 --> 00:52:09.520
+expression here which gives us kind of a
+
+00:52:05.880 --> 00:52:09.520
+polinomial polom
+
+00:52:09.680 --> 00:52:15.760
+expression um also another thing to note
+
+00:52:13.480 --> 00:52:17.599
+is within a neural network computation
+
+00:52:15.760 --> 00:52:21.920
+graph variable names are just labelings
+
+00:52:17.599 --> 00:52:25.359
+of nodes and so if you're using a a
+
+00:52:21.920 --> 00:52:27.680
+computation graph like this you might
+
+00:52:25.359 --> 00:52:29.240
+only be declaring one variable here but
+
+00:52:27.680 --> 00:52:30.839
+actually there's a whole bunch of stuff
+
+00:52:29.240 --> 00:52:32.359
+going on behind the scenes and all of
+
+00:52:30.839 --> 00:52:34.240
+that will take memory and computation
+
+00:52:32.359 --> 00:52:35.440
+time and stuff like that so it's
+
+00:52:34.240 --> 00:52:37.119
+important to be aware of that if you
+
+00:52:35.440 --> 00:52:40.400
+want to make your implementations more
+
+00:52:37.119 --> 00:52:40.400
+efficient than other other
+
+00:52:41.119 --> 00:52:46.680
+things so we have several algorithms
+
+00:52:44.480 --> 00:52:49.079
+that go into implementing neural nuts um
+
+00:52:46.680 --> 00:52:50.760
+the first one is graph construction uh
+
+00:52:49.079 --> 00:52:53.480
+the second one is forward
+
+00:52:50.760 --> 00:52:54.839
+propagation uh and graph construction is
+
+00:52:53.480 --> 00:52:56.359
+basically constructing the graph
+
+00:52:54.839 --> 00:52:58.680
+declaring ing all the variables stuff
+
+00:52:56.359 --> 00:53:01.520
+like this the second one is forward
+
+00:52:58.680 --> 00:53:03.880
+propagation and um the way you do this
+
+00:53:01.520 --> 00:53:06.480
+is in topological order uh you compute
+
+00:53:03.880 --> 00:53:08.280
+the value of a node given its inputs and
+
+00:53:06.480 --> 00:53:11.000
+so basically you start out with all of
+
+00:53:08.280 --> 00:53:12.680
+the nodes that you give is input and
+
+00:53:11.000 --> 00:53:16.040
+then you find any node in the graph
+
+00:53:12.680 --> 00:53:17.799
+where all of its uh all of its tail
+
+00:53:16.040 --> 00:53:20.280
+nodes or all of its children have been
+
+00:53:17.799 --> 00:53:22.119
+calculated so in this case that would be
+
+00:53:20.280 --> 00:53:24.640
+these two nodes and then in arbitrary
+
+00:53:22.119 --> 00:53:27.000
+order or even in parallel you calculate
+
+00:53:24.640 --> 00:53:28.280
+the value of all of the satisfied nodes
+
+00:53:27.000 --> 00:53:31.799
+until you get to the
+
+00:53:28.280 --> 00:53:34.280
+end and then uh the remaining algorithms
+
+00:53:31.799 --> 00:53:36.200
+are back propagation and parameter
+
+00:53:34.280 --> 00:53:38.240
+update I already talked about parameter
+
+00:53:36.200 --> 00:53:40.799
+update uh using stochastic gradient
+
+00:53:38.240 --> 00:53:42.760
+descent but for back propagation we then
+
+00:53:40.799 --> 00:53:45.400
+process examples in Reverse topological
+
+00:53:42.760 --> 00:53:47.640
+order uh calculate derivatives of
+
+00:53:45.400 --> 00:53:50.400
+parameters with respect to final
+
+00:53:47.640 --> 00:53:52.319
+value and so we start out with the very
+
+00:53:50.400 --> 00:53:54.200
+final value usually this is your loss
+
+00:53:52.319 --> 00:53:56.200
+function and then you just step
+
+00:53:54.200 --> 00:54:00.440
+backwards in top ological order to
+
+00:53:56.200 --> 00:54:04.160
+calculate the derivatives of all these
+
+00:54:00.440 --> 00:54:05.920
+so um this is pretty simple I think a
+
+00:54:04.160 --> 00:54:08.040
+lot of people may have seen this already
+
+00:54:05.920 --> 00:54:09.920
+but keeping this in mind as you're
+
+00:54:08.040 --> 00:54:12.480
+implementing NLP models especially
+
+00:54:09.920 --> 00:54:14.240
+models that are really memory intensive
+
+00:54:12.480 --> 00:54:16.559
+or things like that is pretty important
+
+00:54:14.240 --> 00:54:19.040
+because if you accidentally like for
+
+00:54:16.559 --> 00:54:21.799
+example calculate the same thing twice
+
+00:54:19.040 --> 00:54:23.559
+or accidentally create a graph that is
+
+00:54:21.799 --> 00:54:25.720
+manipulating very large tensors and
+
+00:54:23.559 --> 00:54:27.319
+creating very large intermediate States
+
+00:54:25.720 --> 00:54:29.720
+that can kill your memory and and cause
+
+00:54:27.319 --> 00:54:31.839
+big problems so it's an important thing
+
+00:54:29.720 --> 00:54:31.839
+to
+
+00:54:34.359 --> 00:54:38.880
+be um cool any any questions about
+
+00:54:39.040 --> 00:54:44.440
+this okay if not I will go on to the
+
+00:54:41.680 --> 00:54:45.680
+next one so neural network Frameworks
+
+00:54:44.440 --> 00:54:48.920
+there's several neural network
+
+00:54:45.680 --> 00:54:52.880
+Frameworks but in NLP nowadays I really
+
+00:54:48.920 --> 00:54:55.079
+only see two and mostly only see one um
+
+00:54:52.880 --> 00:54:57.960
+so that one that almost everybody us
+
+00:54:55.079 --> 00:55:01.240
+uses is pie torch um and I would
+
+00:54:57.960 --> 00:55:04.559
+recommend using it unless you uh you
+
+00:55:01.240 --> 00:55:07.480
+know if you're a fan of like rust or you
+
+00:55:04.559 --> 00:55:09.200
+know esoteric uh not esoteric but like
+
+00:55:07.480 --> 00:55:11.960
+unusual programming languages and you
+
+00:55:09.200 --> 00:55:14.720
+like Beauty and things like this another
+
+00:55:11.960 --> 00:55:15.799
+option might be Jacks uh so I'll explain
+
+00:55:14.720 --> 00:55:18.440
+a little bit about the difference
+
+00:55:15.799 --> 00:55:19.960
+between them uh and you can pick
+
+00:55:18.440 --> 00:55:23.559
+accordingly
+
+00:55:19.960 --> 00:55:25.359
+um first uh both of these Frameworks uh
+
+00:55:23.559 --> 00:55:26.839
+are developed by big companies and they
+
+00:55:25.359 --> 00:55:28.520
+have a lot of engineering support behind
+
+00:55:26.839 --> 00:55:29.720
+them that's kind of an important thing
+
+00:55:28.520 --> 00:55:31.280
+to think about when you're deciding
+
+00:55:29.720 --> 00:55:32.599
+which framework to use because you know
+
+00:55:31.280 --> 00:55:36.000
+it'll be well
+
+00:55:32.599 --> 00:55:38.039
+supported um pytorch is definitely most
+
+00:55:36.000 --> 00:55:40.400
+widely used in NLP especially NLP
+
+00:55:38.039 --> 00:55:44.240
+research um and it's used in some NLP
+
+00:55:40.400 --> 00:55:47.359
+project J is used in some NLP
+
+00:55:44.240 --> 00:55:49.960
+projects um pytorch favors Dynamic
+
+00:55:47.359 --> 00:55:53.760
+execution so what dynamic execution
+
+00:55:49.960 --> 00:55:55.880
+means is um you basically create a
+
+00:55:53.760 --> 00:55:59.760
+computation graph and and then execute
+
+00:55:55.880 --> 00:56:02.760
+it uh every time you process an input uh
+
+00:55:59.760 --> 00:56:04.680
+in contrast there's also you define the
+
+00:56:02.760 --> 00:56:07.200
+computation graph first and then execute
+
+00:56:04.680 --> 00:56:09.280
+it over and over again so in other words
+
+00:56:07.200 --> 00:56:10.680
+the graph construction step only happens
+
+00:56:09.280 --> 00:56:13.119
+once kind of at the beginning of
+
+00:56:10.680 --> 00:56:16.799
+computation and then you compile it
+
+00:56:13.119 --> 00:56:20.039
+afterwards and it's actually pytorch
+
+00:56:16.799 --> 00:56:23.359
+supports kind of defining and compiling
+
+00:56:20.039 --> 00:56:27.480
+and Jax supports more Dynamic things but
+
+00:56:23.359 --> 00:56:30.160
+the way they were designed is uh is kind
+
+00:56:27.480 --> 00:56:32.960
+of favoring Dynamic execution or
+
+00:56:30.160 --> 00:56:37.079
+favoring definition in population
+
+00:56:32.960 --> 00:56:39.200
+and the difference between these two is
+
+00:56:37.079 --> 00:56:41.760
+this one gives you more flexibility this
+
+00:56:39.200 --> 00:56:45.440
+one gives you better optimization in wor
+
+00:56:41.760 --> 00:56:49.760
+speed if you want to if you want to do
+
+00:56:45.440 --> 00:56:52.400
+that um another thing about Jax is um
+
+00:56:49.760 --> 00:56:55.200
+it's kind of very close to numpy in a
+
+00:56:52.400 --> 00:56:57.440
+way like it uses a very num something
+
+00:56:55.200 --> 00:56:59.960
+that's kind of close to numpy it's very
+
+00:56:57.440 --> 00:57:02.359
+heavily based on tensors and so because
+
+00:56:59.960 --> 00:57:04.640
+of this you can kind of easily do some
+
+00:57:02.359 --> 00:57:06.640
+interesting things like okay I want to
+
+00:57:04.640 --> 00:57:11.319
+take this tensor and I want to split it
+
+00:57:06.640 --> 00:57:14.000
+over two gpus um and this is good if
+
+00:57:11.319 --> 00:57:17.119
+you're training like a very large model
+
+00:57:14.000 --> 00:57:20.920
+and you want to put kind
+
+00:57:17.119 --> 00:57:20.920
+of this part of the
+
+00:57:22.119 --> 00:57:26.520
+model uh you want to put this part of
+
+00:57:24.119 --> 00:57:30.079
+the model on GP 1 this on gpu2 this on
+
+00:57:26.520 --> 00:57:31.599
+GPU 3 this on GPU it's slightly simpler
+
+00:57:30.079 --> 00:57:34.400
+conceptually to do in Jacks but it's
+
+00:57:31.599 --> 00:57:37.160
+also possible to do in
+
+00:57:34.400 --> 00:57:39.119
+p and pytorch by far has the most
+
+00:57:37.160 --> 00:57:41.640
+vibrant ecosystem so like as I said
+
+00:57:39.119 --> 00:57:44.200
+pytorch is a good default choice but you
+
+00:57:41.640 --> 00:57:47.480
+can consider using Jack if you uh if you
+
+00:57:44.200 --> 00:57:47.480
+like new
+
+00:57:48.079 --> 00:57:55.480
+things cool um yeah actually I already
+
+00:57:51.599 --> 00:57:58.079
+talked about that so in the interest of
+
+00:57:55.480 --> 00:58:02.119
+time I may not go into these very deeply
+
+00:57:58.079 --> 00:58:05.799
+but it's important to note that we have
+
+00:58:02.119 --> 00:58:05.799
+examples of all of
+
+00:58:06.920 --> 00:58:12.520
+the models that I talked about in the
+
+00:58:09.359 --> 00:58:16.720
+class today these are created for
+
+00:58:12.520 --> 00:58:17.520
+Simplicity not for Speed or efficiency
+
+00:58:16.720 --> 00:58:20.480
+of
+
+00:58:17.520 --> 00:58:24.920
+implementation um so these are kind of
+
+00:58:20.480 --> 00:58:27.760
+torch P torch based uh examples uh where
+
+00:58:24.920 --> 00:58:31.599
+you can create the bag of words
+
+00:58:27.760 --> 00:58:36.440
+Model A continuous bag of words
+
+00:58:31.599 --> 00:58:39.640
+model um and
+
+00:58:36.440 --> 00:58:41.640
+a deep continuous bag of wordss
+
+00:58:39.640 --> 00:58:44.359
+model
+
+00:58:41.640 --> 00:58:46.039
+and all of these I believe are
+
+00:58:44.359 --> 00:58:48.760
+implemented in
+
+00:58:46.039 --> 00:58:51.960
+model.py and the most important thing is
+
+00:58:48.760 --> 00:58:54.960
+where you define the forward pass and
+
+00:58:51.960 --> 00:58:57.319
+maybe I can just give a a simple example
+
+00:58:54.960 --> 00:58:58.200
+this but here this is where you do the
+
+00:58:57.319 --> 00:59:01.839
+word
+
+00:58:58.200 --> 00:59:04.400
+embedding this is where you sum up all
+
+00:59:01.839 --> 00:59:08.119
+of the embeddings and add a
+
+00:59:04.400 --> 00:59:10.200
+bias um and then this is uh where you
+
+00:59:08.119 --> 00:59:13.960
+return the the
+
+00:59:10.200 --> 00:59:13.960
+score and then oh
+
+00:59:14.799 --> 00:59:19.119
+sorry the continuous bag of words model
+
+00:59:17.520 --> 00:59:22.160
+sums up some
+
+00:59:19.119 --> 00:59:23.640
+embeddings uh or gets the embeddings
+
+00:59:22.160 --> 00:59:25.799
+sums up some
+
+00:59:23.640 --> 00:59:28.079
+embeddings
+
+00:59:25.799 --> 00:59:30.599
+uh gets the score here and then runs it
+
+00:59:28.079 --> 00:59:33.200
+through a linear or changes the view
+
+00:59:30.599 --> 00:59:35.119
+runs it through a linear layer and then
+
+00:59:33.200 --> 00:59:38.319
+the Deep continuous bag of words model
+
+00:59:35.119 --> 00:59:41.160
+also adds a few layers of uh like linear
+
+00:59:38.319 --> 00:59:43.119
+transformations in Dage so you should be
+
+00:59:41.160 --> 00:59:44.640
+able to see that these correspond pretty
+
+00:59:43.119 --> 00:59:47.440
+closely to the things that I had on the
+
+00:59:44.640 --> 00:59:49.280
+slides so um hopefully that's a good
+
+00:59:47.440 --> 00:59:51.839
+start if you're not very familiar with
+
+00:59:49.280 --> 00:59:51.839
+implementing
+
+00:59:53.119 --> 00:59:58.440
+model oh and yes the recitation uh will
+
+00:59:56.599 --> 00:59:59.799
+be about playing around with sentence
+
+00:59:58.440 --> 01:00:01.200
+piece and playing around with these so
+
+00:59:59.799 --> 01:00:02.839
+if you have any look at them have any
+
+01:00:01.200 --> 01:00:05.000
+questions you're welcome to show up
+
+01:00:02.839 --> 01:00:09.880
+where I walk
+
+01:00:05.000 --> 01:00:09.880
+through cool um any any questions about
+
+01:00:12.839 --> 01:00:19.720
+these okay so a few more final important
+
+01:00:16.720 --> 01:00:21.720
+Concepts um another concept that you
+
+01:00:19.720 --> 01:00:25.440
+should definitely be aware of is the
+
+01:00:21.720 --> 01:00:27.280
+atom Optimizer uh so there's lots of uh
+
+01:00:25.440 --> 01:00:30.559
+optimizers that you could be using but
+
+01:00:27.280 --> 01:00:32.200
+almost all research in NLP uses some uh
+
+01:00:30.559 --> 01:00:38.440
+variety of the atom
+
+01:00:32.200 --> 01:00:40.839
+Optimizer and the U the way this works
+
+01:00:38.440 --> 01:00:42.559
+is it
+
+01:00:40.839 --> 01:00:45.640
+optimizes
+
+01:00:42.559 --> 01:00:48.480
+the um it optimizes model considering
+
+01:00:45.640 --> 01:00:49.359
+the rolling average of the gradient and
+
+01:00:48.480 --> 01:00:53.160
+uh
+
+01:00:49.359 --> 01:00:55.920
+momentum and the way it works is here we
+
+01:00:53.160 --> 01:00:58.839
+have a gradient here we have
+
+01:00:55.920 --> 01:01:04.000
+momentum and what you can see is
+
+01:00:58.839 --> 01:01:06.680
+happening here is we add a little bit of
+
+01:01:04.000 --> 01:01:09.200
+the gradient in uh how much you add in
+
+01:01:06.680 --> 01:01:12.720
+is with respect to the size of this beta
+
+01:01:09.200 --> 01:01:16.000
+1 parameter and you add it into uh the
+
+01:01:12.720 --> 01:01:18.640
+momentum term so this momentum term like
+
+01:01:16.000 --> 01:01:20.440
+gradually increases and decreases so in
+
+01:01:18.640 --> 01:01:23.440
+contrast to standard gradient percent
+
+01:01:20.440 --> 01:01:25.839
+which could be
+
+01:01:23.440 --> 01:01:28.440
+updating
+
+01:01:25.839 --> 01:01:31.440
+uh each parameter kind of like very
+
+01:01:28.440 --> 01:01:33.359
+differently on each time step this will
+
+01:01:31.440 --> 01:01:35.680
+make the momentum kind of transition
+
+01:01:33.359 --> 01:01:37.240
+more smoothly by taking the rolling
+
+01:01:35.680 --> 01:01:39.880
+average of the
+
+01:01:37.240 --> 01:01:43.400
+gradient and then the the second thing
+
+01:01:39.880 --> 01:01:47.640
+is um by taking the momentum this is the
+
+01:01:43.400 --> 01:01:51.000
+rolling average of the I guess gradient
+
+01:01:47.640 --> 01:01:54.440
+uh variance sorry I this should be
+
+01:01:51.000 --> 01:01:58.079
+variance and the reason why you need
+
+01:01:54.440 --> 01:02:01.319
+need to keep track of the variance is
+
+01:01:58.079 --> 01:02:03.319
+some uh some parameters will have very
+
+01:02:01.319 --> 01:02:06.559
+large variance in their gradients and
+
+01:02:03.319 --> 01:02:11.480
+might fluctuate very uh strongly and
+
+01:02:06.559 --> 01:02:13.039
+others might have a smaller uh chain
+
+01:02:11.480 --> 01:02:15.240
+variant in their gradients and not
+
+01:02:13.039 --> 01:02:18.240
+fluctuate very much but we want to make
+
+01:02:15.240 --> 01:02:20.200
+sure that we update the ones we still
+
+01:02:18.240 --> 01:02:22.240
+update the ones that have a very small
+
+01:02:20.200 --> 01:02:25.760
+uh change of their variance and the
+
+01:02:22.240 --> 01:02:27.440
+reason why is kind of let's say you have
+
+01:02:25.760 --> 01:02:30.440
+a
+
+01:02:27.440 --> 01:02:30.440
+multi-layer
+
+01:02:32.480 --> 01:02:38.720
+network
+
+01:02:34.480 --> 01:02:41.240
+um or actually sorry a better
+
+01:02:38.720 --> 01:02:44.319
+um a better example is like let's say we
+
+01:02:41.240 --> 01:02:47.559
+have a big word embedding Matrix and
+
+01:02:44.319 --> 01:02:53.359
+over here we have like really frequent
+
+01:02:47.559 --> 01:02:56.279
+words and then over here we have uh
+
+01:02:53.359 --> 01:02:59.319
+gradi
+
+01:02:56.279 --> 01:03:00.880
+no we have like less frequent words we
+
+01:02:59.319 --> 01:03:02.799
+want to make sure that all of these get
+
+01:03:00.880 --> 01:03:06.160
+updated appropriately all of these get
+
+01:03:02.799 --> 01:03:08.640
+like enough updates and so over here
+
+01:03:06.160 --> 01:03:10.760
+this one will have lots of updates and
+
+01:03:08.640 --> 01:03:13.680
+so uh kind of
+
+01:03:10.760 --> 01:03:16.599
+the amount that we
+
+01:03:13.680 --> 01:03:20.039
+update or the the amount that we update
+
+01:03:16.599 --> 01:03:21.799
+the uh this will be relatively large
+
+01:03:20.039 --> 01:03:23.119
+whereas over here this will not have
+
+01:03:21.799 --> 01:03:24.880
+very many updates we'll have lots of
+
+01:03:23.119 --> 01:03:26.480
+zero updates also
+
+01:03:24.880 --> 01:03:29.160
+and so the amount that we update this
+
+01:03:26.480 --> 01:03:32.520
+will be relatively small and so this
+
+01:03:29.160 --> 01:03:36.119
+kind of squared to gradient here will uh
+
+01:03:32.520 --> 01:03:38.400
+be smaller for the values over here and
+
+01:03:36.119 --> 01:03:41.359
+what that allows us to do is it allows
+
+01:03:38.400 --> 01:03:44.200
+us to maybe I can just go to the bottom
+
+01:03:41.359 --> 01:03:46.039
+we end up uh dividing by the square root
+
+01:03:44.200 --> 01:03:47.599
+of this and because we divide by the
+
+01:03:46.039 --> 01:03:51.000
+square root of this if this is really
+
+01:03:47.599 --> 01:03:55.680
+large like 50 and 70 and then this over
+
+01:03:51.000 --> 01:03:59.480
+here is like one 0.5
+
+01:03:55.680 --> 01:04:01.920
+uh or something we will be upgrading the
+
+01:03:59.480 --> 01:04:03.920
+ones that have like less Square
+
+01:04:01.920 --> 01:04:06.880
+gradients so it will it allows you to
+
+01:04:03.920 --> 01:04:08.760
+upweight the less common gradients more
+
+01:04:06.880 --> 01:04:10.440
+frequently and then there's also some
+
+01:04:08.760 --> 01:04:13.400
+terms for correcting bias early in
+
+01:04:10.440 --> 01:04:16.440
+training because these momentum in uh in
+
+01:04:13.400 --> 01:04:19.559
+variance or momentum in squared gradient
+
+01:04:16.440 --> 01:04:23.119
+terms are not going to be like well
+
+01:04:19.559 --> 01:04:24.839
+calibrated yet so it prevents them from
+
+01:04:23.119 --> 01:04:28.880
+going very three wire beginning of
+
+01:04:24.839 --> 01:04:30.839
+training so this is uh the details of
+
+01:04:28.880 --> 01:04:33.640
+this again are not like super super
+
+01:04:30.839 --> 01:04:37.359
+important um another thing that I didn't
+
+01:04:33.640 --> 01:04:40.200
+write on the slides is uh now in
+
+01:04:37.359 --> 01:04:43.920
+Transformers it's also super common to
+
+01:04:40.200 --> 01:04:47.400
+have an overall learning rate schle so
+
+01:04:43.920 --> 01:04:50.520
+even um Even Adam has this uh Ada
+
+01:04:47.400 --> 01:04:53.440
+learning rate parameter here and we what
+
+01:04:50.520 --> 01:04:55.240
+we often do is we adjust this so we
+
+01:04:53.440 --> 01:04:57.839
+start at low
+
+01:04:55.240 --> 01:04:59.640
+we raise it up and then we have a Decay
+
+01:04:57.839 --> 01:05:03.039
+uh at the end and exactly how much you
+
+01:04:59.640 --> 01:05:04.440
+do this kind of depends on um you know
+
+01:05:03.039 --> 01:05:06.160
+how big your model is how much data
+
+01:05:04.440 --> 01:05:09.160
+you're tring on eventually and the
+
+01:05:06.160 --> 01:05:12.440
+reason why we do this is transformers
+
+01:05:09.160 --> 01:05:13.839
+are unfortunately super sensitive to
+
+01:05:12.440 --> 01:05:15.359
+having a high learning rate right at the
+
+01:05:13.839 --> 01:05:16.559
+very beginning so if you update them
+
+01:05:15.359 --> 01:05:17.920
+with a high learning rate right at the
+
+01:05:16.559 --> 01:05:22.920
+very beginning they go haywire and you
+
+01:05:17.920 --> 01:05:24.400
+get a really weird model um and but you
+
+01:05:22.920 --> 01:05:26.760
+want to raise it eventually so your
+
+01:05:24.400 --> 01:05:28.920
+model is learning appropriately and then
+
+01:05:26.760 --> 01:05:30.400
+in all stochastic gradient descent no
+
+01:05:28.920 --> 01:05:31.680
+matter whether you're using atom or
+
+01:05:30.400 --> 01:05:33.400
+anything else it's a good idea to
+
+01:05:31.680 --> 01:05:36.200
+gradually decrease the learning rate at
+
+01:05:33.400 --> 01:05:38.119
+the end to prevent the model from
+
+01:05:36.200 --> 01:05:40.480
+continuing to fluctuate and getting it
+
+01:05:38.119 --> 01:05:42.760
+to a stable point that gives you good
+
+01:05:40.480 --> 01:05:45.559
+accuracy over a large part of data so
+
+01:05:42.760 --> 01:05:47.480
+this is often included like if you look
+
+01:05:45.559 --> 01:05:51.000
+at any standard Transformer training
+
+01:05:47.480 --> 01:05:53.079
+recipe it will have that this so that's
+
+01:05:51.000 --> 01:05:54.799
+kind of the the go-to
+
+01:05:53.079 --> 01:05:58.960
+optimizer
+
+01:05:54.799 --> 01:06:01.039
+um are there any questions or
+
+01:05:58.960 --> 01:06:02.599
+discussion there's also tricky things
+
+01:06:01.039 --> 01:06:04.000
+like cyclic learning rates where you
+
+01:06:02.599 --> 01:06:06.599
+decrease the learning rate increase it
+
+01:06:04.000 --> 01:06:08.559
+and stuff like that but I won't go into
+
+01:06:06.599 --> 01:06:11.000
+that and don't actually use it that
+
+01:06:08.559 --> 01:06:12.760
+much second thing is visualization of
+
+01:06:11.000 --> 01:06:15.400
+embeddings so normally when we have word
+
+01:06:12.760 --> 01:06:19.760
+embeddings usually they're kind of large
+
+01:06:15.400 --> 01:06:21.559
+um and they can be like 512 or 1024
+
+01:06:19.760 --> 01:06:25.079
+dimensions
+
+01:06:21.559 --> 01:06:28.720
+and so one thing that we can do is we
+
+01:06:25.079 --> 01:06:31.079
+can down weight them or sorry down uh
+
+01:06:28.720 --> 01:06:34.400
+like reduce the dimensions or perform
+
+01:06:31.079 --> 01:06:35.880
+dimensionality reduction and put them in
+
+01:06:34.400 --> 01:06:37.680
+like two or three dimensions which are
+
+01:06:35.880 --> 01:06:40.200
+easy for humans to
+
+01:06:37.680 --> 01:06:42.000
+visualize this is an example using
+
+01:06:40.200 --> 01:06:44.839
+principal component analysis which is a
+
+01:06:42.000 --> 01:06:48.279
+linear Dimension reduction technique and
+
+01:06:44.839 --> 01:06:50.680
+this is uh an example from 10 years ago
+
+01:06:48.279 --> 01:06:52.359
+now uh one of the first major word
+
+01:06:50.680 --> 01:06:55.240
+embedding papers where they demonstrated
+
+01:06:52.359 --> 01:06:57.720
+that if you do this sort of linear
+
+01:06:55.240 --> 01:06:59.440
+Dimension reduction uh you get actually
+
+01:06:57.720 --> 01:07:01.279
+some interesting things where you can
+
+01:06:59.440 --> 01:07:03.240
+draw a vector that's almost the same
+
+01:07:01.279 --> 01:07:06.400
+direction between like countries and
+
+01:07:03.240 --> 01:07:09.319
+their uh countries and their capitals
+
+01:07:06.400 --> 01:07:13.720
+for example so this is a good thing to
+
+01:07:09.319 --> 01:07:16.559
+do but actually PCA uh doesn't give
+
+01:07:13.720 --> 01:07:20.760
+you in some cases PCA doesn't give you
+
+01:07:16.559 --> 01:07:22.920
+super great uh visualizations sorry yeah
+
+01:07:20.760 --> 01:07:25.920
+well for like if it's
+
+01:07:22.920 --> 01:07:25.920
+like
+
+01:07:29.880 --> 01:07:35.039
+um for things like this I think you
+
+01:07:33.119 --> 01:07:37.359
+probably would still see vectors in the
+
+01:07:35.039 --> 01:07:38.760
+same direction but I don't think it like
+
+01:07:37.359 --> 01:07:40.920
+there's a reason why I'm introducing
+
+01:07:38.760 --> 01:07:44.279
+nonlinear projections next because the
+
+01:07:40.920 --> 01:07:46.799
+more standard way to do this is uh
+
+01:07:44.279 --> 01:07:50.640
+nonlinear projections in in particular a
+
+01:07:46.799 --> 01:07:54.880
+method called tisne and the way um they
+
+01:07:50.640 --> 01:07:56.880
+do this is they try to group
+
+01:07:54.880 --> 01:07:59.000
+things that are close together in high
+
+01:07:56.880 --> 01:08:01.240
+dimensional space so that they're also
+
+01:07:59.000 --> 01:08:04.440
+close together in low dimensional space
+
+01:08:01.240 --> 01:08:08.520
+but they remove the Restriction that
+
+01:08:04.440 --> 01:08:10.799
+this is uh that this is linear so this
+
+01:08:08.520 --> 01:08:15.480
+is an example of just grouping together
+
+01:08:10.799 --> 01:08:18.040
+some digits uh from the memus data
+
+01:08:15.480 --> 01:08:20.279
+set or sorry reducing the dimension of
+
+01:08:18.040 --> 01:08:23.640
+digits from the mest data
+
+01:08:20.279 --> 01:08:25.640
+set according to PCA and you can see it
+
+01:08:23.640 --> 01:08:28.000
+gives these kind of blobs that overlap
+
+01:08:25.640 --> 01:08:29.799
+with each other and stuff like this but
+
+01:08:28.000 --> 01:08:31.679
+if you do it with tney this is
+
+01:08:29.799 --> 01:08:34.799
+completely unsupervised actually it's
+
+01:08:31.679 --> 01:08:37.080
+not training any model for labeling the
+
+01:08:34.799 --> 01:08:39.239
+labels are just used to draw the colors
+
+01:08:37.080 --> 01:08:42.520
+and you can see that it gets pretty
+
+01:08:39.239 --> 01:08:44.520
+coherent um clusters that correspond to
+
+01:08:42.520 --> 01:08:48.120
+like what the actual digits
+
+01:08:44.520 --> 01:08:50.120
+are um however uh one problem with
+
+01:08:48.120 --> 01:08:53.159
+titney I I still think it's better than
+
+01:08:50.120 --> 01:08:55.000
+PCA for a large number of uh
+
+01:08:53.159 --> 01:08:59.199
+applications
+
+01:08:55.000 --> 01:09:01.040
+but settings of tisy matter and tisy has
+
+01:08:59.199 --> 01:09:02.920
+a few settings kind of the most
+
+01:09:01.040 --> 01:09:04.120
+important ones are the overall
+
+01:09:02.920 --> 01:09:06.560
+perplexity
+
+01:09:04.120 --> 01:09:09.040
+hyperparameter and uh the number of
+
+01:09:06.560 --> 01:09:12.319
+steps that you perform and there's a
+
+01:09:09.040 --> 01:09:14.920
+nice example uh of a paper or kind of
+
+01:09:12.319 --> 01:09:16.359
+like online post uh that demonstrates
+
+01:09:14.920 --> 01:09:18.560
+how if you change these parameters you
+
+01:09:16.359 --> 01:09:22.279
+can get very different things so if this
+
+01:09:18.560 --> 01:09:24.080
+is the original data you run tisy and it
+
+01:09:22.279 --> 01:09:26.640
+gives you very different things based on
+
+01:09:24.080 --> 01:09:29.279
+the hyper parameters that you change um
+
+01:09:26.640 --> 01:09:32.880
+and here's another example uh you have
+
+01:09:29.279 --> 01:09:36.960
+two linear uh things like this and so
+
+01:09:32.880 --> 01:09:40.839
+PCA no matter how you ran PCA you would
+
+01:09:36.960 --> 01:09:44.080
+still get a linear output from this so
+
+01:09:40.839 --> 01:09:45.960
+normally uh you know it might change the
+
+01:09:44.080 --> 01:09:49.239
+order it might squash it a little bit or
+
+01:09:45.960 --> 01:09:51.239
+something like this but um if you run
+
+01:09:49.239 --> 01:09:53.400
+tisy it gives you crazy things it even
+
+01:09:51.239 --> 01:09:56.040
+gives you like DNA and other stuff like
+
+01:09:53.400 --> 01:09:58.040
+that so so um you do need to be a little
+
+01:09:56.040 --> 01:10:00.600
+bit careful that uh this is not
+
+01:09:58.040 --> 01:10:02.320
+necessarily going to tell you nice
+
+01:10:00.600 --> 01:10:04.400
+linear correlations like this so like
+
+01:10:02.320 --> 01:10:06.159
+let's say this correlation existed if
+
+01:10:04.400 --> 01:10:09.199
+you use tisy it might not necessarily
+
+01:10:06.159 --> 01:10:09.199
+come out to
+
+01:10:09.320 --> 01:10:14.880
+TIY
+
+01:10:11.800 --> 01:10:16.920
+cool yep uh that that's my final thing
+
+01:10:14.880 --> 01:10:18.520
+actually I talked said sequence models
+
+01:10:16.920 --> 01:10:19.679
+in the next class but it's in the class
+
+01:10:18.520 --> 01:10:21.440
+after this I'm going to be talking about
+
+01:10:19.679 --> 01:10:24.199
+language
+
+01:10:21.440 --> 01:10:27.159
+modeling uh cool any any questions
+
+01:10:24.199 --> 01:10:27.159
+or