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+1
+00:00:03,879 --> 00:00:07,480
+cool um so this time I'm going to talk
+
+2
+00:00:05,480 --> 00:00:08,880
+about word representation and text
+
+3
+00:00:07,480 --> 00:00:11,480
+classifiers these are kind of the
+
+4
+00:00:08,880 --> 00:00:14,080
+foundations that you need to know uh in
+
+5
+00:00:11,480 --> 00:00:15,640
+order to move on to the more complex
+
+6
+00:00:14,080 --> 00:00:17,920
+things that we'll be talking in future
+
+7
+00:00:15,640 --> 00:00:19,640
+classes uh but actually the in
+
+8
+00:00:17,920 --> 00:00:22,760
+particular the word representation part
+
+9
+00:00:19,640 --> 00:00:25,439
+is pretty important it's a major uh
+
+10
+00:00:22,760 --> 00:00:31,800
+thing that we need to do for all NLP
+
+11
+00:00:25,439 --> 00:00:34,239
+models so uh let's go into it
+
+12
+00:00:31,800 --> 00:00:38,200
+so last class I talked about the bag of
+
+13
+00:00:34,239 --> 00:00:40,239
+words model um and just to review this
+
+14
+00:00:38,200 --> 00:00:43,920
+was a model where basically we take each
+
+15
+00:00:40,239 --> 00:00:45,520
+word we represent it as a one hot Vector
+
+16
+00:00:43,920 --> 00:00:48,760
+uh like
+
+17
+00:00:45,520 --> 00:00:51,120
+this and we add all of these vectors
+
+18
+00:00:48,760 --> 00:00:53,160
+together we multiply the resulting
+
+19
+00:00:51,120 --> 00:00:55,160
+frequency vector by some weights and we
+
+20
+00:00:53,160 --> 00:00:57,239
+get a score out of this and we can use
+
+21
+00:00:55,160 --> 00:00:58,559
+this score for binary classification or
+
+22
+00:00:57,239 --> 00:01:00,239
+if we want to do multiclass
+
+23
+00:00:58,559 --> 00:01:02,519
+classification we get you know multiple
+
+24
+00:01:00,239 --> 00:01:05,720
+scores for each
+
+25
+00:01:02,519 --> 00:01:08,040
+class and the features F were just based
+
+26
+00:01:05,720 --> 00:01:08,920
+on our word identities and the weights
+
+27
+00:01:08,040 --> 00:01:12,159
+were
+
+28
+00:01:08,920 --> 00:01:14,680
+learned and um if we look at what's
+
+29
+00:01:12,159 --> 00:01:17,520
+missing in bag of words
+
+30
+00:01:14,680 --> 00:01:19,600
+models um we talked about handling of
+
+31
+00:01:17,520 --> 00:01:23,280
+conjugated or compound
+
+32
+00:01:19,600 --> 00:01:25,439
+words we talked about handling of word
+
+33
+00:01:23,280 --> 00:01:27,880
+similarity and we talked about handling
+
+34
+00:01:25,439 --> 00:01:30,240
+of combination features and handling of
+
+35
+00:01:27,880 --> 00:01:33,280
+sentence structure and so all of these
+
+36
+00:01:30,240 --> 00:01:35,000
+are are tricky problems uh we saw that
+
+37
+00:01:33,280 --> 00:01:37,000
+you know creating a rule-based system to
+
+38
+00:01:35,000 --> 00:01:39,000
+solve these problems is non-trivial and
+
+39
+00:01:37,000 --> 00:01:41,399
+at the very least would take a lot of
+
+40
+00:01:39,000 --> 00:01:44,079
+time and so now I want to talk about
+
+41
+00:01:41,399 --> 00:01:47,119
+some solutions to the problems in this
+
+42
+00:01:44,079 --> 00:01:49,280
+class so the first the solution to the
+
+43
+00:01:47,119 --> 00:01:52,240
+first problem or a solution to the first
+
+44
+00:01:49,280 --> 00:01:54,880
+problem is uh subword or character based
+
+45
+00:01:52,240 --> 00:01:57,520
+models and that's what I'll talk about
+
+46
+00:01:54,880 --> 00:02:00,719
+first handling of word similarity this
+
+47
+00:01:57,520 --> 00:02:02,960
+can be handled uh using Word edings
+
+48
+00:02:00,719 --> 00:02:05,079
+and the word embeddings uh will be
+
+49
+00:02:02,960 --> 00:02:07,159
+another thing we'll talk about this time
+
+50
+00:02:05,079 --> 00:02:08,879
+handling of combination features uh we
+
+51
+00:02:07,159 --> 00:02:11,039
+can handle through neural networks which
+
+52
+00:02:08,879 --> 00:02:14,040
+we'll also talk about this time and then
+
+53
+00:02:11,039 --> 00:02:15,560
+handling of sentence structure uh the
+
+54
+00:02:14,040 --> 00:02:17,720
+kind of standard way of handling this
+
+55
+00:02:15,560 --> 00:02:20,120
+now is through sequence-based models and
+
+56
+00:02:17,720 --> 00:02:24,879
+that will be uh starting in a few
+
+57
+00:02:20,120 --> 00:02:28,080
+classes so uh let's jump into
+
+58
+00:02:24,879 --> 00:02:30,000
+it so subword models uh as I mentioned
+
+59
+00:02:28,080 --> 00:02:31,840
+this is a really really important part
+
+60
+00:02:30,000 --> 00:02:33,360
+all of the models that we're building
+
+61
+00:02:31,840 --> 00:02:35,480
+nowadays including you know
+
+62
+00:02:33,360 --> 00:02:38,239
+state-of-the-art language models and and
+
+63
+00:02:35,480 --> 00:02:42,200
+things like this and the basic idea
+
+64
+00:02:38,239 --> 00:02:44,720
+behind this is that we want to split uh
+
+65
+00:02:42,200 --> 00:02:48,040
+in particular split less common words up
+
+66
+00:02:44,720 --> 00:02:50,200
+into multiple subboard tokens so to give
+
+67
+00:02:48,040 --> 00:02:52,200
+an example of this uh if we have
+
+68
+00:02:50,200 --> 00:02:55,040
+something like the companies are
+
+69
+00:02:52,200 --> 00:02:57,000
+expanding uh it might split companies
+
+70
+00:02:55,040 --> 00:03:02,120
+into compan
+
+71
+00:02:57,000 --> 00:03:05,000
+e and expand in like this and there are
+
+72
+00:03:02,120 --> 00:03:08,480
+a few benefits of this uh the first
+
+73
+00:03:05,000 --> 00:03:10,760
+benefit is that this allows you to
+
+74
+00:03:08,480 --> 00:03:13,360
+parameters between word varieties or
+
+75
+00:03:10,760 --> 00:03:15,200
+compound words and the other one is to
+
+76
+00:03:13,360 --> 00:03:17,400
+reduce parameter size and save compute
+
+77
+00:03:15,200 --> 00:03:19,720
+and meming and both of these are kind of
+
+78
+00:03:17,400 --> 00:03:23,239
+like equally important things that we
+
+79
+00:03:19,720 --> 00:03:25,519
+need to be uh we need to be considering
+
+80
+00:03:23,239 --> 00:03:26,440
+so does anyone know how many words there
+
+81
+00:03:25,519 --> 00:03:28,680
+are in
+
+82
+00:03:26,440 --> 00:03:31,680
+English any
+
+83
+00:03:28,680 --> 00:03:31,680
+ideas
+
+84
+00:03:36,799 --> 00:03:43,400
+yeah two
+
+85
+00:03:38,599 --> 00:03:45,560
+million pretty good um any other
+
+86
+00:03:43,400 --> 00:03:47,159
+ideas
+
+87
+00:03:45,560 --> 00:03:50,360
+yeah
+
+88
+00:03:47,159 --> 00:03:53,599
+60,000 some models use 60,000 I I think
+
+89
+00:03:50,360 --> 00:03:56,200
+60,000 is probably these subword models
+
+90
+00:03:53,599 --> 00:03:58,079
+uh when you're talking about this so
+
+91
+00:03:56,200 --> 00:03:59,319
+they can use sub models to take the 2
+
+92
+00:03:58,079 --> 00:04:03,480
+million which I think is a reasonable
+
+93
+00:03:59,319 --> 00:04:07,400
+guess to 6 60,000 any other
+
+94
+00:04:03,480 --> 00:04:08,840
+ideas 700,000 okay pretty good um so
+
+95
+00:04:07,400 --> 00:04:11,799
+this was a per question it doesn't
+
+96
+00:04:08,840 --> 00:04:14,760
+really have a good answer um but two 200
+
+97
+00:04:11,799 --> 00:04:17,479
+million's probably pretty good six uh
+
+98
+00:04:14,760 --> 00:04:19,160
+700,000 is pretty good the reason why
+
+99
+00:04:17,479 --> 00:04:21,360
+this is a trick question is because are
+
+100
+00:04:19,160 --> 00:04:24,440
+company and companies different
+
+101
+00:04:21,360 --> 00:04:26,840
+words uh maybe maybe not right because
+
+102
+00:04:24,440 --> 00:04:30,120
+if we know the word company we can you
+
+103
+00:04:26,840 --> 00:04:32,520
+know guess what the word companies means
+
+104
+00:04:30,120 --> 00:04:35,720
+um what about automobile is that a
+
+105
+00:04:32,520 --> 00:04:37,400
+different word well maybe if we know
+
+106
+00:04:35,720 --> 00:04:39,400
+Auto and mobile we can kind of guess
+
+107
+00:04:37,400 --> 00:04:41,160
+what automobile means but not really so
+
+108
+00:04:39,400 --> 00:04:43,479
+maybe that's a different word there's
+
+109
+00:04:41,160 --> 00:04:45,960
+all kinds of Shades of Gray there and
+
+110
+00:04:43,479 --> 00:04:48,120
+also we have really frequent words that
+
+111
+00:04:45,960 --> 00:04:50,360
+everybody can probably acknowledge our
+
+112
+00:04:48,120 --> 00:04:52,320
+words like
+
+113
+00:04:50,360 --> 00:04:55,639
+the and
+
+114
+00:04:52,320 --> 00:04:58,520
+a and um maybe
+
+115
+00:04:55,639 --> 00:05:00,680
+car and then we have words down here
+
+116
+00:04:58,520 --> 00:05:02,320
+which are like Miss spellings or
+
+117
+00:05:00,680 --> 00:05:04,160
+something like that misspellings of
+
+118
+00:05:02,320 --> 00:05:06,520
+actual correct words or
+
+119
+00:05:04,160 --> 00:05:09,199
+slay uh or other things like that and
+
+120
+00:05:06,520 --> 00:05:12,520
+then it's questionable whether those are
+
+121
+00:05:09,199 --> 00:05:17,199
+actual words or not so um there's a
+
+122
+00:05:12,520 --> 00:05:19,520
+famous uh law called Zip's
+
+123
+00:05:17,199 --> 00:05:21,280
+law um which probably a lot of people
+
+124
+00:05:19,520 --> 00:05:23,360
+have heard of it's also the source of
+
+125
+00:05:21,280 --> 00:05:26,919
+your zip
+
+126
+00:05:23,360 --> 00:05:30,160
+file um which is using Zip's law to
+
+127
+00:05:26,919 --> 00:05:32,400
+compress uh compress output by making
+
+128
+00:05:30,160 --> 00:05:34,880
+the uh more frequent words have shorter
+
+129
+00:05:32,400 --> 00:05:37,520
+bite strings and less frequent words
+
+130
+00:05:34,880 --> 00:05:38,800
+have uh you know less frequent bite
+
+131
+00:05:37,520 --> 00:05:43,120
+strings but basically like we're going
+
+132
+00:05:38,800 --> 00:05:45,120
+to have an infinite number of words or
+
+133
+00:05:43,120 --> 00:05:46,360
+at least strings that are separated by
+
+134
+00:05:45,120 --> 00:05:49,280
+white space so we need to handle this
+
+135
+00:05:46,360 --> 00:05:53,199
+somehow and that's what subword units
+
+136
+00:05:49,280 --> 00:05:54,560
+do so um 60,000 was a good guess for the
+
+137
+00:05:53,199 --> 00:05:57,160
+number of subword units you might use in
+
+138
+00:05:54,560 --> 00:06:00,759
+a model and so uh by using subw units we
+
+139
+00:05:57,160 --> 00:06:04,840
+can limit to about that much
+
+140
+00:06:00,759 --> 00:06:08,160
+so there's a couple of common uh ways to
+
+141
+00:06:04,840 --> 00:06:10,440
+create these subword units and basically
+
+142
+00:06:08,160 --> 00:06:14,560
+all of them rely on the fact that you
+
+143
+00:06:10,440 --> 00:06:16,039
+want more common strings to become
+
+144
+00:06:14,560 --> 00:06:19,599
+subword
+
+145
+00:06:16,039 --> 00:06:22,199
+units um or actually sorry I realize
+
+146
+00:06:19,599 --> 00:06:24,280
+maybe before doing that I could explain
+
+147
+00:06:22,199 --> 00:06:26,360
+an alternative to creating subword units
+
+148
+00:06:24,280 --> 00:06:29,639
+so the alternative to creating subword
+
+149
+00:06:26,360 --> 00:06:33,560
+units is to treat every character or
+
+150
+00:06:29,639 --> 00:06:36,919
+maybe every bite in a string as a single
+
+151
+00:06:33,560 --> 00:06:38,560
+thing that you encode in forent so in
+
+152
+00:06:36,919 --> 00:06:42,520
+other words instead of trying to model
+
+153
+00:06:38,560 --> 00:06:47,919
+the companies are expanding we Model T h
+
+154
+00:06:42,520 --> 00:06:50,199
+e space c o m uh etc etc can anyone
+
+155
+00:06:47,919 --> 00:06:53,199
+think of any downsides of
+
+156
+00:06:50,199 --> 00:06:53,199
+this
+
+157
+00:06:57,039 --> 00:07:01,879
+yeah yeah the set of these will be very
+
+158
+00:07:00,080 --> 00:07:05,000
+will be very small but that's not
+
+159
+00:07:01,879 --> 00:07:05,000
+necessarily a problem
+
+160
+00:07:08,560 --> 00:07:15,599
+right yeah um and any other
+
+161
+00:07:12,599 --> 00:07:15,599
+ideas
+
+162
+00:07:19,520 --> 00:07:24,360
+yeah yeah the resulting sequences will
+
+163
+00:07:22,080 --> 00:07:25,520
+be very long um and when you say
+
+164
+00:07:24,360 --> 00:07:27,160
+difficult to use it could be difficult
+
+165
+00:07:25,520 --> 00:07:29,560
+to use for a couple of reasons there's
+
+166
+00:07:27,160 --> 00:07:31,840
+mainly two reasons actually any any IDE
+
+167
+00:07:29,560 --> 00:07:31,840
+about
+
+168
+00:07:33,479 --> 00:07:37,800
+this any
+
+169
+00:07:46,280 --> 00:07:50,599
+yeah yeah that's a little bit of a
+
+170
+00:07:49,000 --> 00:07:52,319
+separate problem than the character
+
+171
+00:07:50,599 --> 00:07:53,919
+based model so let me get back to that
+
+172
+00:07:52,319 --> 00:07:56,400
+but uh let let's finish the discussion
+
+173
+00:07:53,919 --> 00:07:58,360
+of the character based models so if it's
+
+174
+00:07:56,400 --> 00:08:00,120
+really if it's really long maybe a
+
+175
+00:07:58,360 --> 00:08:01,879
+simple thing like uh let's say you have
+
+176
+00:08:00,120 --> 00:08:06,560
+a big neural network and it's processing
+
+177
+00:08:01,879 --> 00:08:06,560
+a really long sequence any ideas what
+
+178
+00:08:06,919 --> 00:08:10,879
+happens basically you run out of memory
+
+179
+00:08:09,280 --> 00:08:13,440
+or it takes a really long time right so
+
+180
+00:08:10,879 --> 00:08:16,840
+you have computational problems another
+
+181
+00:08:13,440 --> 00:08:18,479
+reason why is um think of what a bag of
+
+182
+00:08:16,840 --> 00:08:21,400
+words model would look like if it was a
+
+183
+00:08:18,479 --> 00:08:21,400
+bag of characters
+
+184
+00:08:21,800 --> 00:08:25,919
+model it wouldn't be very informative
+
+185
+00:08:24,199 --> 00:08:27,599
+about whether like a sentence is
+
+186
+00:08:25,919 --> 00:08:30,919
+positive sentiment or negative sentiment
+
+187
+00:08:27,599 --> 00:08:32,959
+right because instead of having uh go o
+
+188
+00:08:30,919 --> 00:08:35,039
+you would have uh instead of having good
+
+189
+00:08:32,959 --> 00:08:36,360
+you would have go o and that doesn't
+
+190
+00:08:35,039 --> 00:08:38,560
+really directly tell you whether it's
+
+191
+00:08:36,360 --> 00:08:41,719
+positive sentiment or not so those are
+
+192
+00:08:38,560 --> 00:08:43,680
+basically the two problems um compute
+
+193
+00:08:41,719 --> 00:08:45,320
+and lack of expressiveness in the
+
+194
+00:08:43,680 --> 00:08:50,720
+underlying representations so you need
+
+195
+00:08:45,320 --> 00:08:52,080
+to handle both of those yes so if we uh
+
+196
+00:08:50,720 --> 00:08:54,480
+move from
+
+197
+00:08:52,080 --> 00:08:56,440
+character better expressiveness and we
+
+198
+00:08:54,480 --> 00:08:58,920
+assume that if we just get the bigger
+
+199
+00:08:56,440 --> 00:09:00,120
+and bigger paragraphs we'll get even
+
+200
+00:08:58,920 --> 00:09:02,760
+better
+
+201
+00:09:00,120 --> 00:09:05,120
+yeah so a very good question I'll repeat
+
+202
+00:09:02,760 --> 00:09:06,560
+it um and actually this also goes back
+
+203
+00:09:05,120 --> 00:09:08,040
+to the other question you asked about
+
+204
+00:09:06,560 --> 00:09:09,519
+words that look the same but are
+
+205
+00:09:08,040 --> 00:09:12,160
+pronounced differently or have different
+
+206
+00:09:09,519 --> 00:09:14,360
+meanings and so like let's say we just
+
+207
+00:09:12,160 --> 00:09:15,920
+remembered this whole sentence right the
+
+208
+00:09:14,360 --> 00:09:18,279
+companies are
+
+209
+00:09:15,920 --> 00:09:21,600
+expanding um and that was like a single
+
+210
+00:09:18,279 --> 00:09:22,680
+embedding and we somehow embedded it the
+
+211
+00:09:21,600 --> 00:09:25,720
+problem would be we're never going to
+
+212
+00:09:22,680 --> 00:09:27,120
+see that sentence again um or if we go
+
+213
+00:09:25,720 --> 00:09:29,480
+to longer sentences we're never going to
+
+214
+00:09:27,120 --> 00:09:31,839
+see the longer sentences again so it
+
+215
+00:09:29,480 --> 00:09:34,320
+becomes too sparse so there's kind of a
+
+216
+00:09:31,839 --> 00:09:37,240
+sweet spot between
+
+217
+00:09:34,320 --> 00:09:40,279
+like long enough to be expressive and
+
+218
+00:09:37,240 --> 00:09:42,480
+short enough to occur many times so that
+
+219
+00:09:40,279 --> 00:09:43,959
+you can learn appropriately and that's
+
+220
+00:09:42,480 --> 00:09:47,120
+kind of what subword models are aiming
+
+221
+00:09:43,959 --> 00:09:48,360
+for and if you get longer subwords then
+
+222
+00:09:47,120 --> 00:09:50,200
+you'll get things that are more
+
+223
+00:09:48,360 --> 00:09:52,959
+expressive but more sparse in shorter
+
+224
+00:09:50,200 --> 00:09:55,440
+subwords you'll get things that are like
+
+225
+00:09:52,959 --> 00:09:57,279
+uh less expressive but less spice so you
+
+226
+00:09:55,440 --> 00:09:59,120
+need to balance between them and then
+
+227
+00:09:57,279 --> 00:10:00,600
+once we get into sequence modeling they
+
+228
+00:09:59,120 --> 00:10:02,600
+start being able to model like which
+
+229
+00:10:00,600 --> 00:10:04,120
+words are next to each other uh which
+
+230
+00:10:02,600 --> 00:10:06,040
+tokens are next to each other and stuff
+
+231
+00:10:04,120 --> 00:10:07,800
+like that so even if they are less
+
+232
+00:10:06,040 --> 00:10:11,279
+expressive the combination between them
+
+233
+00:10:07,800 --> 00:10:12,600
+can be expressive so um yeah that's kind
+
+234
+00:10:11,279 --> 00:10:13,440
+of a preview of what we're going to be
+
+235
+00:10:12,600 --> 00:10:17,320
+doing
+
+236
+00:10:13,440 --> 00:10:19,279
+next okay so um let's assume that we
+
+237
+00:10:17,320 --> 00:10:21,320
+want to have some subwords that are
+
+238
+00:10:19,279 --> 00:10:23,000
+longer than characters but shorter than
+
+239
+00:10:21,320 --> 00:10:26,240
+tokens how do we make these in a
+
+240
+00:10:23,000 --> 00:10:28,680
+consistent way there's two major ways of
+
+241
+00:10:26,240 --> 00:10:31,480
+doing this uh the first one is bite pair
+
+242
+00:10:28,680 --> 00:10:32,839
+encoding and this is uh very very simple
+
+243
+00:10:31,480 --> 00:10:35,839
+in fact it's so
+
+244
+00:10:32,839 --> 00:10:35,839
+simple
+
+245
+00:10:36,600 --> 00:10:40,839
+that we can implement
+
+246
+00:10:41,839 --> 00:10:47,240
+it in this notebook here which you can
+
+247
+00:10:44,600 --> 00:10:51,720
+click through to on the
+
+248
+00:10:47,240 --> 00:10:55,440
+slides and it's uh
+
+249
+00:10:51,720 --> 00:10:58,040
+about 10 lines of code um and so
+
+250
+00:10:55,440 --> 00:11:01,040
+basically what B pair encoding
+
+251
+00:10:58,040 --> 00:11:01,040
+does
+
+252
+00:11:04,600 --> 00:11:09,560
+is that you start out with um all of the
+
+253
+00:11:07,000 --> 00:11:14,360
+vocabulary that you want to process
+
+254
+00:11:09,560 --> 00:11:17,560
+where each vocabulary item is split into
+
+255
+00:11:14,360 --> 00:11:21,240
+uh the characters and an end of word
+
+256
+00:11:17,560 --> 00:11:23,360
+symbol and you have a corresponding
+
+257
+00:11:21,240 --> 00:11:27,519
+frequency of
+
+258
+00:11:23,360 --> 00:11:31,120
+this you then uh get statistics about
+
+259
+00:11:27,519 --> 00:11:33,279
+the most common pairs of tokens that
+
+260
+00:11:31,120 --> 00:11:34,880
+occur next to each other and so here the
+
+261
+00:11:33,279 --> 00:11:38,240
+most common pairs of tokens that occur
+
+262
+00:11:34,880 --> 00:11:41,920
+next to each other are e s because it
+
+263
+00:11:38,240 --> 00:11:46,560
+occurs nine times because it occurs in
+
+264
+00:11:41,920 --> 00:11:48,279
+newest and wildest also s and t w
+
+265
+00:11:46,560 --> 00:11:51,440
+because those occur there too and then
+
+266
+00:11:48,279 --> 00:11:53,519
+you have we and other things like that
+
+267
+00:11:51,440 --> 00:11:56,000
+so out of all the most frequent ones you
+
+268
+00:11:53,519 --> 00:11:59,920
+just merge them together and that gives
+
+269
+00:11:56,000 --> 00:12:02,720
+you uh new s new
+
+270
+00:11:59,920 --> 00:12:05,200
+EST and wide
+
+271
+00:12:02,720 --> 00:12:09,360
+EST and then you do the same thing this
+
+272
+00:12:05,200 --> 00:12:12,519
+time now you get EST so now you get this
+
+273
+00:12:09,360 --> 00:12:14,279
+uh suffix EST and that looks pretty
+
+274
+00:12:12,519 --> 00:12:16,399
+reasonable for English right you know
+
+275
+00:12:14,279 --> 00:12:19,040
+EST is a common suffix that we use it
+
+276
+00:12:16,399 --> 00:12:22,399
+seems like it should be a single token
+
+277
+00:12:19,040 --> 00:12:25,880
+and um so you just do this over and over
+
+278
+00:12:22,399 --> 00:12:29,279
+again if you want a vocabulary of 60,000
+
+279
+00:12:25,880 --> 00:12:31,120
+for example you would do um 60,000 minus
+
+280
+00:12:29,279 --> 00:12:33,079
+number of characters merge operations
+
+281
+00:12:31,120 --> 00:12:37,160
+and eventually you would get a B of
+
+282
+00:12:33,079 --> 00:12:41,920
+60,000 um and yeah very very simple
+
+283
+00:12:37,160 --> 00:12:41,920
+method to do this um any questions about
+
+284
+00:12:43,160 --> 00:12:46,160
+that
+
+285
+00:12:57,839 --> 00:13:00,839
+yeah
+
+286
+00:13:15,600 --> 00:13:20,959
+yeah so uh just to repeat the the
+
+287
+00:13:18,040 --> 00:13:23,560
+comment uh this seems like a greedy
+
+288
+00:13:20,959 --> 00:13:25,320
+version of Huffman encoding which is a
+
+289
+00:13:23,560 --> 00:13:28,839
+you know similar to what you're using in
+
+290
+00:13:25,320 --> 00:13:32,000
+your zip file a way to shorten things by
+
+291
+00:13:28,839 --> 00:13:36,560
+getting longer uh more frequent things
+
+292
+00:13:32,000 --> 00:13:39,120
+being inced as a single token um I think
+
+293
+00:13:36,560 --> 00:13:40,760
+B pair encoding did originally start
+
+294
+00:13:39,120 --> 00:13:43,720
+like that that's part of the reason why
+
+295
+00:13:40,760 --> 00:13:45,760
+the encoding uh thing is here I think it
+
+296
+00:13:43,720 --> 00:13:47,360
+originally started there I haven't read
+
+297
+00:13:45,760 --> 00:13:49,360
+really deeply into this but I can talk
+
+298
+00:13:47,360 --> 00:13:53,240
+more about how the next one corresponds
+
+299
+00:13:49,360 --> 00:13:54,440
+to information Theory and Tuesday I'm
+
+300
+00:13:53,240 --> 00:13:55,720
+going to talk even more about how
+
+301
+00:13:54,440 --> 00:13:57,720
+language models correspond to
+
+302
+00:13:55,720 --> 00:14:00,040
+information theories so we can uh we can
+
+303
+00:13:57,720 --> 00:14:04,519
+discuss maybe in more detail
+
+304
+00:14:00,040 --> 00:14:07,639
+to um so the the alternative option is
+
+305
+00:14:04,519 --> 00:14:10,000
+to use unigram models and unigram models
+
+306
+00:14:07,639 --> 00:14:12,240
+are the simplest type of language model
+
+307
+00:14:10,000 --> 00:14:15,079
+I'm going to talk more in detail about
+
+308
+00:14:12,240 --> 00:14:18,279
+them next time but basically uh the way
+
+309
+00:14:15,079 --> 00:14:20,759
+it works is you create a model that
+
+310
+00:14:18,279 --> 00:14:23,600
+generates all word uh words in the
+
+311
+00:14:20,759 --> 00:14:26,199
+sequence independently sorry I thought I
+
+312
+00:14:23,600 --> 00:14:26,199
+had a
+
+313
+00:14:26,320 --> 00:14:31,800
+um I thought I had an equation but
+
+314
+00:14:28,800 --> 00:14:31,800
+basically the
+
+315
+00:14:32,240 --> 00:14:35,759
+equation looks
+
+316
+00:14:38,079 --> 00:14:41,079
+like
+
+317
+00:14:47,720 --> 00:14:52,120
+this so you say the probability of the
+
+318
+00:14:50,360 --> 00:14:53,440
+sequence is the product of the
+
+319
+00:14:52,120 --> 00:14:54,279
+probabilities of each of the words in
+
+320
+00:14:53,440 --> 00:14:55,959
+the
+
+321
+00:14:54,279 --> 00:15:00,079
+sequence
+
+322
+00:14:55,959 --> 00:15:04,079
+and uh then you try to pick a vocabulary
+
+323
+00:15:00,079 --> 00:15:06,839
+that maximizes the probability of the
+
+324
+00:15:04,079 --> 00:15:09,320
+Corpus given a fixed vocabulary size so
+
+325
+00:15:06,839 --> 00:15:10,320
+you try to say okay you get a vocabulary
+
+326
+00:15:09,320 --> 00:15:14,440
+size of
+
+327
+00:15:10,320 --> 00:15:16,920
+60,000 how do you um how do you pick the
+
+328
+00:15:14,440 --> 00:15:19,680
+best 60,000 vocabulary to maximize the
+
+329
+00:15:16,920 --> 00:15:22,440
+probability of the the Corpus and that
+
+330
+00:15:19,680 --> 00:15:25,959
+will result in something very similar uh
+
+331
+00:15:22,440 --> 00:15:27,920
+it will also try to give longer uh
+
+332
+00:15:25,959 --> 00:15:29,880
+vocabulary uh sorry more common
+
+333
+00:15:27,920 --> 00:15:32,240
+vocabulary long sequences because that
+
+334
+00:15:29,880 --> 00:15:35,560
+allows you to to maximize this
+
+335
+00:15:32,240 --> 00:15:36,959
+objective um the optimization for this
+
+336
+00:15:35,560 --> 00:15:40,040
+is performed using something called the
+
+337
+00:15:36,959 --> 00:15:44,440
+EM algorithm where basically you uh
+
+338
+00:15:40,040 --> 00:15:48,560
+predict the uh the probability of each
+
+339
+00:15:44,440 --> 00:15:51,600
+token showing up and uh then select the
+
+340
+00:15:48,560 --> 00:15:53,279
+most common tokens and then trim off the
+
+341
+00:15:51,600 --> 00:15:54,759
+ones that are less common and then just
+
+342
+00:15:53,279 --> 00:15:58,120
+do this over and over again until you
+
+343
+00:15:54,759 --> 00:15:59,839
+drop down to the 60,000 token lat so the
+
+344
+00:15:58,120 --> 00:16:02,040
+details for this are not important for
+
+345
+00:15:59,839 --> 00:16:04,160
+most people in this class uh because
+
+346
+00:16:02,040 --> 00:16:07,480
+you're going to just be using a toolkit
+
+347
+00:16:04,160 --> 00:16:08,880
+that implements this for you um but if
+
+348
+00:16:07,480 --> 00:16:10,759
+you're interested in this I'm happy to
+
+349
+00:16:08,880 --> 00:16:14,199
+talk to you about it
+
+350
+00:16:10,759 --> 00:16:14,199
+yeah is there
+
+351
+00:16:14,680 --> 00:16:18,959
+problem Oh in unigram models there's a
+
+352
+00:16:17,199 --> 00:16:20,959
+huge problem with assuming Independence
+
+353
+00:16:18,959 --> 00:16:22,720
+in language models because then you
+
+354
+00:16:20,959 --> 00:16:25,120
+could rearrange the order of words in
+
+355
+00:16:22,720 --> 00:16:26,600
+sentences um that that's something we're
+
+356
+00:16:25,120 --> 00:16:27,519
+going to talk about in language model
+
+357
+00:16:26,600 --> 00:16:30,560
+next
+
+358
+00:16:27,519 --> 00:16:32,839
+time but the the good thing about this
+
+359
+00:16:30,560 --> 00:16:34,519
+is the EM algorithm requires dynamic
+
+360
+00:16:32,839 --> 00:16:36,079
+programming in this case and you can't
+
+361
+00:16:34,519 --> 00:16:37,800
+easily do dynamic programming if you
+
+362
+00:16:36,079 --> 00:16:40,160
+don't make that
+
+363
+00:16:37,800 --> 00:16:41,880
+assumptions um and then finally after
+
+364
+00:16:40,160 --> 00:16:43,560
+you've picked your vocabulary and you've
+
+365
+00:16:41,880 --> 00:16:45,720
+assigned a probability to each word in
+
+366
+00:16:43,560 --> 00:16:47,800
+the vocabulary you then find a
+
+367
+00:16:45,720 --> 00:16:49,639
+segmentation of the input that maximizes
+
+368
+00:16:47,800 --> 00:16:52,600
+the unigram
+
+369
+00:16:49,639 --> 00:16:54,880
+probabilities um so this is basically
+
+370
+00:16:52,600 --> 00:16:56,519
+the idea of what's going on here um I'm
+
+371
+00:16:54,880 --> 00:16:58,120
+not going to go into a lot of detail
+
+372
+00:16:56,519 --> 00:17:00,560
+about this because most people are just
+
+373
+00:16:58,120 --> 00:17:02,279
+going to be users of this algorithm so
+
+374
+00:17:00,560 --> 00:17:06,240
+it's not super super
+
+375
+00:17:02,279 --> 00:17:09,400
+important um the one important thing
+
+376
+00:17:06,240 --> 00:17:11,240
+about this is that there's a library
+
+377
+00:17:09,400 --> 00:17:15,520
+called sentence piece that's used very
+
+378
+00:17:11,240 --> 00:17:19,199
+widely in order to build these um in
+
+379
+00:17:15,520 --> 00:17:22,000
+order to build these subword units and
+
+380
+00:17:19,199 --> 00:17:23,720
+uh basically what you do is you run the
+
+381
+00:17:22,000 --> 00:17:27,600
+sentence piece
+
+382
+00:17:23,720 --> 00:17:30,200
+train uh model or sorry uh program and
+
+383
+00:17:27,600 --> 00:17:32,640
+that gives you uh you select your vocab
+
+384
+00:17:30,200 --> 00:17:34,240
+size uh this also this character
+
+385
+00:17:32,640 --> 00:17:36,120
+coverage is basically how well do you
+
+386
+00:17:34,240 --> 00:17:39,760
+need to cover all of the characters in
+
+387
+00:17:36,120 --> 00:17:41,840
+your vocabulary or in your input text um
+
+388
+00:17:39,760 --> 00:17:45,240
+what model type do you use and then you
+
+389
+00:17:41,840 --> 00:17:48,640
+run this uh sentence piece en code file
+
+390
+00:17:45,240 --> 00:17:51,039
+uh to uh encode the output and split the
+
+391
+00:17:48,640 --> 00:17:54,799
+output and there's also python bindings
+
+392
+00:17:51,039 --> 00:17:56,240
+available for this and by the one thing
+
+393
+00:17:54,799 --> 00:17:57,919
+that you should know is by default it
+
+394
+00:17:56,240 --> 00:18:00,600
+uses the unigram model but it also
+
+395
+00:17:57,919 --> 00:18:01,960
+supports EP in my experience it doesn't
+
+396
+00:18:00,600 --> 00:18:05,159
+make a huge difference about which one
+
+397
+00:18:01,960 --> 00:18:07,640
+you use the bigger thing is how um how
+
+398
+00:18:05,159 --> 00:18:10,159
+big is your vocabulary size and if your
+
+399
+00:18:07,640 --> 00:18:11,880
+vocabulary size is smaller then things
+
+400
+00:18:10,159 --> 00:18:13,760
+will be more efficient but less
+
+401
+00:18:11,880 --> 00:18:17,480
+expressive if your vocabulary size is
+
+402
+00:18:13,760 --> 00:18:21,280
+bigger things will be um will
+
+403
+00:18:17,480 --> 00:18:23,240
+be more expressive but less efficient
+
+404
+00:18:21,280 --> 00:18:25,360
+and A good rule of thumb is like
+
+405
+00:18:23,240 --> 00:18:26,960
+something like 60,000 to 80,000 is
+
+406
+00:18:25,360 --> 00:18:29,120
+pretty reasonable if you're only doing
+
+407
+00:18:26,960 --> 00:18:31,320
+English if you're spreading out to
+
+408
+00:18:29,120 --> 00:18:32,600
+things that do other languages um which
+
+409
+00:18:31,320 --> 00:18:35,960
+I'll talk about in a second then you
+
+410
+00:18:32,600 --> 00:18:38,720
+need a much bigger B regular
+
+411
+00:18:35,960 --> 00:18:40,559
+say so there's two considerations here
+
+412
+00:18:38,720 --> 00:18:42,440
+two important considerations when using
+
+413
+00:18:40,559 --> 00:18:46,320
+these models uh the first is
+
+414
+00:18:42,440 --> 00:18:48,760
+multilinguality as I said so when you're
+
+415
+00:18:46,320 --> 00:18:50,760
+using um subword
+
+416
+00:18:48,760 --> 00:18:54,710
+models they're hard to use
+
+417
+00:18:50,760 --> 00:18:55,840
+multilingually because as I said before
+
+418
+00:18:54,710 --> 00:18:59,799
+[Music]
+
+419
+00:18:55,840 --> 00:19:03,799
+they give longer strings to more
+
+420
+00:18:59,799 --> 00:19:06,520
+frequent strings basically so then
+
+421
+00:19:03,799 --> 00:19:09,559
+imagine what happens if 50% of your
+
+422
+00:19:06,520 --> 00:19:11,919
+Corpus is English another 30% of your
+
+423
+00:19:09,559 --> 00:19:15,400
+Corpus is
+
+424
+00:19:11,919 --> 00:19:17,200
+other languages written in Latin script
+
+425
+00:19:15,400 --> 00:19:21,720
+10% is
+
+426
+00:19:17,200 --> 00:19:25,480
+Chinese uh 5% is cerlic script languages
+
+427
+00:19:21,720 --> 00:19:27,240
+four 4% is 3% is Japanese and then you
+
+428
+00:19:25,480 --> 00:19:31,080
+have like
+
+429
+00:19:27,240 --> 00:19:33,320
+0.01% written in like burmes or
+
+430
+00:19:31,080 --> 00:19:35,520
+something like that suddenly burmes just
+
+431
+00:19:33,320 --> 00:19:37,400
+gets chunked up really really tiny
+
+432
+00:19:35,520 --> 00:19:38,360
+really long sequences and it doesn't
+
+433
+00:19:37,400 --> 00:19:45,559
+work as
+
+434
+00:19:38,360 --> 00:19:45,559
+well um so one way that people fix this
+
+435
+00:19:45,919 --> 00:19:50,520
+um and actually there's a really nice uh
+
+436
+00:19:48,760 --> 00:19:52,600
+blog post about this called exploring
+
+437
+00:19:50,520 --> 00:19:53,760
+B's vocabulary which I referenced here
+
+438
+00:19:52,600 --> 00:19:58,039
+if you're interested in learning more
+
+439
+00:19:53,760 --> 00:20:02,960
+about that um but one way that people
+
+440
+00:19:58,039 --> 00:20:05,240
+were around this is if your
+
+441
+00:20:02,960 --> 00:20:07,960
+actual uh data
+
+442
+00:20:05,240 --> 00:20:11,559
+distribution looks like this like
+
+443
+00:20:07,960 --> 00:20:11,559
+English uh
+
+444
+00:20:17,039 --> 00:20:23,159
+Ty we actually sorry I took out the
+
+445
+00:20:19,280 --> 00:20:23,159
+Indian languages in my example
+
+446
+00:20:24,960 --> 00:20:30,159
+apologies
+
+447
+00:20:27,159 --> 00:20:30,159
+so
+
+448
+00:20:30,400 --> 00:20:35,919
+um what you do is you essentially create
+
+449
+00:20:33,640 --> 00:20:40,000
+a different distribution that like
+
+450
+00:20:35,919 --> 00:20:43,559
+downweights English a little bit and up
+
+451
+00:20:40,000 --> 00:20:47,000
+weights up weights all of the other
+
+452
+00:20:43,559 --> 00:20:49,480
+languages um so that you get more of
+
+453
+00:20:47,000 --> 00:20:53,159
+other languages when creating so this is
+
+454
+00:20:49,480 --> 00:20:53,159
+a common work around that you can do for
+
+455
+00:20:54,200 --> 00:20:59,960
+this um the
+
+456
+00:20:56,799 --> 00:21:03,000
+second problem with these is
+
+457
+00:20:59,960 --> 00:21:08,000
+arbitrariness so as you saw in my
+
+458
+00:21:03,000 --> 00:21:11,240
+example with bpe e s s and t and of
+
+459
+00:21:08,000 --> 00:21:13,520
+board symbol all have the same probabil
+
+460
+00:21:11,240 --> 00:21:16,960
+or have the same frequency right so if
+
+461
+00:21:13,520 --> 00:21:21,520
+we get to that point do we segment es or
+
+462
+00:21:16,960 --> 00:21:25,039
+do we seg uh EST or do we segment e
+
+463
+00:21:21,520 --> 00:21:26,559
+s and so this is also a problem and it
+
+464
+00:21:25,039 --> 00:21:29,000
+actually can affect your results
+
+465
+00:21:26,559 --> 00:21:30,480
+especially if you like don't have a
+
+466
+00:21:29,000 --> 00:21:31,760
+really strong vocabulary for the
+
+467
+00:21:30,480 --> 00:21:33,279
+language you're working in or you're
+
+468
+00:21:31,760 --> 00:21:37,200
+working in a new
+
+469
+00:21:33,279 --> 00:21:40,159
+domain and so there's a few workarounds
+
+470
+00:21:37,200 --> 00:21:41,520
+for this uh one workaround for this is
+
+471
+00:21:40,159 --> 00:21:44,000
+uh called subword
+
+472
+00:21:41,520 --> 00:21:46,279
+regularization and the way it works is
+
+473
+00:21:44,000 --> 00:21:49,400
+instead
+
+474
+00:21:46,279 --> 00:21:51,640
+of just having a single segmentation and
+
+475
+00:21:49,400 --> 00:21:54,679
+getting the kind of
+
+476
+00:21:51,640 --> 00:21:56,200
+maximally probable segmentation or the
+
+477
+00:21:54,679 --> 00:21:58,480
+one the greedy one that you get out of
+
+478
+00:21:56,200 --> 00:22:01,360
+BP instead you sample different
+
+479
+00:21:58,480 --> 00:22:03,000
+segmentations in training time and use
+
+480
+00:22:01,360 --> 00:22:05,720
+the different segmentations and that
+
+481
+00:22:03,000 --> 00:22:09,200
+makes your model more robust to this
+
+482
+00:22:05,720 --> 00:22:10,840
+kind of variation and that's also
+
+483
+00:22:09,200 --> 00:22:15,679
+actually the reason why sentence piece
+
+484
+00:22:10,840 --> 00:22:17,919
+was released was through this um subword
+
+485
+00:22:15,679 --> 00:22:19,559
+regularization paper so that's also
+
+486
+00:22:17,919 --> 00:22:22,720
+implemented in sentence piece if that's
+
+487
+00:22:19,559 --> 00:22:22,720
+something you're interested in
+
+488
+00:22:24,919 --> 00:22:32,520
+trying cool um are there any questions
+
+489
+00:22:28,480 --> 00:22:32,520
+or discussions about this
+
+490
+00:22:53,279 --> 00:22:56,279
+yeah
+
+491
+00:22:56,960 --> 00:22:59,960
+already
+
+492
+00:23:06,799 --> 00:23:11,080
+yeah so this is a good question um just
+
+493
+00:23:08,960 --> 00:23:12,760
+to repeat the question it was like let's
+
+494
+00:23:11,080 --> 00:23:16,080
+say we have a big
+
+495
+00:23:12,760 --> 00:23:19,640
+multilingual um subword
+
+496
+00:23:16,080 --> 00:23:23,440
+model and we want to add a new language
+
+497
+00:23:19,640 --> 00:23:26,240
+in some way uh how can we reuse the
+
+498
+00:23:23,440 --> 00:23:28,880
+existing model but add a new
+
+499
+00:23:26,240 --> 00:23:31,080
+language it's a good question if you're
+
+500
+00:23:28,880 --> 00:23:33,679
+only using it for subord
+
+501
+00:23:31,080 --> 00:23:36,320
+segmentation um one one nice thing about
+
+502
+00:23:33,679 --> 00:23:36,320
+the unigram
+
+503
+00:23:36,400 --> 00:23:41,799
+model here is this is kind of a
+
+504
+00:23:38,880 --> 00:23:43,679
+probabilistic model so it's very easy to
+
+505
+00:23:41,799 --> 00:23:46,360
+do the kind of standard things that we
+
+506
+00:23:43,679 --> 00:23:48,240
+do with probabilistic models which is
+
+507
+00:23:46,360 --> 00:23:50,559
+like let's say we had an
+
+508
+00:23:48,240 --> 00:23:53,919
+old uh an
+
+509
+00:23:50,559 --> 00:23:56,880
+old vocabulary for
+
+510
+00:23:53,919 --> 00:23:59,880
+this um we could just
+
+511
+00:23:56,880 --> 00:23:59,880
+interpolate
+
+512
+00:24:07,159 --> 00:24:12,320
+um we could interpolate like this and
+
+513
+00:24:09,559 --> 00:24:13,840
+just you know uh combine the
+
+514
+00:24:12,320 --> 00:24:17,080
+probabilities of the two and then use
+
+515
+00:24:13,840 --> 00:24:19,520
+that combine probability in order to
+
+516
+00:24:17,080 --> 00:24:21,320
+segment the new language um things like
+
+517
+00:24:19,520 --> 00:24:24,159
+this have been uh done before but I
+
+518
+00:24:21,320 --> 00:24:26,159
+don't remember the exact preferences uh
+
+519
+00:24:24,159 --> 00:24:30,440
+for them but that that's what I would do
+
+520
+00:24:26,159 --> 00:24:31,960
+here another interesting thing is um
+
+521
+00:24:30,440 --> 00:24:35,399
+this might be getting a little ahead of
+
+522
+00:24:31,960 --> 00:24:35,399
+myself but there's
+
+523
+00:24:48,559 --> 00:24:58,279
+a there's a paper that talks about um
+
+524
+00:24:55,360 --> 00:25:00,159
+how you can take things that or trained
+
+525
+00:24:58,279 --> 00:25:03,360
+with another
+
+526
+00:25:00,159 --> 00:25:05,480
+vocabulary and basically the idea is um
+
+527
+00:25:03,360 --> 00:25:09,320
+you pre-train on whatever languages you
+
+528
+00:25:05,480 --> 00:25:10,679
+have and then uh you learn embeddings in
+
+529
+00:25:09,320 --> 00:25:11,880
+the new language you freeze the body of
+
+530
+00:25:10,679 --> 00:25:14,360
+the model and learn embeddings in the
+
+531
+00:25:11,880 --> 00:25:15,880
+new language so that's another uh method
+
+532
+00:25:14,360 --> 00:25:19,080
+that's used it's called on the cross
+
+533
+00:25:15,880 --> 00:25:19,080
+lingual printability
+
+534
+00:25:21,840 --> 00:25:26,159
+representations and I'll probably talk
+
+535
+00:25:23,840 --> 00:25:28,480
+about that in the last class of this uh
+
+536
+00:25:26,159 --> 00:25:30,720
+thing so you can remember that
+
+537
+00:25:28,480 --> 00:25:33,720
+then cool any other
+
+538
+00:25:30,720 --> 00:25:33,720
+questions
+
+539
+00:25:38,480 --> 00:25:42,640
+yeah is bag of words a first step to
+
+540
+00:25:41,039 --> 00:25:46,640
+process your data if you want to do
+
+541
+00:25:42,640 --> 00:25:49,919
+Generation Um do you mean like
+
+542
+00:25:46,640 --> 00:25:52,440
+uh a word based model or a subword based
+
+543
+00:25:49,919 --> 00:25:52,440
+model
+
+544
+00:25:56,679 --> 00:26:00,480
+or like is
+
+545
+00:26:02,360 --> 00:26:08,000
+this so the subword segmentation is the
+
+546
+00:26:05,919 --> 00:26:10,640
+first step of creating just about any
+
+547
+00:26:08,000 --> 00:26:13,080
+model nowadays like every model every
+
+548
+00:26:10,640 --> 00:26:16,600
+model uses this and they usually use
+
+549
+00:26:13,080 --> 00:26:21,520
+this either to segment characters or
+
+550
+00:26:16,600 --> 00:26:23,559
+byes um characters are like Unicode code
+
+551
+00:26:21,520 --> 00:26:25,799
+points so they actually correspond to an
+
+552
+00:26:23,559 --> 00:26:28,279
+actual visual character and then bites
+
+553
+00:26:25,799 --> 00:26:31,120
+are many unicode characters are like
+
+554
+00:26:28,279 --> 00:26:35,000
+three by like a Chinese character is
+
+555
+00:26:31,120 --> 00:26:37,159
+three byes if I remember correctly so um
+
+556
+00:26:35,000 --> 00:26:38,640
+the bbased segmentation is nice because
+
+557
+00:26:37,159 --> 00:26:41,240
+you don't even need to worry about unic
+
+558
+00:26:38,640 --> 00:26:43,880
+code you can just do the like you can
+
+559
+00:26:41,240 --> 00:26:45,640
+just segment the pile like literally as
+
+560
+00:26:43,880 --> 00:26:49,440
+is and so a lot of people do it that way
+
+561
+00:26:45,640 --> 00:26:53,279
+too uh llama as far as I know is
+
+562
+00:26:49,440 --> 00:26:55,720
+bites I believe GPT is also bites um but
+
+563
+00:26:53,279 --> 00:26:58,799
+pre previous to like three or four years
+
+564
+00:26:55,720 --> 00:27:02,799
+ago people used SCS I
+
+565
+00:26:58,799 --> 00:27:05,000
+cool um okay so this is really really
+
+566
+00:27:02,799 --> 00:27:05,919
+important it's not like super complex
+
+567
+00:27:05,000 --> 00:27:09,760
+and
+
+568
+00:27:05,919 --> 00:27:13,039
+practically uh you will just maybe maybe
+
+569
+00:27:09,760 --> 00:27:15,840
+train or maybe just use a tokenizer um
+
+570
+00:27:13,039 --> 00:27:18,559
+but uh that that's an important thing to
+
+571
+00:27:15,840 --> 00:27:20,760
+me cool uh next I'd like to move on to
+
+572
+00:27:18,559 --> 00:27:24,399
+continuous word eddings
+
+573
+00:27:20,760 --> 00:27:26,720
+so the basic idea is that previously we
+
+574
+00:27:24,399 --> 00:27:28,240
+represented words with a sparse Vector
+
+575
+00:27:26,720 --> 00:27:30,120
+uh with a single one
+
+576
+00:27:28,240 --> 00:27:31,960
+also known as one poot Vector so it
+
+577
+00:27:30,120 --> 00:27:35,720
+looked a little bit like
+
+578
+00:27:31,960 --> 00:27:37,640
+this and instead what continuous word
+
+579
+00:27:35,720 --> 00:27:39,640
+embeddings do is they look up a dense
+
+580
+00:27:37,640 --> 00:27:42,320
+vector and so you get a dense
+
+581
+00:27:39,640 --> 00:27:45,760
+representation where the entire Vector
+
+582
+00:27:42,320 --> 00:27:45,760
+has continuous values in
+
+583
+00:27:46,000 --> 00:27:51,919
+it and I talked about a bag of words
+
+584
+00:27:49,200 --> 00:27:54,320
+model but we could also create a
+
+585
+00:27:51,919 --> 00:27:58,360
+continuous bag of words model and the
+
+586
+00:27:54,320 --> 00:28:01,159
+way this works is you look up the
+
+587
+00:27:58,360 --> 00:28:03,720
+values of each Vector the embeddings of
+
+588
+00:28:01,159 --> 00:28:06,320
+each Vector this gives you an embedding
+
+589
+00:28:03,720 --> 00:28:08,440
+Vector for the entire sequence and then
+
+590
+00:28:06,320 --> 00:28:15,120
+you multiply this by a weight
+
+591
+00:28:08,440 --> 00:28:17,559
+Matrix uh where the so this is column so
+
+592
+00:28:15,120 --> 00:28:19,960
+the rows of the weight Matrix uh
+
+593
+00:28:17,559 --> 00:28:22,919
+correspond to to the size of this
+
+594
+00:28:19,960 --> 00:28:24,760
+continuous embedding and The Columns of
+
+595
+00:28:22,919 --> 00:28:28,320
+the weight Matrix would correspond to
+
+596
+00:28:24,760 --> 00:28:30,919
+the uh overall um
+
+597
+00:28:28,320 --> 00:28:32,559
+to the overall uh number of labels that
+
+598
+00:28:30,919 --> 00:28:36,919
+you would have here and then that would
+
+599
+00:28:32,559 --> 00:28:40,120
+give you sces and so this uh basically
+
+600
+00:28:36,919 --> 00:28:41,679
+what this is saying is each Vector now
+
+601
+00:28:40,120 --> 00:28:43,440
+instead of having a single thing that
+
+602
+00:28:41,679 --> 00:28:46,799
+represents which vocabulary item you're
+
+603
+00:28:43,440 --> 00:28:48,679
+looking at uh you would kind of hope
+
+604
+00:28:46,799 --> 00:28:52,120
+that you would get vectors where words
+
+605
+00:28:48,679 --> 00:28:54,919
+that are similar uh by some mention of
+
+606
+00:28:52,120 --> 00:28:57,760
+by some concept of similar like syntatic
+
+607
+00:28:54,919 --> 00:28:59,679
+uh syntax semantics whether they're in
+
+608
+00:28:57,760 --> 00:29:03,120
+the same language or not are close in
+
+609
+00:28:59,679 --> 00:29:06,679
+the vector space and each Vector element
+
+610
+00:29:03,120 --> 00:29:09,399
+is a feature uh so for example each
+
+611
+00:29:06,679 --> 00:29:11,519
+Vector element corresponds to is this an
+
+612
+00:29:09,399 --> 00:29:14,960
+animate object or is this a positive
+
+613
+00:29:11,519 --> 00:29:17,399
+word or other Vector other things like
+
+614
+00:29:14,960 --> 00:29:19,399
+that so just to give an example here
+
+615
+00:29:17,399 --> 00:29:21,760
+this is totally made up I just made it
+
+616
+00:29:19,399 --> 00:29:24,360
+in keynote so it's not natural Vector
+
+617
+00:29:21,760 --> 00:29:26,279
+space but to Ill illustrate the concept
+
+618
+00:29:24,360 --> 00:29:27,960
+I showed here what if we had a
+
+619
+00:29:26,279 --> 00:29:30,240
+two-dimensional vector
+
+620
+00:29:27,960 --> 00:29:33,399
+space where the two-dimensional Vector
+
+621
+00:29:30,240 --> 00:29:36,240
+space the xais here is corresponding to
+
+622
+00:29:33,399 --> 00:29:38,679
+whether it's animate or not and the the
+
+623
+00:29:36,240 --> 00:29:41,480
+Y AIS here is corresponding to whether
+
+624
+00:29:38,679 --> 00:29:44,080
+it's like positive sentiment or not and
+
+625
+00:29:41,480 --> 00:29:46,399
+so this is kind of like our ideal uh
+
+626
+00:29:44,080 --> 00:29:49,799
+goal
+
+627
+00:29:46,399 --> 00:29:52,279
+here um so why would we want to do this
+
+628
+00:29:49,799 --> 00:29:52,279
+yeah sorry
+
+629
+00:29:56,320 --> 00:30:03,399
+guys what do the like in the one it's
+
+630
+00:30:00,919 --> 00:30:06,399
+one
+
+631
+00:30:03,399 --> 00:30:06,399
+yep
+
+632
+00:30:07,200 --> 00:30:12,519
+like so what would the four entries do
+
+633
+00:30:09,880 --> 00:30:14,799
+here the four entries here are learned
+
+634
+00:30:12,519 --> 00:30:17,039
+so they are um they're learned just
+
+635
+00:30:14,799 --> 00:30:18,519
+together with the model um and I'm going
+
+636
+00:30:17,039 --> 00:30:22,120
+to talk about exactly how we learn them
+
+637
+00:30:18,519 --> 00:30:24,000
+soon but the the final goal is that
+
+638
+00:30:22,120 --> 00:30:25,399
+after learning has happened they look
+
+639
+00:30:24,000 --> 00:30:26,799
+they have these two properties like
+
+640
+00:30:25,399 --> 00:30:28,600
+similar words are close together in the
+
+641
+00:30:26,799 --> 00:30:30,080
+vectorace
+
+642
+00:30:28,600 --> 00:30:32,640
+and
+
+643
+00:30:30,080 --> 00:30:35,679
+um that's like number one that's the
+
+644
+00:30:32,640 --> 00:30:37,600
+most important and then number two is
+
+645
+00:30:35,679 --> 00:30:39,279
+ideally these uh features would have
+
+646
+00:30:37,600 --> 00:30:41,200
+some meaning uh maybe human
+
+647
+00:30:39,279 --> 00:30:44,720
+interpretable meaning maybe not human
+
+648
+00:30:41,200 --> 00:30:47,880
+interpretable meaning but
+
+649
+00:30:44,720 --> 00:30:50,880
+yeah so um one thing that I should
+
+650
+00:30:47,880 --> 00:30:53,159
+mention is I I showed a contrast between
+
+651
+00:30:50,880 --> 00:30:55,159
+the bag of words uh the one hot
+
+652
+00:30:53,159 --> 00:30:57,000
+representations here and the dense
+
+653
+00:30:55,159 --> 00:31:00,880
+representations here and I used this
+
+654
+00:30:57,000 --> 00:31:03,880
+look look up operation for both of them
+
+655
+00:31:00,880 --> 00:31:07,399
+and this this lookup
+
+656
+00:31:03,880 --> 00:31:09,559
+operation actually um can be viewed as
+
+657
+00:31:07,399 --> 00:31:11,799
+grabbing a single Vector from a big
+
+658
+00:31:09,559 --> 00:31:14,919
+Matrix of word
+
+659
+00:31:11,799 --> 00:31:17,760
+embeddings and
+
+660
+00:31:14,919 --> 00:31:19,760
+so the way it can work is like we have
+
+661
+00:31:17,760 --> 00:31:22,919
+this big vector and then we look up word
+
+662
+00:31:19,760 --> 00:31:25,919
+number two in a zero index Matrix and it
+
+663
+00:31:22,919 --> 00:31:27,799
+would just grab this out of that Matrix
+
+664
+00:31:25,919 --> 00:31:29,880
+and that's practically what most like
+
+665
+00:31:27,799 --> 00:31:32,240
+deep learning libraries or or whatever
+
+666
+00:31:29,880 --> 00:31:35,840
+Library you use are going to be
+
+667
+00:31:32,240 --> 00:31:38,000
+doing but another uh way you can view it
+
+668
+00:31:35,840 --> 00:31:40,880
+is you can view it as multiplying by a
+
+669
+00:31:38,000 --> 00:31:43,880
+one hot vector and so you have this
+
+670
+00:31:40,880 --> 00:31:48,679
+Vector uh exactly the same Matrix uh but
+
+671
+00:31:43,880 --> 00:31:50,799
+you just multiply by a vector uh 0 1 z z
+
+672
+00:31:48,679 --> 00:31:55,720
+and that gives you exactly the same
+
+673
+00:31:50,799 --> 00:31:58,200
+things um so the Practical imple
+
+674
+00:31:55,720 --> 00:31:59,720
+implementations of this uh uh tend to be
+
+675
+00:31:58,200 --> 00:32:01,279
+the first one because the first one's a
+
+676
+00:31:59,720 --> 00:32:04,679
+lot faster to implement you don't need
+
+677
+00:32:01,279 --> 00:32:06,760
+to multiply like this big thing by a
+
+678
+00:32:04,679 --> 00:32:11,000
+huge Vector but there
+
+679
+00:32:06,760 --> 00:32:13,880
+are advantages of knowing the second one
+
+680
+00:32:11,000 --> 00:32:15,519
+uh just to give an example what if you
+
+681
+00:32:13,880 --> 00:32:19,600
+for whatever reason you came up with
+
+682
+00:32:15,519 --> 00:32:21,440
+like an a crazy model that predicts a
+
+683
+00:32:19,600 --> 00:32:24,120
+probability distribution over words
+
+684
+00:32:21,440 --> 00:32:25,720
+instead of just words maybe it's a
+
+685
+00:32:24,120 --> 00:32:27,679
+language model that has an idea of what
+
+686
+00:32:25,720 --> 00:32:30,200
+the next word is going to look like
+
+687
+00:32:27,679 --> 00:32:32,159
+and maybe your um maybe your model
+
+688
+00:32:30,200 --> 00:32:35,279
+thinks the next word has a 50%
+
+689
+00:32:32,159 --> 00:32:36,600
+probability of being capped 30%
+
+690
+00:32:35,279 --> 00:32:42,279
+probability of being
+
+691
+00:32:36,600 --> 00:32:44,960
+dog and uh 2% probability uh sorry uh
+
+692
+00:32:42,279 --> 00:32:47,200
+20% probability being
+
+693
+00:32:44,960 --> 00:32:50,000
+bir you can take this vector and
+
+694
+00:32:47,200 --> 00:32:51,480
+multiply it by The Matrix and get like a
+
+695
+00:32:50,000 --> 00:32:53,639
+word embedding that's kind of a mix of
+
+696
+00:32:51,480 --> 00:32:55,639
+all of those word which might be
+
+697
+00:32:53,639 --> 00:32:57,960
+interesting and let you do creative
+
+698
+00:32:55,639 --> 00:33:02,120
+things so um knowing that these two
+
+699
+00:32:57,960 --> 00:33:05,360
+things are the same are the same is kind
+
+700
+00:33:02,120 --> 00:33:05,360
+of useful for that kind of
+
+701
+00:33:05,919 --> 00:33:11,480
+thing um any any questions about this
+
+702
+00:33:09,120 --> 00:33:13,919
+I'm G to talk about how we train next so
+
+703
+00:33:11,480 --> 00:33:18,159
+maybe maybe I can goow into
+
+704
+00:33:13,919 --> 00:33:23,159
+that okay cool so how do we get the
+
+705
+00:33:18,159 --> 00:33:25,840
+vectors uh like the question uh so up
+
+706
+00:33:23,159 --> 00:33:27,519
+until now we trained a bag of words
+
+707
+00:33:25,840 --> 00:33:29,080
+model and the way we trained a bag of
+
+708
+00:33:27,519 --> 00:33:31,159
+words model was using the structured
+
+709
+00:33:29,080 --> 00:33:35,440
+perceptron algorithm where if the model
+
+710
+00:33:31,159 --> 00:33:39,639
+got the answer wrong we would either
+
+711
+00:33:35,440 --> 00:33:42,799
+increment or decrement the embeddings
+
+712
+00:33:39,639 --> 00:33:45,080
+based on whether uh whether the label
+
+713
+00:33:42,799 --> 00:33:46,559
+was positive or negative right so I
+
+714
+00:33:45,080 --> 00:33:48,919
+showed an example of this very simple
+
+715
+00:33:46,559 --> 00:33:51,039
+algorithm you don't even uh need to
+
+716
+00:33:48,919 --> 00:33:52,480
+write any like numpy or anything like
+
+717
+00:33:51,039 --> 00:33:55,919
+that to implement that
+
+718
+00:33:52,480 --> 00:33:59,559
+algorithm uh so here here it is so we
+
+719
+00:33:55,919 --> 00:34:02,320
+have like 4X why in uh data we extract
+
+720
+00:33:59,559 --> 00:34:04,639
+the features we run the classifier uh we
+
+721
+00:34:02,320 --> 00:34:07,440
+have the predicted why and then we
+
+722
+00:34:04,639 --> 00:34:09,480
+increment or decrement
+
+723
+00:34:07,440 --> 00:34:12,679
+features but how do we train more
+
+724
+00:34:09,480 --> 00:34:15,599
+complex models so I think most people
+
+725
+00:34:12,679 --> 00:34:17,079
+here have taken a uh machine learning
+
+726
+00:34:15,599 --> 00:34:19,159
+class of some kind so this will be
+
+727
+00:34:17,079 --> 00:34:21,079
+reviewed for a lot of people uh but
+
+728
+00:34:19,159 --> 00:34:22,280
+basically we do this uh by doing
+
+729
+00:34:21,079 --> 00:34:24,839
+gradient
+
+730
+00:34:22,280 --> 00:34:27,240
+descent and in order to do so we write
+
+731
+00:34:24,839 --> 00:34:29,919
+down a loss function calculate the
+
+732
+00:34:27,240 --> 00:34:30,919
+derivatives of the L function with
+
+733
+00:34:29,919 --> 00:34:35,079
+respect to the
+
+734
+00:34:30,919 --> 00:34:37,320
+parameters and move uh the parameters in
+
+735
+00:34:35,079 --> 00:34:40,839
+the direction that reduces the loss
+
+736
+00:34:37,320 --> 00:34:42,720
+mtion and so specifically for this bag
+
+737
+00:34:40,839 --> 00:34:45,560
+of words or continuous bag of words
+
+738
+00:34:42,720 --> 00:34:48,240
+model um we want this loss of function
+
+739
+00:34:45,560 --> 00:34:50,839
+to be a loss function that gets lower as
+
+740
+00:34:48,240 --> 00:34:52,240
+the model gets better and I'm going to
+
+741
+00:34:50,839 --> 00:34:54,000
+give two examples from binary
+
+742
+00:34:52,240 --> 00:34:57,400
+classification both of these are used in
+
+743
+00:34:54,000 --> 00:34:58,839
+NLP models uh reasonably frequently
+
+744
+00:34:57,400 --> 00:35:01,440
+uh there's a bunch of other loss
+
+745
+00:34:58,839 --> 00:35:02,800
+functions but these are kind of the two
+
+746
+00:35:01,440 --> 00:35:05,480
+major
+
+747
+00:35:02,800 --> 00:35:08,160
+ones so the first one um which is
+
+748
+00:35:05,480 --> 00:35:10,160
+actually less frequent is the hinge loss
+
+749
+00:35:08,160 --> 00:35:13,400
+and then the second one is taking a
+
+750
+00:35:10,160 --> 00:35:15,800
+sigmoid and then doing negative log
+
+751
+00:35:13,400 --> 00:35:19,760
+likelyhood so the hinge loss basically
+
+752
+00:35:15,800 --> 00:35:22,760
+what we do is we uh take the max of the
+
+753
+00:35:19,760 --> 00:35:26,119
+label times the score that is output by
+
+754
+00:35:22,760 --> 00:35:29,200
+the model and zero and what this looks
+
+755
+00:35:26,119 --> 00:35:33,480
+like is we have a hinged loss uh where
+
+756
+00:35:29,200 --> 00:35:36,880
+if Y is equal to one the loss if Y is
+
+757
+00:35:33,480 --> 00:35:39,520
+greater than zero is zero so as long as
+
+758
+00:35:36,880 --> 00:35:42,680
+we get basically as long as we get the
+
+759
+00:35:39,520 --> 00:35:45,079
+answer right there's no loss um as the
+
+760
+00:35:42,680 --> 00:35:47,400
+answer gets more wrong the loss gets
+
+761
+00:35:45,079 --> 00:35:49,880
+worse like this and then similarly if
+
+762
+00:35:47,400 --> 00:35:53,160
+the label is negative if we get a
+
+763
+00:35:49,880 --> 00:35:54,839
+negative score uh then we get zero loss
+
+764
+00:35:53,160 --> 00:35:55,800
+and the loss increases if we have a
+
+765
+00:35:54,839 --> 00:35:58,800
+positive
+
+766
+00:35:55,800 --> 00:36:00,800
+score so the sigmoid plus negative log
+
+767
+00:35:58,800 --> 00:36:05,440
+likelihood the way this works is you
+
+768
+00:36:00,800 --> 00:36:07,400
+multiply y * the score here and um then
+
+769
+00:36:05,440 --> 00:36:09,960
+we have the sigmoid function which is
+
+770
+00:36:07,400 --> 00:36:14,079
+just kind of a nice function that looks
+
+771
+00:36:09,960 --> 00:36:15,440
+like this with zero and one centered
+
+772
+00:36:14,079 --> 00:36:19,480
+around
+
+773
+00:36:15,440 --> 00:36:21,240
+zero and then we take the negative log
+
+774
+00:36:19,480 --> 00:36:22,319
+of this sigmoid function or the negative
+
+775
+00:36:21,240 --> 00:36:27,160
+log
+
+776
+00:36:22,319 --> 00:36:28,520
+likelihood and that gives us a uh L that
+
+777
+00:36:27,160 --> 00:36:30,440
+looks a little bit like this so
+
+778
+00:36:28,520 --> 00:36:32,640
+basically you can see that these look
+
+779
+00:36:30,440 --> 00:36:36,040
+very similar right the difference being
+
+780
+00:36:32,640 --> 00:36:37,760
+that the hinge loss is uh sharp and we
+
+781
+00:36:36,040 --> 00:36:41,119
+get exactly a zero loss if we get the
+
+782
+00:36:37,760 --> 00:36:44,319
+answer right and the sigmoid is smooth
+
+783
+00:36:41,119 --> 00:36:48,440
+uh and we never get a zero
+
+784
+00:36:44,319 --> 00:36:50,680
+loss um so does anyone have an idea of
+
+785
+00:36:48,440 --> 00:36:53,119
+the benefits and disadvantages of
+
+786
+00:36:50,680 --> 00:36:55,680
+these I kind of flashed one on the
+
+787
+00:36:53,119 --> 00:36:57,599
+screen already
+
+788
+00:36:55,680 --> 00:36:59,400
+but
+
+789
+00:36:57,599 --> 00:37:01,359
+so I flash that on the screen so I'll
+
+790
+00:36:59,400 --> 00:37:03,680
+give this one and then I can have a quiz
+
+791
+00:37:01,359 --> 00:37:06,319
+about the sign but the the hinge glass
+
+792
+00:37:03,680 --> 00:37:07,720
+is more closely linked to accuracy and
+
+793
+00:37:06,319 --> 00:37:10,400
+the reason why it's more closely linked
+
+794
+00:37:07,720 --> 00:37:13,640
+to accuracy is because basically we will
+
+795
+00:37:10,400 --> 00:37:16,079
+get a zero loss if the model gets the
+
+796
+00:37:13,640 --> 00:37:18,319
+answer right so when the model gets all
+
+797
+00:37:16,079 --> 00:37:20,240
+of the answers right we will just stop
+
+798
+00:37:18,319 --> 00:37:22,760
+updating our model whatsoever because we
+
+799
+00:37:20,240 --> 00:37:25,440
+never we don't have any loss whatsoever
+
+800
+00:37:22,760 --> 00:37:27,720
+and the gradient of the loss is zero um
+
+801
+00:37:25,440 --> 00:37:29,960
+what about the sigmoid uh a negative log
+
+802
+00:37:27,720 --> 00:37:33,160
+likelihood uh there there's kind of two
+
+803
+00:37:29,960 --> 00:37:36,160
+major advantages of this anyone want to
+
+804
+00:37:33,160 --> 00:37:36,160
+review their machine learning
+
+805
+00:37:38,240 --> 00:37:41,800
+test sorry what was
+
+806
+00:37:43,800 --> 00:37:49,960
+that for for R uh yeah maybe there's a
+
+807
+00:37:48,200 --> 00:37:51,319
+more direct I think I know what you're
+
+808
+00:37:49,960 --> 00:37:54,560
+saying but maybe there's a more direct
+
+809
+00:37:51,319 --> 00:37:54,560
+way to say that um
+
+810
+00:37:54,839 --> 00:38:00,760
+yeah yeah so the gradient is nonzero
+
+811
+00:37:57,560 --> 00:38:04,240
+everywhere and uh the gradient also kind
+
+812
+00:38:00,760 --> 00:38:05,839
+of increases as your score gets worse so
+
+813
+00:38:04,240 --> 00:38:08,440
+those are that's one advantage it makes
+
+814
+00:38:05,839 --> 00:38:11,240
+it easier to optimize models um another
+
+815
+00:38:08,440 --> 00:38:13,839
+one linked to the ROC score but maybe we
+
+816
+00:38:11,240 --> 00:38:13,839
+could say it more
+
+817
+00:38:16,119 --> 00:38:19,400
+directly any
+
+818
+00:38:20,040 --> 00:38:26,920
+ideas okay um basically the sigmoid can
+
+819
+00:38:23,240 --> 00:38:30,160
+be interpreted as a probability so um if
+
+820
+00:38:26,920 --> 00:38:32,839
+the the sigmoid is between Zer and one
+
+821
+00:38:30,160 --> 00:38:34,640
+uh and because it's between zero and one
+
+822
+00:38:32,839 --> 00:38:36,720
+we can say the sigmoid is a
+
+823
+00:38:34,640 --> 00:38:38,640
+probability um and that can be useful
+
+824
+00:38:36,720 --> 00:38:40,119
+for various things like if we want a
+
+825
+00:38:38,640 --> 00:38:41,960
+downstream model or if we want a
+
+826
+00:38:40,119 --> 00:38:45,480
+confidence prediction out of the model
+
+827
+00:38:41,960 --> 00:38:48,200
+so those are two uh advantages of using
+
+828
+00:38:45,480 --> 00:38:49,920
+a s plus negative log likelihood there's
+
+829
+00:38:48,200 --> 00:38:53,160
+no probabilistic interpretation to
+
+830
+00:38:49,920 --> 00:38:56,560
+something transing theas
+
+831
+00:38:53,160 --> 00:38:59,200
+basically cool um so the next thing that
+
+832
+00:38:56,560 --> 00:39:01,240
+that we do is we calculate derivatives
+
+833
+00:38:59,200 --> 00:39:04,040
+and we calculate the derivative of the
+
+834
+00:39:01,240 --> 00:39:05,920
+parameter given the loss function um to
+
+835
+00:39:04,040 --> 00:39:09,839
+give an example of the bag of words
+
+836
+00:39:05,920 --> 00:39:13,480
+model and the hinge loss um the hinge
+
+837
+00:39:09,839 --> 00:39:16,480
+loss as I said is the max of the score
+
+838
+00:39:13,480 --> 00:39:19,359
+and times y in the bag of words model
+
+839
+00:39:16,480 --> 00:39:22,640
+the score was the frequency of that
+
+840
+00:39:19,359 --> 00:39:25,880
+vocabulary item in the input multiplied
+
+841
+00:39:22,640 --> 00:39:27,680
+by the weight here and so if we this is
+
+842
+00:39:25,880 --> 00:39:29,520
+a simple a function that I can just do
+
+843
+00:39:27,680 --> 00:39:34,440
+the derivative by hand and if I do the
+
+844
+00:39:29,520 --> 00:39:36,920
+deriva by hand what comes out is if y *
+
+845
+00:39:34,440 --> 00:39:39,319
+this value is greater than zero so in
+
+846
+00:39:36,920 --> 00:39:44,640
+other words if this Max uh picks this
+
+847
+00:39:39,319 --> 00:39:48,319
+instead of this then the derivative is y
+
+848
+00:39:44,640 --> 00:39:52,359
+* stre and otherwise uh it
+
+849
+00:39:48,319 --> 00:39:52,359
+is in the opposite
+
+850
+00:39:55,400 --> 00:40:00,160
+direction
+
+851
+00:39:56,920 --> 00:40:02,839
+then uh optimizing gradients uh we do
+
+852
+00:40:00,160 --> 00:40:06,200
+standard uh in standard stochastic
+
+853
+00:40:02,839 --> 00:40:07,839
+gradient descent uh which is the most
+
+854
+00:40:06,200 --> 00:40:10,920
+standard optimization algorithm for
+
+855
+00:40:07,839 --> 00:40:14,440
+these models uh we basically have a
+
+856
+00:40:10,920 --> 00:40:17,440
+gradient over uh you take the gradient
+
+857
+00:40:14,440 --> 00:40:20,040
+over the parameter of the loss function
+
+858
+00:40:17,440 --> 00:40:22,480
+and we call it GT so here um sorry I
+
+859
+00:40:20,040 --> 00:40:25,599
+switched my terminology between W and
+
+860
+00:40:22,480 --> 00:40:28,280
+Theta so this could be W uh the previous
+
+861
+00:40:25,599 --> 00:40:31,000
+value of w
+
+862
+00:40:28,280 --> 00:40:35,440
+um and this is the gradient of the loss
+
+863
+00:40:31,000 --> 00:40:37,040
+and then uh we take the previous value
+
+864
+00:40:35,440 --> 00:40:39,680
+and then we subtract out the learning
+
+865
+00:40:37,040 --> 00:40:39,680
+rate times the
+
+866
+00:40:40,680 --> 00:40:45,720
+gradient and uh there are many many
+
+867
+00:40:43,200 --> 00:40:47,280
+other optimization options uh I'll cover
+
+868
+00:40:45,720 --> 00:40:50,960
+the more frequent one called Adam at the
+
+869
+00:40:47,280 --> 00:40:54,319
+end of this uh this lecture but um this
+
+870
+00:40:50,960 --> 00:40:57,160
+is the basic way of optimizing the
+
+871
+00:40:54,319 --> 00:41:00,599
+model so
+
+872
+00:40:57,160 --> 00:41:03,359
+then my question now is what is this
+
+873
+00:41:00,599 --> 00:41:07,000
+algorithm with respect
+
+874
+00:41:03,359 --> 00:41:10,119
+to this is an algorithm that is
+
+875
+00:41:07,000 --> 00:41:12,280
+taking that has a loss function it's
+
+876
+00:41:10,119 --> 00:41:14,079
+calculating derivatives and it's
+
+877
+00:41:12,280 --> 00:41:17,240
+optimizing gradients using stochastic
+
+878
+00:41:14,079 --> 00:41:18,839
+gradient descent so does anyone have a
+
+879
+00:41:17,240 --> 00:41:20,960
+guess about what the loss function is
+
+880
+00:41:18,839 --> 00:41:23,520
+here and maybe what is the learning rate
+
+881
+00:41:20,960 --> 00:41:23,520
+of stas
+
+882
+00:41:24,319 --> 00:41:29,480
+gradient I kind of gave you a hint about
+
+883
+00:41:26,599 --> 00:41:29,480
+the L one
+
+884
+00:41:31,640 --> 00:41:37,839
+actually and just to recap what this is
+
+885
+00:41:34,440 --> 00:41:41,440
+doing here it's um if predicted Y is
+
+886
+00:41:37,839 --> 00:41:44,560
+equal to Y then it is moving the uh the
+
+887
+00:41:41,440 --> 00:41:48,240
+future weights in the direction of Y
+
+888
+00:41:44,560 --> 00:41:48,240
+times the frequency
+
+889
+00:41:52,599 --> 00:41:56,960
+Vector
+
+890
+00:41:55,240 --> 00:41:59,079
+yeah
+
+891
+00:41:56,960 --> 00:42:01,640
+yeah exactly so the loss function is
+
+892
+00:41:59,079 --> 00:42:05,800
+hinge loss and the learning rate is one
+
+893
+00:42:01,640 --> 00:42:07,880
+um and just to show how that you know
+
+894
+00:42:05,800 --> 00:42:12,359
+corresponds we have this if statement
+
+895
+00:42:07,880 --> 00:42:12,359
+here and we have the increment of the
+
+896
+00:42:12,960 --> 00:42:20,240
+features and this is what the um what
+
+897
+00:42:16,920 --> 00:42:21,599
+the L sorry the derivative looked like
+
+898
+00:42:20,240 --> 00:42:24,240
+so we have
+
+899
+00:42:21,599 --> 00:42:26,920
+if this is moving in the right direction
+
+900
+00:42:24,240 --> 00:42:29,520
+for the label uh then we increment
+
+901
+00:42:26,920 --> 00:42:31,599
+otherwise we do nothing so
+
+902
+00:42:29,520 --> 00:42:33,559
+basically you can see that even this
+
+903
+00:42:31,599 --> 00:42:35,200
+really simple algorithm that I you know
+
+904
+00:42:33,559 --> 00:42:37,480
+implemented with a few lines of python
+
+905
+00:42:35,200 --> 00:42:38,839
+is essentially equivalent to this uh
+
+906
+00:42:37,480 --> 00:42:40,760
+stochastic gradient descent that we
+
+907
+00:42:38,839 --> 00:42:44,559
+doing
+
+908
+00:42:40,760 --> 00:42:46,359
+models so the good news about this is
+
+909
+00:42:44,559 --> 00:42:48,359
+you know this this is really simple but
+
+910
+00:42:46,359 --> 00:42:50,599
+it only really works forit like a bag of
+
+911
+00:42:48,359 --> 00:42:55,400
+words model or a simple feature based
+
+912
+00:42:50,599 --> 00:42:57,200
+model uh but it opens up a lot of uh new
+
+913
+00:42:55,400 --> 00:43:00,440
+possibilities for how we can optimize
+
+914
+00:42:57,200 --> 00:43:01,599
+models and in particular I mentioned uh
+
+915
+00:43:00,440 --> 00:43:04,839
+that there was a problem with
+
+916
+00:43:01,599 --> 00:43:08,200
+combination features last class like
+
+917
+00:43:04,839 --> 00:43:11,200
+don't hate and don't love are not just
+
+918
+00:43:08,200 --> 00:43:12,760
+you know hate plus don't and love plus
+
+919
+00:43:11,200 --> 00:43:14,119
+don't it's actually the combination of
+
+920
+00:43:12,760 --> 00:43:17,680
+the two is really
+
+921
+00:43:14,119 --> 00:43:20,160
+important and so um yeah just to give an
+
+922
+00:43:17,680 --> 00:43:23,440
+example we have don't love is maybe bad
+
+923
+00:43:20,160 --> 00:43:26,960
+uh nothing I don't love is very
+
+924
+00:43:23,440 --> 00:43:30,960
+good and so in order
+
+925
+00:43:26,960 --> 00:43:34,040
+to solve this problem we turn to neural
+
+926
+00:43:30,960 --> 00:43:37,160
+networks and the way we do this is we
+
+927
+00:43:34,040 --> 00:43:39,119
+have a lookup of dense embeddings sorry
+
+928
+00:43:37,160 --> 00:43:41,839
+I actually I just realized my coloring
+
+929
+00:43:39,119 --> 00:43:44,119
+is off I was using red to indicate dense
+
+930
+00:43:41,839 --> 00:43:46,480
+embeddings so this should be maybe red
+
+931
+00:43:44,119 --> 00:43:49,319
+instead of blue but um we take these
+
+932
+00:43:46,480 --> 00:43:51,200
+stents embeddings and then we create
+
+933
+00:43:49,319 --> 00:43:53,720
+some complicated function to extract
+
+934
+00:43:51,200 --> 00:43:55,079
+combination features um and then use
+
+935
+00:43:53,720 --> 00:43:57,359
+those to calculate
+
+936
+00:43:55,079 --> 00:44:02,200
+scores
+
+937
+00:43:57,359 --> 00:44:04,480
+um and so we calculate these combination
+
+938
+00:44:02,200 --> 00:44:08,240
+features and what we want to do is we
+
+939
+00:44:04,480 --> 00:44:12,880
+want to extract vectors from the input
+
+940
+00:44:08,240 --> 00:44:12,880
+where each Vector has features
+
+941
+00:44:15,839 --> 00:44:21,040
+um sorry this is in the wrong order so
+
+942
+00:44:18,240 --> 00:44:22,559
+I'll I'll get back to this um so this
+
+943
+00:44:21,040 --> 00:44:25,319
+this was talking about the The
+
+944
+00:44:22,559 --> 00:44:27,200
+Continuous bag of words features so the
+
+945
+00:44:25,319 --> 00:44:30,960
+problem with the continuous bag of words
+
+946
+00:44:27,200 --> 00:44:30,960
+features was we were extracting
+
+947
+00:44:31,359 --> 00:44:36,359
+features
+
+948
+00:44:33,079 --> 00:44:36,359
+um like
+
+949
+00:44:36,839 --> 00:44:41,400
+this but then we were directly using the
+
+950
+00:44:39,760 --> 00:44:43,359
+the feature the dense features that we
+
+951
+00:44:41,400 --> 00:44:45,559
+extracted to make predictions without
+
+952
+00:44:43,359 --> 00:44:48,839
+actually allowing for any interactions
+
+953
+00:44:45,559 --> 00:44:51,839
+between the features um and
+
+954
+00:44:48,839 --> 00:44:55,160
+so uh neural networks the way we fix
+
+955
+00:44:51,839 --> 00:44:57,079
+this is we first extract these features
+
+956
+00:44:55,160 --> 00:44:59,440
+uh we take these these features of each
+
+957
+00:44:57,079 --> 00:45:04,000
+word embedding and then we run them
+
+958
+00:44:59,440 --> 00:45:07,240
+through uh kind of linear transforms in
+
+959
+00:45:04,000 --> 00:45:09,880
+nonlinear uh like linear multiplications
+
+960
+00:45:07,240 --> 00:45:10,880
+and then nonlinear transforms to extract
+
+961
+00:45:09,880 --> 00:45:13,920
+additional
+
+962
+00:45:10,880 --> 00:45:15,839
+features and uh finally run this through
+
+963
+00:45:13,920 --> 00:45:18,640
+several layers and then use the
+
+964
+00:45:15,839 --> 00:45:21,119
+resulting features to make our
+
+965
+00:45:18,640 --> 00:45:23,200
+predictions and when we do this this
+
+966
+00:45:21,119 --> 00:45:25,319
+allows us to do more uh interesting
+
+967
+00:45:23,200 --> 00:45:28,319
+things so like for example we could
+
+968
+00:45:25,319 --> 00:45:30,000
+learn feature combination a node in the
+
+969
+00:45:28,319 --> 00:45:32,599
+second layer might be feature one and
+
+970
+00:45:30,000 --> 00:45:35,240
+feature five are active so that could be
+
+971
+00:45:32,599 --> 00:45:38,680
+like feature one corresponds to negative
+
+972
+00:45:35,240 --> 00:45:43,640
+sentiment words like hate
+
+973
+00:45:38,680 --> 00:45:45,839
+despise um and other things like that so
+
+974
+00:45:43,640 --> 00:45:50,079
+for hate and despise feature one would
+
+975
+00:45:45,839 --> 00:45:53,119
+have a high value like 8.0 and then
+
+976
+00:45:50,079 --> 00:45:55,480
+7.2 and then we also have negation words
+
+977
+00:45:53,119 --> 00:45:57,040
+like don't or not or something like that
+
+978
+00:45:55,480 --> 00:46:00,040
+and those would
+
+979
+00:45:57,040 --> 00:46:00,040
+have
+
+980
+00:46:03,720 --> 00:46:08,640
+don't would have a high value for like 2
+
+981
+00:46:11,880 --> 00:46:15,839
+five and so these would be the word
+
+982
+00:46:14,200 --> 00:46:18,040
+embeddings where each word embedding
+
+983
+00:46:15,839 --> 00:46:20,599
+corresponded to you know features of the
+
+984
+00:46:18,040 --> 00:46:23,480
+words and
+
+985
+00:46:20,599 --> 00:46:25,480
+then um after that we would extract
+
+986
+00:46:23,480 --> 00:46:29,319
+feature combinations in this second
+
+987
+00:46:25,480 --> 00:46:32,079
+layer that say oh we see at least one
+
+988
+00:46:29,319 --> 00:46:33,760
+word where the first feature is active
+
+989
+00:46:32,079 --> 00:46:36,359
+and we see at least one word where the
+
+990
+00:46:33,760 --> 00:46:37,920
+fifth feature is active so now that
+
+991
+00:46:36,359 --> 00:46:40,640
+allows us to capture the fact that we
+
+992
+00:46:37,920 --> 00:46:42,319
+saw like don't hate or don't despise or
+
+993
+00:46:40,640 --> 00:46:44,559
+not hate or not despise or something
+
+994
+00:46:42,319 --> 00:46:44,559
+like
+
+995
+00:46:45,079 --> 00:46:51,760
+that so this is the way uh kind of this
+
+996
+00:46:49,680 --> 00:46:54,839
+is a deep uh continuous bag of words
+
+997
+00:46:51,760 --> 00:46:56,839
+model um this actually was proposed in
+
+998
+00:46:54,839 --> 00:46:58,119
+205 15 I don't think I have the
+
+999
+00:46:56,839 --> 00:47:02,599
+reference on the slide but I think it's
+
+1000
+00:46:58,119 --> 00:47:05,040
+in the notes um on the website and
+
+1001
+00:47:02,599 --> 00:47:07,200
+actually at that point in time they
+
+1002
+00:47:05,040 --> 00:47:09,200
+demon there were several interesting
+
+1003
+00:47:07,200 --> 00:47:11,960
+results that showed that even this like
+
+1004
+00:47:09,200 --> 00:47:13,960
+really simple model did really well uh
+
+1005
+00:47:11,960 --> 00:47:16,319
+at text classification and other simple
+
+1006
+00:47:13,960 --> 00:47:18,640
+tasks like that because it was able to
+
+1007
+00:47:16,319 --> 00:47:21,720
+you know share features of the words and
+
+1008
+00:47:18,640 --> 00:47:23,800
+then extract combinations to the
+
+1009
+00:47:21,720 --> 00:47:28,200
+features
+
+1010
+00:47:23,800 --> 00:47:29,760
+so um in order order to learn these we
+
+1011
+00:47:28,200 --> 00:47:30,920
+need to start turning to neural networks
+
+1012
+00:47:29,760 --> 00:47:34,400
+and the reason why we need to start
+
+1013
+00:47:30,920 --> 00:47:38,040
+turning to neural networks is
+
+1014
+00:47:34,400 --> 00:47:41,920
+because while I can calculate the loss
+
+1015
+00:47:38,040 --> 00:47:43,280
+function of the while I can calculate
+
+1016
+00:47:41,920 --> 00:47:44,839
+the loss function of the hinged loss for
+
+1017
+00:47:43,280 --> 00:47:47,720
+a bag of words model by hand I
+
+1018
+00:47:44,839 --> 00:47:49,359
+definitely don't I probably could but
+
+1019
+00:47:47,720 --> 00:47:51,240
+don't want to do it for a model that
+
+1020
+00:47:49,359 --> 00:47:53,200
+starts become as complicated as this
+
+1021
+00:47:51,240 --> 00:47:57,440
+with multiple Matrix multiplications
+
+1022
+00:47:53,200 --> 00:48:00,520
+Andes and stuff like that so the way we
+
+1023
+00:47:57,440 --> 00:48:05,000
+do this just a very brief uh coverage of
+
+1024
+00:48:00,520 --> 00:48:06,200
+this uh for because um I think probably
+
+1025
+00:48:05,000 --> 00:48:08,400
+a lot of people have dealt with neural
+
+1026
+00:48:06,200 --> 00:48:10,200
+networks before um the original
+
+1027
+00:48:08,400 --> 00:48:12,880
+motivation was that we had neurons in
+
+1028
+00:48:10,200 --> 00:48:16,160
+the brain uh where
+
+1029
+00:48:12,880 --> 00:48:18,839
+the each of the neuron synapses took in
+
+1030
+00:48:16,160 --> 00:48:21,480
+an electrical signal and once they got
+
+1031
+00:48:18,839 --> 00:48:24,079
+enough electrical signal they would fire
+
+1032
+00:48:21,480 --> 00:48:25,960
+um but now the current conception of
+
+1033
+00:48:24,079 --> 00:48:28,160
+neural networks or deep learning models
+
+1034
+00:48:25,960 --> 00:48:30,440
+is basically computation
+
+1035
+00:48:28,160 --> 00:48:32,400
+graphs and the way a computation graph
+
+1036
+00:48:30,440 --> 00:48:34,760
+Works um and I'm especially going to
+
+1037
+00:48:32,400 --> 00:48:36,240
+talk about the way it works in natural
+
+1038
+00:48:34,760 --> 00:48:38,119
+language processing which might be a
+
+1039
+00:48:36,240 --> 00:48:42,319
+contrast to the way it works in computer
+
+1040
+00:48:38,119 --> 00:48:43,960
+vision is um we have an expression uh
+
+1041
+00:48:42,319 --> 00:48:46,480
+that looks like this and maybe maybe
+
+1042
+00:48:43,960 --> 00:48:47,640
+it's the expression X corresponding to
+
+1043
+00:48:46,480 --> 00:48:51,880
+uh a
+
+1044
+00:48:47,640 --> 00:48:53,400
+scal um and each node corresponds to
+
+1045
+00:48:51,880 --> 00:48:55,599
+something like a tensor a matrix a
+
+1046
+00:48:53,400 --> 00:48:57,599
+vector a scalar so scaler is uh kind
+
+1047
+00:48:55,599 --> 00:49:00,480
+kind of Zero Dimensional it's a single
+
+1048
+00:48:57,599 --> 00:49:01,720
+value one dimensional two dimensional or
+
+1049
+00:49:00,480 --> 00:49:04,200
+arbitrary
+
+1050
+00:49:01,720 --> 00:49:06,040
+dimensional um and then we also have
+
+1051
+00:49:04,200 --> 00:49:08,000
+nodes that correspond to the result of
+
+1052
+00:49:06,040 --> 00:49:11,480
+function applications so if we have X be
+
+1053
+00:49:08,000 --> 00:49:14,079
+a vector uh we take the vector transpose
+
+1054
+00:49:11,480 --> 00:49:18,160
+and so each Edge represents a function
+
+1055
+00:49:14,079 --> 00:49:20,559
+argument and also a data
+
+1056
+00:49:18,160 --> 00:49:23,960
+dependency and a node with an incoming
+
+1057
+00:49:20,559 --> 00:49:27,000
+Edge is a function of that Edge's tail
+
+1058
+00:49:23,960 --> 00:49:29,040
+node and importantly each node knows how
+
+1059
+00:49:27,000 --> 00:49:30,640
+to compute its value and the value of
+
+1060
+00:49:29,040 --> 00:49:32,640
+its derivative with respect to each
+
+1061
+00:49:30,640 --> 00:49:34,440
+argument times the derivative of an
+
+1062
+00:49:32,640 --> 00:49:37,920
+arbitrary
+
+1063
+00:49:34,440 --> 00:49:41,000
+input and functions could be basically
+
+1064
+00:49:37,920 --> 00:49:45,400
+arbitrary functions it can be unary Nary
+
+1065
+00:49:41,000 --> 00:49:49,440
+unary binary Nary often unary or binary
+
+1066
+00:49:45,400 --> 00:49:52,400
+and computation graphs are directed in
+
+1067
+00:49:49,440 --> 00:49:57,040
+cyclic and um one important thing to
+
+1068
+00:49:52,400 --> 00:50:00,640
+note is that you can um have multiple
+
+1069
+00:49:57,040 --> 00:50:02,559
+ways of expressing the same function so
+
+1070
+00:50:00,640 --> 00:50:04,839
+this is actually really important as you
+
+1071
+00:50:02,559 --> 00:50:06,920
+start implementing things and the reason
+
+1072
+00:50:04,839 --> 00:50:09,359
+why is the left graph and the right
+
+1073
+00:50:06,920 --> 00:50:12,960
+graph both express the same thing the
+
+1074
+00:50:09,359 --> 00:50:18,640
+left graph expresses X
+
+1075
+00:50:12,960 --> 00:50:22,559
+transpose time A Time X where is whereas
+
+1076
+00:50:18,640 --> 00:50:27,160
+this one has x a and then it puts it
+
+1077
+00:50:22,559 --> 00:50:28,760
+into a node that is X transpose a x
+
+1078
+00:50:27,160 --> 00:50:30,319
+and so these Express exactly the same
+
+1079
+00:50:28,760 --> 00:50:32,319
+thing but the graph on the left is
+
+1080
+00:50:30,319 --> 00:50:33,760
+larger and the reason why this is
+
+1081
+00:50:32,319 --> 00:50:38,920
+important is for practical
+
+1082
+00:50:33,760 --> 00:50:40,359
+implementation of neural networks um you
+
+1083
+00:50:38,920 --> 00:50:43,200
+the larger graphs are going to take more
+
+1084
+00:50:40,359 --> 00:50:46,799
+memory and going to be slower usually
+
+1085
+00:50:43,200 --> 00:50:48,200
+and so often um in a neural network we
+
+1086
+00:50:46,799 --> 00:50:49,559
+look at like pipe part which we're going
+
+1087
+00:50:48,200 --> 00:50:52,160
+to look at in a
+
+1088
+00:50:49,559 --> 00:50:55,520
+second
+
+1089
+00:50:52,160 --> 00:50:57,920
+um you will have something you will be
+
+1090
+00:50:55,520 --> 00:50:57,920
+able to
+
+1091
+00:50:58,680 --> 00:51:01,680
+do
+
+1092
+00:51:03,079 --> 00:51:07,880
+this or you'll be able to do
+
+1093
+00:51:18,760 --> 00:51:22,880
+like
+
+1094
+00:51:20,359 --> 00:51:24,839
+this so these are two different options
+
+1095
+00:51:22,880 --> 00:51:26,920
+this one is using more operations and
+
+1096
+00:51:24,839 --> 00:51:29,559
+this one is using using less operations
+
+1097
+00:51:26,920 --> 00:51:31,000
+and this is going to be faster because
+
+1098
+00:51:29,559 --> 00:51:33,119
+basically the implementation within
+
+1099
+00:51:31,000 --> 00:51:34,799
+Pythor will have been optimized for you
+
+1100
+00:51:33,119 --> 00:51:36,799
+it will only require one graph node
+
+1101
+00:51:34,799 --> 00:51:37,880
+instead of multiple graph nodes and
+
+1102
+00:51:36,799 --> 00:51:39,799
+that's even more important when you
+
+1103
+00:51:37,880 --> 00:51:41,040
+start talking about like attention or
+
+1104
+00:51:39,799 --> 00:51:43,920
+something like that which we're going to
+
+1105
+00:51:41,040 --> 00:51:46,079
+be covering very soon um attention is a
+
+1106
+00:51:43,920 --> 00:51:47,359
+very multi-head attention or something
+
+1107
+00:51:46,079 --> 00:51:49,839
+like that is a very complicated
+
+1108
+00:51:47,359 --> 00:51:52,079
+operation so you want to make sure that
+
+1109
+00:51:49,839 --> 00:51:54,359
+you're using the operators that are
+
+1110
+00:51:52,079 --> 00:51:57,359
+available to you to make this more
+
+1111
+00:51:54,359 --> 00:51:57,359
+efficient
+
+1112
+00:51:57,440 --> 00:52:00,760
+um and then finally we could like add
+
+1113
+00:51:59,280 --> 00:52:01,920
+all of these together at the end we
+
+1114
+00:52:00,760 --> 00:52:04,000
+could add a
+
+1115
+00:52:01,920 --> 00:52:05,880
+constant um and then we get this
+
+1116
+00:52:04,000 --> 00:52:09,520
+expression here which gives us kind of a
+
+1117
+00:52:05,880 --> 00:52:09,520
+polinomial polom
+
+1118
+00:52:09,680 --> 00:52:15,760
+expression um also another thing to note
+
+1119
+00:52:13,480 --> 00:52:17,599
+is within a neural network computation
+
+1120
+00:52:15,760 --> 00:52:21,920
+graph variable names are just labelings
+
+1121
+00:52:17,599 --> 00:52:25,359
+of nodes and so if you're using a a
+
+1122
+00:52:21,920 --> 00:52:27,680
+computation graph like this you might
+
+1123
+00:52:25,359 --> 00:52:29,240
+only be declaring one variable here but
+
+1124
+00:52:27,680 --> 00:52:30,839
+actually there's a whole bunch of stuff
+
+1125
+00:52:29,240 --> 00:52:32,359
+going on behind the scenes and all of
+
+1126
+00:52:30,839 --> 00:52:34,240
+that will take memory and computation
+
+1127
+00:52:32,359 --> 00:52:35,440
+time and stuff like that so it's
+
+1128
+00:52:34,240 --> 00:52:37,119
+important to be aware of that if you
+
+1129
+00:52:35,440 --> 00:52:40,400
+want to make your implementations more
+
+1130
+00:52:37,119 --> 00:52:40,400
+efficient than other other
+
+1131
+00:52:41,119 --> 00:52:46,680
+things so we have several algorithms
+
+1132
+00:52:44,480 --> 00:52:49,079
+that go into implementing neural nuts um
+
+1133
+00:52:46,680 --> 00:52:50,760
+the first one is graph construction uh
+
+1134
+00:52:49,079 --> 00:52:53,480
+the second one is forward
+
+1135
+00:52:50,760 --> 00:52:54,839
+propagation uh and graph construction is
+
+1136
+00:52:53,480 --> 00:52:56,359
+basically constructing the graph
+
+1137
+00:52:54,839 --> 00:52:58,680
+declaring ing all the variables stuff
+
+1138
+00:52:56,359 --> 00:53:01,520
+like this the second one is forward
+
+1139
+00:52:58,680 --> 00:53:03,880
+propagation and um the way you do this
+
+1140
+00:53:01,520 --> 00:53:06,480
+is in topological order uh you compute
+
+1141
+00:53:03,880 --> 00:53:08,280
+the value of a node given its inputs and
+
+1142
+00:53:06,480 --> 00:53:11,000
+so basically you start out with all of
+
+1143
+00:53:08,280 --> 00:53:12,680
+the nodes that you give is input and
+
+1144
+00:53:11,000 --> 00:53:16,040
+then you find any node in the graph
+
+1145
+00:53:12,680 --> 00:53:17,799
+where all of its uh all of its tail
+
+1146
+00:53:16,040 --> 00:53:20,280
+nodes or all of its children have been
+
+1147
+00:53:17,799 --> 00:53:22,119
+calculated so in this case that would be
+
+1148
+00:53:20,280 --> 00:53:24,640
+these two nodes and then in arbitrary
+
+1149
+00:53:22,119 --> 00:53:27,000
+order or even in parallel you calculate
+
+1150
+00:53:24,640 --> 00:53:28,280
+the value of all of the satisfied nodes
+
+1151
+00:53:27,000 --> 00:53:31,799
+until you get to the
+
+1152
+00:53:28,280 --> 00:53:34,280
+end and then uh the remaining algorithms
+
+1153
+00:53:31,799 --> 00:53:36,200
+are back propagation and parameter
+
+1154
+00:53:34,280 --> 00:53:38,240
+update I already talked about parameter
+
+1155
+00:53:36,200 --> 00:53:40,799
+update uh using stochastic gradient
+
+1156
+00:53:38,240 --> 00:53:42,760
+descent but for back propagation we then
+
+1157
+00:53:40,799 --> 00:53:45,400
+process examples in Reverse topological
+
+1158
+00:53:42,760 --> 00:53:47,640
+order uh calculate derivatives of
+
+1159
+00:53:45,400 --> 00:53:50,400
+parameters with respect to final
+
+1160
+00:53:47,640 --> 00:53:52,319
+value and so we start out with the very
+
+1161
+00:53:50,400 --> 00:53:54,200
+final value usually this is your loss
+
+1162
+00:53:52,319 --> 00:53:56,200
+function and then you just step
+
+1163
+00:53:54,200 --> 00:54:00,440
+backwards in top ological order to
+
+1164
+00:53:56,200 --> 00:54:04,160
+calculate the derivatives of all these
+
+1165
+00:54:00,440 --> 00:54:05,920
+so um this is pretty simple I think a
+
+1166
+00:54:04,160 --> 00:54:08,040
+lot of people may have seen this already
+
+1167
+00:54:05,920 --> 00:54:09,920
+but keeping this in mind as you're
+
+1168
+00:54:08,040 --> 00:54:12,480
+implementing NLP models especially
+
+1169
+00:54:09,920 --> 00:54:14,240
+models that are really memory intensive
+
+1170
+00:54:12,480 --> 00:54:16,559
+or things like that is pretty important
+
+1171
+00:54:14,240 --> 00:54:19,040
+because if you accidentally like for
+
+1172
+00:54:16,559 --> 00:54:21,799
+example calculate the same thing twice
+
+1173
+00:54:19,040 --> 00:54:23,559
+or accidentally create a graph that is
+
+1174
+00:54:21,799 --> 00:54:25,720
+manipulating very large tensors and
+
+1175
+00:54:23,559 --> 00:54:27,319
+creating very large intermediate States
+
+1176
+00:54:25,720 --> 00:54:29,720
+that can kill your memory and and cause
+
+1177
+00:54:27,319 --> 00:54:31,839
+big problems so it's an important thing
+
+1178
+00:54:29,720 --> 00:54:31,839
+to
+
+1179
+00:54:34,359 --> 00:54:38,880
+be um cool any any questions about
+
+1180
+00:54:39,040 --> 00:54:44,440
+this okay if not I will go on to the
+
+1181
+00:54:41,680 --> 00:54:45,680
+next one so neural network Frameworks
+
+1182
+00:54:44,440 --> 00:54:48,920
+there's several neural network
+
+1183
+00:54:45,680 --> 00:54:52,880
+Frameworks but in NLP nowadays I really
+
+1184
+00:54:48,920 --> 00:54:55,079
+only see two and mostly only see one um
+
+1185
+00:54:52,880 --> 00:54:57,960
+so that one that almost everybody us
+
+1186
+00:54:55,079 --> 00:55:01,240
+uses is pie torch um and I would
+
+1187
+00:54:57,960 --> 00:55:04,559
+recommend using it unless you uh you
+
+1188
+00:55:01,240 --> 00:55:07,480
+know if you're a fan of like rust or you
+
+1189
+00:55:04,559 --> 00:55:09,200
+know esoteric uh not esoteric but like
+
+1190
+00:55:07,480 --> 00:55:11,960
+unusual programming languages and you
+
+1191
+00:55:09,200 --> 00:55:14,720
+like Beauty and things like this another
+
+1192
+00:55:11,960 --> 00:55:15,799
+option might be Jacks uh so I'll explain
+
+1193
+00:55:14,720 --> 00:55:18,440
+a little bit about the difference
+
+1194
+00:55:15,799 --> 00:55:19,960
+between them uh and you can pick
+
+1195
+00:55:18,440 --> 00:55:23,559
+accordingly
+
+1196
+00:55:19,960 --> 00:55:25,359
+um first uh both of these Frameworks uh
+
+1197
+00:55:23,559 --> 00:55:26,839
+are developed by big companies and they
+
+1198
+00:55:25,359 --> 00:55:28,520
+have a lot of engineering support behind
+
+1199
+00:55:26,839 --> 00:55:29,720
+them that's kind of an important thing
+
+1200
+00:55:28,520 --> 00:55:31,280
+to think about when you're deciding
+
+1201
+00:55:29,720 --> 00:55:32,599
+which framework to use because you know
+
+1202
+00:55:31,280 --> 00:55:36,000
+it'll be well
+
+1203
+00:55:32,599 --> 00:55:38,039
+supported um pytorch is definitely most
+
+1204
+00:55:36,000 --> 00:55:40,400
+widely used in NLP especially NLP
+
+1205
+00:55:38,039 --> 00:55:44,240
+research um and it's used in some NLP
+
+1206
+00:55:40,400 --> 00:55:47,359
+project J is used in some NLP
+
+1207
+00:55:44,240 --> 00:55:49,960
+projects um pytorch favors Dynamic
+
+1208
+00:55:47,359 --> 00:55:53,760
+execution so what dynamic execution
+
+1209
+00:55:49,960 --> 00:55:55,880
+means is um you basically create a
+
+1210
+00:55:53,760 --> 00:55:59,760
+computation graph and and then execute
+
+1211
+00:55:55,880 --> 00:56:02,760
+it uh every time you process an input uh
+
+1212
+00:55:59,760 --> 00:56:04,680
+in contrast there's also you define the
+
+1213
+00:56:02,760 --> 00:56:07,200
+computation graph first and then execute
+
+1214
+00:56:04,680 --> 00:56:09,280
+it over and over again so in other words
+
+1215
+00:56:07,200 --> 00:56:10,680
+the graph construction step only happens
+
+1216
+00:56:09,280 --> 00:56:13,119
+once kind of at the beginning of
+
+1217
+00:56:10,680 --> 00:56:16,799
+computation and then you compile it
+
+1218
+00:56:13,119 --> 00:56:20,039
+afterwards and it's actually pytorch
+
+1219
+00:56:16,799 --> 00:56:23,359
+supports kind of defining and compiling
+
+1220
+00:56:20,039 --> 00:56:27,480
+and Jax supports more Dynamic things but
+
+1221
+00:56:23,359 --> 00:56:30,160
+the way they were designed is uh is kind
+
+1222
+00:56:27,480 --> 00:56:32,960
+of favoring Dynamic execution or
+
+1223
+00:56:30,160 --> 00:56:37,079
+favoring definition in population
+
+1224
+00:56:32,960 --> 00:56:39,200
+and the difference between these two is
+
+1225
+00:56:37,079 --> 00:56:41,760
+this one gives you more flexibility this
+
+1226
+00:56:39,200 --> 00:56:45,440
+one gives you better optimization in wor
+
+1227
+00:56:41,760 --> 00:56:49,760
+speed if you want to if you want to do
+
+1228
+00:56:45,440 --> 00:56:52,400
+that um another thing about Jax is um
+
+1229
+00:56:49,760 --> 00:56:55,200
+it's kind of very close to numpy in a
+
+1230
+00:56:52,400 --> 00:56:57,440
+way like it uses a very num something
+
+1231
+00:56:55,200 --> 00:56:59,960
+that's kind of close to numpy it's very
+
+1232
+00:56:57,440 --> 00:57:02,359
+heavily based on tensors and so because
+
+1233
+00:56:59,960 --> 00:57:04,640
+of this you can kind of easily do some
+
+1234
+00:57:02,359 --> 00:57:06,640
+interesting things like okay I want to
+
+1235
+00:57:04,640 --> 00:57:11,319
+take this tensor and I want to split it
+
+1236
+00:57:06,640 --> 00:57:14,000
+over two gpus um and this is good if
+
+1237
+00:57:11,319 --> 00:57:17,119
+you're training like a very large model
+
+1238
+00:57:14,000 --> 00:57:20,920
+and you want to put kind
+
+1239
+00:57:17,119 --> 00:57:20,920
+of this part of the
+
+1240
+00:57:22,119 --> 00:57:26,520
+model uh you want to put this part of
+
+1241
+00:57:24,119 --> 00:57:30,079
+the model on GP 1 this on gpu2 this on
+
+1242
+00:57:26,520 --> 00:57:31,599
+GPU 3 this on GPU it's slightly simpler
+
+1243
+00:57:30,079 --> 00:57:34,400
+conceptually to do in Jacks but it's
+
+1244
+00:57:31,599 --> 00:57:37,160
+also possible to do in
+
+1245
+00:57:34,400 --> 00:57:39,119
+p and pytorch by far has the most
+
+1246
+00:57:37,160 --> 00:57:41,640
+vibrant ecosystem so like as I said
+
+1247
+00:57:39,119 --> 00:57:44,200
+pytorch is a good default choice but you
+
+1248
+00:57:41,640 --> 00:57:47,480
+can consider using Jack if you uh if you
+
+1249
+00:57:44,200 --> 00:57:47,480
+like new
+
+1250
+00:57:48,079 --> 00:57:55,480
+things cool um yeah actually I already
+
+1251
+00:57:51,599 --> 00:57:58,079
+talked about that so in the interest of
+
+1252
+00:57:55,480 --> 00:58:02,119
+time I may not go into these very deeply
+
+1253
+00:57:58,079 --> 00:58:05,799
+but it's important to note that we have
+
+1254
+00:58:02,119 --> 00:58:05,799
+examples of all of
+
+1255
+00:58:06,920 --> 00:58:12,520
+the models that I talked about in the
+
+1256
+00:58:09,359 --> 00:58:16,720
+class today these are created for
+
+1257
+00:58:12,520 --> 00:58:17,520
+Simplicity not for Speed or efficiency
+
+1258
+00:58:16,720 --> 00:58:20,480
+of
+
+1259
+00:58:17,520 --> 00:58:24,920
+implementation um so these are kind of
+
+1260
+00:58:20,480 --> 00:58:27,760
+torch P torch based uh examples uh where
+
+1261
+00:58:24,920 --> 00:58:31,599
+you can create the bag of words
+
+1262
+00:58:27,760 --> 00:58:36,440
+Model A continuous bag of words
+
+1263
+00:58:31,599 --> 00:58:39,640
+model um and
+
+1264
+00:58:36,440 --> 00:58:41,640
+a deep continuous bag of wordss
+
+1265
+00:58:39,640 --> 00:58:44,359
+model
+
+1266
+00:58:41,640 --> 00:58:46,039
+and all of these I believe are
+
+1267
+00:58:44,359 --> 00:58:48,760
+implemented in
+
+1268
+00:58:46,039 --> 00:58:51,960
+model.py and the most important thing is
+
+1269
+00:58:48,760 --> 00:58:54,960
+where you define the forward pass and
+
+1270
+00:58:51,960 --> 00:58:57,319
+maybe I can just give a a simple example
+
+1271
+00:58:54,960 --> 00:58:58,200
+this but here this is where you do the
+
+1272
+00:58:57,319 --> 00:59:01,839
+word
+
+1273
+00:58:58,200 --> 00:59:04,400
+embedding this is where you sum up all
+
+1274
+00:59:01,839 --> 00:59:08,119
+of the embeddings and add a
+
+1275
+00:59:04,400 --> 00:59:10,200
+bias um and then this is uh where you
+
+1276
+00:59:08,119 --> 00:59:13,960
+return the the
+
+1277
+00:59:10,200 --> 00:59:13,960
+score and then oh
+
+1278
+00:59:14,799 --> 00:59:19,119
+sorry the continuous bag of words model
+
+1279
+00:59:17,520 --> 00:59:22,160
+sums up some
+
+1280
+00:59:19,119 --> 00:59:23,640
+embeddings uh or gets the embeddings
+
+1281
+00:59:22,160 --> 00:59:25,799
+sums up some
+
+1282
+00:59:23,640 --> 00:59:28,079
+embeddings
+
+1283
+00:59:25,799 --> 00:59:30,599
+uh gets the score here and then runs it
+
+1284
+00:59:28,079 --> 00:59:33,200
+through a linear or changes the view
+
+1285
+00:59:30,599 --> 00:59:35,119
+runs it through a linear layer and then
+
+1286
+00:59:33,200 --> 00:59:38,319
+the Deep continuous bag of words model
+
+1287
+00:59:35,119 --> 00:59:41,160
+also adds a few layers of uh like linear
+
+1288
+00:59:38,319 --> 00:59:43,119
+transformations in Dage so you should be
+
+1289
+00:59:41,160 --> 00:59:44,640
+able to see that these correspond pretty
+
+1290
+00:59:43,119 --> 00:59:47,440
+closely to the things that I had on the
+
+1291
+00:59:44,640 --> 00:59:49,280
+slides so um hopefully that's a good
+
+1292
+00:59:47,440 --> 00:59:51,839
+start if you're not very familiar with
+
+1293
+00:59:49,280 --> 00:59:51,839
+implementing
+
+1294
+00:59:53,119 --> 00:59:58,440
+model oh and yes the recitation uh will
+
+1295
+00:59:56,599 --> 00:59:59,799
+be about playing around with sentence
+
+1296
+00:59:58,440 --> 01:00:01,200
+piece and playing around with these so
+
+1297
+00:59:59,799 --> 01:00:02,839
+if you have any look at them have any
+
+1298
+01:00:01,200 --> 01:00:05,000
+questions you're welcome to show up
+
+1299
+01:00:02,839 --> 01:00:09,880
+where I walk
+
+1300
+01:00:05,000 --> 01:00:09,880
+through cool um any any questions about
+
+1301
+01:00:12,839 --> 01:00:19,720
+these okay so a few more final important
+
+1302
+01:00:16,720 --> 01:00:21,720
+Concepts um another concept that you
+
+1303
+01:00:19,720 --> 01:00:25,440
+should definitely be aware of is the
+
+1304
+01:00:21,720 --> 01:00:27,280
+atom Optimizer uh so there's lots of uh
+
+1305
+01:00:25,440 --> 01:00:30,559
+optimizers that you could be using but
+
+1306
+01:00:27,280 --> 01:00:32,200
+almost all research in NLP uses some uh
+
+1307
+01:00:30,559 --> 01:00:38,440
+variety of the atom
+
+1308
+01:00:32,200 --> 01:00:40,839
+Optimizer and the U the way this works
+
+1309
+01:00:38,440 --> 01:00:42,559
+is it
+
+1310
+01:00:40,839 --> 01:00:45,640
+optimizes
+
+1311
+01:00:42,559 --> 01:00:48,480
+the um it optimizes model considering
+
+1312
+01:00:45,640 --> 01:00:49,359
+the rolling average of the gradient and
+
+1313
+01:00:48,480 --> 01:00:53,160
+uh
+
+1314
+01:00:49,359 --> 01:00:55,920
+momentum and the way it works is here we
+
+1315
+01:00:53,160 --> 01:00:58,839
+have a gradient here we have
+
+1316
+01:00:55,920 --> 01:01:04,000
+momentum and what you can see is
+
+1317
+01:00:58,839 --> 01:01:06,680
+happening here is we add a little bit of
+
+1318
+01:01:04,000 --> 01:01:09,200
+the gradient in uh how much you add in
+
+1319
+01:01:06,680 --> 01:01:12,720
+is with respect to the size of this beta
+
+1320
+01:01:09,200 --> 01:01:16,000
+1 parameter and you add it into uh the
+
+1321
+01:01:12,720 --> 01:01:18,640
+momentum term so this momentum term like
+
+1322
+01:01:16,000 --> 01:01:20,440
+gradually increases and decreases so in
+
+1323
+01:01:18,640 --> 01:01:23,440
+contrast to standard gradient percent
+
+1324
+01:01:20,440 --> 01:01:25,839
+which could be
+
+1325
+01:01:23,440 --> 01:01:28,440
+updating
+
+1326
+01:01:25,839 --> 01:01:31,440
+uh each parameter kind of like very
+
+1327
+01:01:28,440 --> 01:01:33,359
+differently on each time step this will
+
+1328
+01:01:31,440 --> 01:01:35,680
+make the momentum kind of transition
+
+1329
+01:01:33,359 --> 01:01:37,240
+more smoothly by taking the rolling
+
+1330
+01:01:35,680 --> 01:01:39,880
+average of the
+
+1331
+01:01:37,240 --> 01:01:43,400
+gradient and then the the second thing
+
+1332
+01:01:39,880 --> 01:01:47,640
+is um by taking the momentum this is the
+
+1333
+01:01:43,400 --> 01:01:51,000
+rolling average of the I guess gradient
+
+1334
+01:01:47,640 --> 01:01:54,440
+uh variance sorry I this should be
+
+1335
+01:01:51,000 --> 01:01:58,079
+variance and the reason why you need
+
+1336
+01:01:54,440 --> 01:02:01,319
+need to keep track of the variance is
+
+1337
+01:01:58,079 --> 01:02:03,319
+some uh some parameters will have very
+
+1338
+01:02:01,319 --> 01:02:06,559
+large variance in their gradients and
+
+1339
+01:02:03,319 --> 01:02:11,480
+might fluctuate very uh strongly and
+
+1340
+01:02:06,559 --> 01:02:13,039
+others might have a smaller uh chain
+
+1341
+01:02:11,480 --> 01:02:15,240
+variant in their gradients and not
+
+1342
+01:02:13,039 --> 01:02:18,240
+fluctuate very much but we want to make
+
+1343
+01:02:15,240 --> 01:02:20,200
+sure that we update the ones we still
+
+1344
+01:02:18,240 --> 01:02:22,240
+update the ones that have a very small
+
+1345
+01:02:20,200 --> 01:02:25,760
+uh change of their variance and the
+
+1346
+01:02:22,240 --> 01:02:27,440
+reason why is kind of let's say you have
+
+1347
+01:02:25,760 --> 01:02:30,440
+a
+
+1348
+01:02:27,440 --> 01:02:30,440
+multi-layer
+
+1349
+01:02:32,480 --> 01:02:38,720
+network
+
+1350
+01:02:34,480 --> 01:02:41,240
+um or actually sorry a better
+
+1351
+01:02:38,720 --> 01:02:44,319
+um a better example is like let's say we
+
+1352
+01:02:41,240 --> 01:02:47,559
+have a big word embedding Matrix and
+
+1353
+01:02:44,319 --> 01:02:53,359
+over here we have like really frequent
+
+1354
+01:02:47,559 --> 01:02:56,279
+words and then over here we have uh
+
+1355
+01:02:53,359 --> 01:02:59,319
+gradi
+
+1356
+01:02:56,279 --> 01:03:00,880
+no we have like less frequent words we
+
+1357
+01:02:59,319 --> 01:03:02,799
+want to make sure that all of these get
+
+1358
+01:03:00,880 --> 01:03:06,160
+updated appropriately all of these get
+
+1359
+01:03:02,799 --> 01:03:08,640
+like enough updates and so over here
+
+1360
+01:03:06,160 --> 01:03:10,760
+this one will have lots of updates and
+
+1361
+01:03:08,640 --> 01:03:13,680
+so uh kind of
+
+1362
+01:03:10,760 --> 01:03:16,599
+the amount that we
+
+1363
+01:03:13,680 --> 01:03:20,039
+update or the the amount that we update
+
+1364
+01:03:16,599 --> 01:03:21,799
+the uh this will be relatively large
+
+1365
+01:03:20,039 --> 01:03:23,119
+whereas over here this will not have
+
+1366
+01:03:21,799 --> 01:03:24,880
+very many updates we'll have lots of
+
+1367
+01:03:23,119 --> 01:03:26,480
+zero updates also
+
+1368
+01:03:24,880 --> 01:03:29,160
+and so the amount that we update this
+
+1369
+01:03:26,480 --> 01:03:32,520
+will be relatively small and so this
+
+1370
+01:03:29,160 --> 01:03:36,119
+kind of squared to gradient here will uh
+
+1371
+01:03:32,520 --> 01:03:38,400
+be smaller for the values over here and
+
+1372
+01:03:36,119 --> 01:03:41,359
+what that allows us to do is it allows
+
+1373
+01:03:38,400 --> 01:03:44,200
+us to maybe I can just go to the bottom
+
+1374
+01:03:41,359 --> 01:03:46,039
+we end up uh dividing by the square root
+
+1375
+01:03:44,200 --> 01:03:47,599
+of this and because we divide by the
+
+1376
+01:03:46,039 --> 01:03:51,000
+square root of this if this is really
+
+1377
+01:03:47,599 --> 01:03:55,680
+large like 50 and 70 and then this over
+
+1378
+01:03:51,000 --> 01:03:59,480
+here is like one 0.5
+
+1379
+01:03:55,680 --> 01:04:01,920
+uh or something we will be upgrading the
+
+1380
+01:03:59,480 --> 01:04:03,920
+ones that have like less Square
+
+1381
+01:04:01,920 --> 01:04:06,880
+gradients so it will it allows you to
+
+1382
+01:04:03,920 --> 01:04:08,760
+upweight the less common gradients more
+
+1383
+01:04:06,880 --> 01:04:10,440
+frequently and then there's also some
+
+1384
+01:04:08,760 --> 01:04:13,400
+terms for correcting bias early in
+
+1385
+01:04:10,440 --> 01:04:16,440
+training because these momentum in uh in
+
+1386
+01:04:13,400 --> 01:04:19,559
+variance or momentum in squared gradient
+
+1387
+01:04:16,440 --> 01:04:23,119
+terms are not going to be like well
+
+1388
+01:04:19,559 --> 01:04:24,839
+calibrated yet so it prevents them from
+
+1389
+01:04:23,119 --> 01:04:28,880
+going very three wire beginning of
+
+1390
+01:04:24,839 --> 01:04:30,839
+training so this is uh the details of
+
+1391
+01:04:28,880 --> 01:04:33,640
+this again are not like super super
+
+1392
+01:04:30,839 --> 01:04:37,359
+important um another thing that I didn't
+
+1393
+01:04:33,640 --> 01:04:40,200
+write on the slides is uh now in
+
+1394
+01:04:37,359 --> 01:04:43,920
+Transformers it's also super common to
+
+1395
+01:04:40,200 --> 01:04:47,400
+have an overall learning rate schle so
+
+1396
+01:04:43,920 --> 01:04:50,520
+even um Even Adam has this uh Ada
+
+1397
+01:04:47,400 --> 01:04:53,440
+learning rate parameter here and we what
+
+1398
+01:04:50,520 --> 01:04:55,240
+we often do is we adjust this so we
+
+1399
+01:04:53,440 --> 01:04:57,839
+start at low
+
+1400
+01:04:55,240 --> 01:04:59,640
+we raise it up and then we have a Decay
+
+1401
+01:04:57,839 --> 01:05:03,039
+uh at the end and exactly how much you
+
+1402
+01:04:59,640 --> 01:05:04,440
+do this kind of depends on um you know
+
+1403
+01:05:03,039 --> 01:05:06,160
+how big your model is how much data
+
+1404
+01:05:04,440 --> 01:05:09,160
+you're tring on eventually and the
+
+1405
+01:05:06,160 --> 01:05:12,440
+reason why we do this is transformers
+
+1406
+01:05:09,160 --> 01:05:13,839
+are unfortunately super sensitive to
+
+1407
+01:05:12,440 --> 01:05:15,359
+having a high learning rate right at the
+
+1408
+01:05:13,839 --> 01:05:16,559
+very beginning so if you update them
+
+1409
+01:05:15,359 --> 01:05:17,920
+with a high learning rate right at the
+
+1410
+01:05:16,559 --> 01:05:22,920
+very beginning they go haywire and you
+
+1411
+01:05:17,920 --> 01:05:24,400
+get a really weird model um and but you
+
+1412
+01:05:22,920 --> 01:05:26,760
+want to raise it eventually so your
+
+1413
+01:05:24,400 --> 01:05:28,920
+model is learning appropriately and then
+
+1414
+01:05:26,760 --> 01:05:30,400
+in all stochastic gradient descent no
+
+1415
+01:05:28,920 --> 01:05:31,680
+matter whether you're using atom or
+
+1416
+01:05:30,400 --> 01:05:33,400
+anything else it's a good idea to
+
+1417
+01:05:31,680 --> 01:05:36,200
+gradually decrease the learning rate at
+
+1418
+01:05:33,400 --> 01:05:38,119
+the end to prevent the model from
+
+1419
+01:05:36,200 --> 01:05:40,480
+continuing to fluctuate and getting it
+
+1420
+01:05:38,119 --> 01:05:42,760
+to a stable point that gives you good
+
+1421
+01:05:40,480 --> 01:05:45,559
+accuracy over a large part of data so
+
+1422
+01:05:42,760 --> 01:05:47,480
+this is often included like if you look
+
+1423
+01:05:45,559 --> 01:05:51,000
+at any standard Transformer training
+
+1424
+01:05:47,480 --> 01:05:53,079
+recipe it will have that this so that's
+
+1425
+01:05:51,000 --> 01:05:54,799
+kind of the the go-to
+
+1426
+01:05:53,079 --> 01:05:58,960
+optimizer
+
+1427
+01:05:54,799 --> 01:06:01,039
+um are there any questions or
+
+1428
+01:05:58,960 --> 01:06:02,599
+discussion there's also tricky things
+
+1429
+01:06:01,039 --> 01:06:04,000
+like cyclic learning rates where you
+
+1430
+01:06:02,599 --> 01:06:06,599
+decrease the learning rate increase it
+
+1431
+01:06:04,000 --> 01:06:08,559
+and stuff like that but I won't go into
+
+1432
+01:06:06,599 --> 01:06:11,000
+that and don't actually use it that
+
+1433
+01:06:08,559 --> 01:06:12,760
+much second thing is visualization of
+
+1434
+01:06:11,000 --> 01:06:15,400
+embeddings so normally when we have word
+
+1435
+01:06:12,760 --> 01:06:19,760
+embeddings usually they're kind of large
+
+1436
+01:06:15,400 --> 01:06:21,559
+um and they can be like 512 or 1024
+
+1437
+01:06:19,760 --> 01:06:25,079
+dimensions
+
+1438
+01:06:21,559 --> 01:06:28,720
+and so one thing that we can do is we
+
+1439
+01:06:25,079 --> 01:06:31,079
+can down weight them or sorry down uh
+
+1440
+01:06:28,720 --> 01:06:34,400
+like reduce the dimensions or perform
+
+1441
+01:06:31,079 --> 01:06:35,880
+dimensionality reduction and put them in
+
+1442
+01:06:34,400 --> 01:06:37,680
+like two or three dimensions which are
+
+1443
+01:06:35,880 --> 01:06:40,200
+easy for humans to
+
+1444
+01:06:37,680 --> 01:06:42,000
+visualize this is an example using
+
+1445
+01:06:40,200 --> 01:06:44,839
+principal component analysis which is a
+
+1446
+01:06:42,000 --> 01:06:48,279
+linear Dimension reduction technique and
+
+1447
+01:06:44,839 --> 01:06:50,680
+this is uh an example from 10 years ago
+
+1448
+01:06:48,279 --> 01:06:52,359
+now uh one of the first major word
+
+1449
+01:06:50,680 --> 01:06:55,240
+embedding papers where they demonstrated
+
+1450
+01:06:52,359 --> 01:06:57,720
+that if you do this sort of linear
+
+1451
+01:06:55,240 --> 01:06:59,440
+Dimension reduction uh you get actually
+
+1452
+01:06:57,720 --> 01:07:01,279
+some interesting things where you can
+
+1453
+01:06:59,440 --> 01:07:03,240
+draw a vector that's almost the same
+
+1454
+01:07:01,279 --> 01:07:06,400
+direction between like countries and
+
+1455
+01:07:03,240 --> 01:07:09,319
+their uh countries and their capitals
+
+1456
+01:07:06,400 --> 01:07:13,720
+for example so this is a good thing to
+
+1457
+01:07:09,319 --> 01:07:16,559
+do but actually PCA uh doesn't give
+
+1458
+01:07:13,720 --> 01:07:20,760
+you in some cases PCA doesn't give you
+
+1459
+01:07:16,559 --> 01:07:22,920
+super great uh visualizations sorry yeah
+
+1460
+01:07:20,760 --> 01:07:25,920
+well for like if it's
+
+1461
+01:07:22,920 --> 01:07:25,920
+like
+
+1462
+01:07:29,880 --> 01:07:35,039
+um for things like this I think you
+
+1463
+01:07:33,119 --> 01:07:37,359
+probably would still see vectors in the
+
+1464
+01:07:35,039 --> 01:07:38,760
+same direction but I don't think it like
+
+1465
+01:07:37,359 --> 01:07:40,920
+there's a reason why I'm introducing
+
+1466
+01:07:38,760 --> 01:07:44,279
+nonlinear projections next because the
+
+1467
+01:07:40,920 --> 01:07:46,799
+more standard way to do this is uh
+
+1468
+01:07:44,279 --> 01:07:50,640
+nonlinear projections in in particular a
+
+1469
+01:07:46,799 --> 01:07:54,880
+method called tisne and the way um they
+
+1470
+01:07:50,640 --> 01:07:56,880
+do this is they try to group
+
+1471
+01:07:54,880 --> 01:07:59,000
+things that are close together in high
+
+1472
+01:07:56,880 --> 01:08:01,240
+dimensional space so that they're also
+
+1473
+01:07:59,000 --> 01:08:04,440
+close together in low dimensional space
+
+1474
+01:08:01,240 --> 01:08:08,520
+but they remove the Restriction that
+
+1475
+01:08:04,440 --> 01:08:10,799
+this is uh that this is linear so this
+
+1476
+01:08:08,520 --> 01:08:15,480
+is an example of just grouping together
+
+1477
+01:08:10,799 --> 01:08:18,040
+some digits uh from the memus data
+
+1478
+01:08:15,480 --> 01:08:20,279
+set or sorry reducing the dimension of
+
+1479
+01:08:18,040 --> 01:08:23,640
+digits from the mest data
+
+1480
+01:08:20,279 --> 01:08:25,640
+set according to PCA and you can see it
+
+1481
+01:08:23,640 --> 01:08:28,000
+gives these kind of blobs that overlap
+
+1482
+01:08:25,640 --> 01:08:29,799
+with each other and stuff like this but
+
+1483
+01:08:28,000 --> 01:08:31,679
+if you do it with tney this is
+
+1484
+01:08:29,799 --> 01:08:34,799
+completely unsupervised actually it's
+
+1485
+01:08:31,679 --> 01:08:37,080
+not training any model for labeling the
+
+1486
+01:08:34,799 --> 01:08:39,239
+labels are just used to draw the colors
+
+1487
+01:08:37,080 --> 01:08:42,520
+and you can see that it gets pretty
+
+1488
+01:08:39,239 --> 01:08:44,520
+coherent um clusters that correspond to
+
+1489
+01:08:42,520 --> 01:08:48,120
+like what the actual digits
+
+1490
+01:08:44,520 --> 01:08:50,120
+are um however uh one problem with
+
+1491
+01:08:48,120 --> 01:08:53,159
+titney I I still think it's better than
+
+1492
+01:08:50,120 --> 01:08:55,000
+PCA for a large number of uh
+
+1493
+01:08:53,159 --> 01:08:59,199
+applications
+
+1494
+01:08:55,000 --> 01:09:01,040
+but settings of tisy matter and tisy has
+
+1495
+01:08:59,199 --> 01:09:02,920
+a few settings kind of the most
+
+1496
+01:09:01,040 --> 01:09:04,120
+important ones are the overall
+
+1497
+01:09:02,920 --> 01:09:06,560
+perplexity
+
+1498
+01:09:04,120 --> 01:09:09,040
+hyperparameter and uh the number of
+
+1499
+01:09:06,560 --> 01:09:12,319
+steps that you perform and there's a
+
+1500
+01:09:09,040 --> 01:09:14,920
+nice example uh of a paper or kind of
+
+1501
+01:09:12,319 --> 01:09:16,359
+like online post uh that demonstrates
+
+1502
+01:09:14,920 --> 01:09:18,560
+how if you change these parameters you
+
+1503
+01:09:16,359 --> 01:09:22,279
+can get very different things so if this
+
+1504
+01:09:18,560 --> 01:09:24,080
+is the original data you run tisy and it
+
+1505
+01:09:22,279 --> 01:09:26,640
+gives you very different things based on
+
+1506
+01:09:24,080 --> 01:09:29,279
+the hyper parameters that you change um
+
+1507
+01:09:26,640 --> 01:09:32,880
+and here's another example uh you have
+
+1508
+01:09:29,279 --> 01:09:36,960
+two linear uh things like this and so
+
+1509
+01:09:32,880 --> 01:09:40,839
+PCA no matter how you ran PCA you would
+
+1510
+01:09:36,960 --> 01:09:44,080
+still get a linear output from this so
+
+1511
+01:09:40,839 --> 01:09:45,960
+normally uh you know it might change the
+
+1512
+01:09:44,080 --> 01:09:49,239
+order it might squash it a little bit or
+
+1513
+01:09:45,960 --> 01:09:51,239
+something like this but um if you run
+
+1514
+01:09:49,239 --> 01:09:53,400
+tisy it gives you crazy things it even
+
+1515
+01:09:51,239 --> 01:09:56,040
+gives you like DNA and other stuff like
+
+1516
+01:09:53,400 --> 01:09:58,040
+that so so um you do need to be a little
+
+1517
+01:09:56,040 --> 01:10:00,600
+bit careful that uh this is not
+
+1518
+01:09:58,040 --> 01:10:02,320
+necessarily going to tell you nice
+
+1519
+01:10:00,600 --> 01:10:04,400
+linear correlations like this so like
+
+1520
+01:10:02,320 --> 01:10:06,159
+let's say this correlation existed if
+
+1521
+01:10:04,400 --> 01:10:09,199
+you use tisy it might not necessarily
+
+1522
+01:10:06,159 --> 01:10:09,199
+come out to
+
+1523
+01:10:09,320 --> 01:10:14,880
+TIY
+
+1524
+01:10:11,800 --> 01:10:16,920
+cool yep uh that that's my final thing
+
+1525
+01:10:14,880 --> 01:10:18,520
+actually I talked said sequence models
+
+1526
+01:10:16,920 --> 01:10:19,679
+in the next class but it's in the class
+
+1527
+01:10:18,520 --> 01:10:21,440
+after this I'm going to be talking about
+
+1528
+01:10:19,679 --> 01:10:24,199
+language
+
+1529
+01:10:21,440 --> 01:10:27,159
+modeling uh cool any any questions
+
+1530
+01:10:24,199 --> 01:10:27,159
+or
\ No newline at end of file