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All right. Um, let's get started.
|
All right. Um, let's get started.
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 151.07869
| 27.626228
| 16.266287
| 46.941765
|
slightly fast
|
ɔl ɹaɪt. ʌm, lɛt'ɛs ɡɛt stɑɹtɪd.
|
very noisy
|
moderate reverberation
|
quite monotone
|
there won't be a section, uh, this Friday, okay?
|
there won't be a section, uh, this Friday, okay?
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 114.61998
| 38.79863
| 20.250143
| 57.590553
|
slightly fast
|
ðɛɹ wʌn'ti bi ʌ sɛkʃʌn, ʌ, ðɪs fɹaɪdi, oʊkeɪ?
|
very noisy
|
very confined sounding
|
slightly monotone
|
Um, and then the second part of the algorithm was again,
|
Um, and then the second part of the algorithm was again,
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 95.740906
| 27.820704
| 32.773682
| 37.792564
|
moderate speed
|
ʌm, ʌnd ðɛn ðʌ sɛkʌnd pɑɹt ʌv ðʌ ælɡɜ˞ɪðʌm wɑz ʌɡɛn,
|
very noisy
|
quite roomy sounding
|
quite monotone
|
this is a convex function, this is a convex function.
|
this is a convex function, this is a convex function.
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 99.423248
| 8.325679
| 14.794849
| 59.80439
|
very fast
|
ðɪs ɪz ʌ kʌnvɛks fʌŋkʃʌn, ðɪs ɪz ʌ kʌnvɛks fʌŋkʃʌn.
|
very noisy
|
very confined sounding
|
very monotone
|
Turns out a straight line,
|
Turns out a straight line,
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 117.71151
| 18.102331
| 26.923498
| 31.050282
|
quite fast
|
tɜ˞nz aʊt ʌ stɹeɪt laɪn,
|
very noisy
|
very roomy sounding
|
quite monotone
|
that's also a convex function.
|
that's also a convex function.
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 84.86451
| 16.805794
| 16.351959
| 48.221336
|
quite fast
|
ðæt'ɛs ɔlsoʊ ʌ kʌnvɛks fʌŋkʃʌn.
|
very noisy
|
slightly confined sounding
|
very monotone
|
Uh, but so in this addendum,
|
Uh, but so in this addendum,
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 122.9291
| 27.674416
| 23.589886
| 43.413403
|
moderate speed
|
ʌ, bʌt soʊ ɪn ðɪs ʌdɛndʌm,
|
very noisy
|
moderate reverberation
|
quite monotone
|
we're saying that if f is a strictly convex function,
|
we're saying that if f is a strictly convex function,
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 130.328857
| 33.176041
| 20.344738
| 54.384331
|
very fast
|
wi'ɹeɪ seɪɪŋ ðæt ɪf ɛf ɪz ʌ stɹɪktli kʌnvɛks fʌŋkʃʌn,
|
very noisy
|
quite confined sounding
|
quite monotone
|
meaning basically, you know, it's not a straight line, right?
|
meaning basically, you know, it's not a straight line, right?
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 105.418953
| 27.842913
| 27.497026
| 59.536564
|
very fast
|
minɪŋ beɪsɪkli, ju noʊ, ɪt'ɛs nɑt ʌ stɹeɪt laɪn, ɹaɪt?
|
very noisy
|
very confined sounding
|
quite monotone
|
Uh, and- and a bit more than it's not a straight line.
|
Uh, and- and a bit more than it's not a straight line.
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 114.506989
| 25.301521
| 34.940887
| 59.609764
|
very fast
|
ʌ, ʌnd- ʌnd ʌ bɪt mɔɹ ðæn ɪt'ɛs nɑt ʌ stɹeɪt laɪn.
|
quite noisy
|
very confined sounding
|
quite monotone
|
There's a- but if the curvature,
|
There's a- but if the curvature,
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 109.102585
| 12.887964
| 20.399179
| 59.794968
|
quite fast
|
ðɛɹ'ɛs ʌ- bʌt ɪf ðʌ kɜ˞vʌtʃɜ˞,
|
very noisy
|
very confined sounding
|
very monotone
|
if it's always bending up,
|
if it's always bending up,
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 103.579254
| 12.158989
| 19.127918
| 59.12056
|
slightly slowly
|
ɪf ɪt'ɛs ɔlweɪz bɛndɪŋ ʌp,
|
very noisy
|
very confined sounding
|
very monotone
|
uh, then the only way for the left and right-hand sides to be equal,
|
uh, then the only way for the left and right-hand sides to be equal,
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 115.674667
| 28.160439
| 28.020575
| 59.729652
|
moderate speed
|
ʌ, ðɛn ðʌ oʊnli weɪ fɔɹ ðʌ lɛft ʌnd ɹaɪt-hænd saɪdz tu bi ikwʌl,
|
very noisy
|
very confined sounding
|
quite monotone
|
uh, look at the blue dots, find the mean,
|
uh, look at the blue dots, find the mean,
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 114.025063
| 26.603607
| 22.0091
| 26.896133
|
slightly fast
|
ʌ, lʊk æt ðʌ blu dɑts, faɪnd ðʌ min,
|
very noisy
|
very roomy sounding
|
quite monotone
|
is if x is a constant,
|
is if x is a constant,
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 105.179649
| 16.68531
| 14.779914
| 59.671246
|
moderate speed
|
ɪz ɪf ɛks ɪz ʌ kɑnstʌnt,
|
very noisy
|
very confined sounding
|
very monotone
|
meaning it's a random variable that always takes on the same value.
|
meaning it's a random variable that always takes on the same value.
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 99.076485
| 20.306822
| 23.160994
| 59.312778
|
slightly fast
|
minɪŋ ɪt'ɛs ʌ ɹændʌm vɛɹiʌbʌl ðæt ɔlweɪz teɪks ɑn ðʌ seɪm vælju.
|
very noisy
|
very confined sounding
|
quite monotone
|
Okay? Uh, so Jensen's equality says that, you know,
|
Okay? Uh, so Jensen's equality says that, you know,
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 124.63649
| 40.359505
| 25.974491
| 56.950798
|
moderate speed
|
oʊkeɪ? ʌ, soʊ dʒɛnsʌn'ɛs ɪkwɑlʌti sɛz ðæt, ju noʊ,
|
very noisy
|
very confined sounding
|
slightly monotone
|
um, uh, left-hand side is gonna be the same as right-hand side.
|
um, uh, left-hand side is gonna be the same as right-hand side.
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 107.088821
| 11.073024
| 24.580797
| 59.5228
|
slightly fast
|
ʌm, ʌ, lɛft-hænd saɪd ɪz ɡɑnʌ bi ðʌ seɪm æz ɹaɪt-hænd saɪd.
|
very noisy
|
very confined sounding
|
very monotone
|
Sorry, I think I- I reversed the order of these two for that equation,
|
Sorry, I think I- I reversed the order of these two for that equation,
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 104.051552
| 24.429287
| 23.336163
| 57.563309
|
very fast
|
sɑɹi, aɪ θɪŋk aɪ- aɪ ɹɪvɜ˞st ðʌ ɔɹdɜ˞ ʌv ðiz tu fɔɹ ðæt ɪkweɪʒʌn,
|
very noisy
|
very confined sounding
|
quite monotone
|
but it doesn't matter, right?
|
but it doesn't matter, right?
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 82.879112
| 8.298046
| 20.577389
| 57.170666
|
very fast
|
bʌt ɪt 'ti mætɜ˞, ɹaɪt?
|
very noisy
|
very confined sounding
|
very monotone
|
But so Jensen's equality says left-hand side is always less than equal to the right-hand side,
|
But so Jensen's equality says left-hand side is always less than equal to the right-hand side,
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 134.819946
| 31.511347
| 30.072779
| 59.697399
|
very fast
|
bʌt soʊ dʒɛnsʌn'ɛs ɪkwɑlʌti sɛz lɛft-hænd saɪd ɪz ɔlweɪz lɛs ðæn ikwʌl tu ðʌ ɹaɪt-hænd saɪd,
|
very noisy
|
very confined sounding
|
quite monotone
|
and the only way it's equal is if x,
|
and the only way it's equal is if x,
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 125.713203
| 22.887913
| 26.072083
| 58.053764
|
moderate speed
|
ʌnd ðʌ oʊnli weɪ ɪt'ɛs ikwʌl ɪz ɪf ɛks,
|
very noisy
|
very confined sounding
|
quite monotone
|
you know, is a random variable that always takes on the same value.
|
you know, is a random variable that always takes on the same value.
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 105.390984
| 27.479958
| 26.086296
| 33.676498
|
slightly fast
|
ju noʊ, ɪz ʌ ɹændʌm vɛɹiʌbʌl ðæt ɔlweɪz teɪks ɑn ðʌ seɪm vælju.
|
very noisy
|
very roomy sounding
|
quite monotone
|
Okay? Yeah.
|
Okay? Yeah.
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 94.216156
| 42.808464
| 3.365608
| 59.622604
|
very fast
|
oʊkeɪ? jæ.
|
very noisy
|
very confined sounding
|
slightly monotone
|
look at the red dots, find the mean,
|
look at the red dots, find the mean,
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 107.278252
| 17.9632
| 15.128516
| 31.557333
|
very fast
|
lʊk æt ðʌ ɹɛd dɑts, faɪnd ðʌ min,
|
very noisy
|
very roomy sounding
|
quite monotone
|
What if- what if, uh,
|
What if- what if, uh,
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 198.758499
| 76.238586
| 10.711509
| 16.113033
|
very fast
|
wʌt ɪf- wʌt ɪf, ʌ,
|
very noisy
|
very roomy sounding
|
slightly expressive
|
the value of f of 1 was equal to the value of f of 3?
|
the value of f of 1 was equal to the value of f of 3?
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 122.461555
| 21.993139
| 5.749332
| 9.61562
|
moderate speed
|
ðʌ vælju ʌv ɛf ʌv wɑz ikwʌl tu ðʌ vælju ʌv ɛf ʌv ?
|
very noisy
|
very roomy sounding
|
quite monotone
|
What's that?
|
What's that?
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 117.657692
| 2.414048
| 7.071605
| 55.641602
|
quite slowly
|
wʌt'ɛs ðæt?
|
very noisy
|
quite confined sounding
|
very monotone
|
Yeah. So it turns out,
|
Yeah. So it turns out,
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 139.378845
| 26.682171
| 25.827139
| 57.909473
|
moderate speed
|
jæ. soʊ ɪt tɜ˞nz aʊt,
|
very noisy
|
very confined sounding
|
quite monotone
|
what if the value of f of 1 is equal to the value of f of 3?
|
what if the value of f of 1 is equal to the value of f of 3?
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 113.284737
| 25.800838
| 24.801716
| 40.240215
|
very fast
|
wʌt ɪf ðʌ vælju ʌv ɛf ʌv ɪz ikwʌl tu ðʌ vælju ʌv ɛf ʌv ?
|
very noisy
|
slightly roomy sounding
|
quite monotone
|
It turns out it does.
|
It turns out it does.
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 96.40464
| 23.739313
| 17.983
| 39.220028
|
moderate speed
|
ɪt tɜ˞nz aʊt ɪt dʌz.
|
very noisy
|
slightly roomy sounding
|
quite monotone
|
Very, uh, so let's see.
|
Very, uh, so let's see.
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 127.442299
| 14.272443
| 23.439531
| 33.694077
|
moderate speed
|
vɛɹi, ʌ, soʊ lɛt'ɛs si.
|
very noisy
|
very roomy sounding
|
very monotone
|
So one way that could happen would be if the function were like that,
|
So one way that could happen would be if the function were like that,
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 117.368317
| 28.476231
| 14.923311
| 45.174026
|
moderate speed
|
soʊ wʌn weɪ ðæt kʊd hæpʌn wʊd bi ɪf ðʌ fʌŋkʃʌn wɜ˞ laɪk ðæt,
|
very noisy
|
moderate reverberation
|
quite monotone
|
and then if you take the droll and chord,
|
and then if you take the droll and chord,
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 108.630608
| 15.027569
| 18.705677
| 58.923233
|
quite fast
|
ʌnd ðɛn ɪf ju teɪk ðʌ dɹoʊl ʌnd kɔɹd,
|
very noisy
|
very confined sounding
|
very monotone
|
take the mean is still higher.
|
take the mean is still higher.
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 99.215996
| 15.749424
| 17.897211
| 42.75153
|
slightly fast
|
teɪk ðʌ min ɪz stɪl haɪɜ˞.
|
very noisy
|
slightly roomy sounding
|
very monotone
|
and then move the cluster centroids over,
|
and then move the cluster centroids over,
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 96.884956
| 12.769713
| 21.484674
| 59.609314
|
slightly slowly
|
ʌnd ðɛn muv ðʌ klʌstɜ˞ oʊvɜ˞,
|
very noisy
|
very confined sounding
|
very monotone
|
I mean, it's like if the point of f of 1,
|
I mean, it's like if the point of f of 1,
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 122.526047
| 4.622565
| 9.781054
| 7.863521
|
moderate speed
|
aɪ min, ɪt'ɛs laɪk ɪf ðʌ pɔɪnt ʌv ɛf ʌv ,
|
very noisy
|
very roomy sounding
|
very monotone
|
which, for example,
|
which, for example,
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 112.888687
| 3.495117
| 6.419312
| 10.293677
|
slightly fast
|
wɪtʃ, fɔɹ ɪɡzæmpʌl,
|
very noisy
|
very roomy sounding
|
very monotone
|
.
|
.
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 111.034546
| 6.052987
| null | null |
very fast
|
.
|
very clear
|
very confined sounding
|
very monotone
|
Then it's impor- if- if f of- if- if- if you had a flat part here,
|
Then it's impor- if- if f of- if- if- if you had a flat part here,
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 129.30162
| 33.84869
| 13.749016
| 54.803352
|
slightly fast
|
ðɛn ɪt'ɛs - ɪf- ɪf ɛf ʌv- ɪf- ɪf- ɪf ju hæd ʌ flæt pɑɹt hiɹ,
|
very noisy
|
quite confined sounding
|
quite monotone
|
then the function is not strictly convex.
|
then the function is not strictly convex.
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 99.915474
| 17.920065
| 20.903069
| 59.355587
|
quite fast
|
ðɛn ðʌ fʌŋkʃʌn ɪz nɑt stɹɪktli kʌnvɛks.
|
very noisy
|
very confined sounding
|
quite monotone
|
And so it's still less than equal to,
|
And so it's still less than equal to,
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 108.801628
| 16.869116
| 21.334211
| 59.661388
|
slightly fast
|
ʌnd soʊ ɪt'ɛs stɪl lɛs ðæn ikwʌl tu,
|
very noisy
|
very confined sounding
|
very monotone
|
but it's not- but it can't be equal to if x is random.
|
but it's not- but it can't be equal to if x is random.
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 87.942688
| 11.887512
| 13.945253
| 59.551311
|
quite fast
|
bʌt ɪt'ɛs nɑt- bʌt ɪt kæn'ti bi ikwʌl tu ɪf ɛks ɪz ɹændʌm.
|
very noisy
|
very confined sounding
|
very monotone
|
Okay? So, um, and- and- and we'll use this,
|
Okay? So, um, and- and- and we'll use this,
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 114.948059
| 40.463715
| 18.917381
| 56.327686
|
moderate speed
|
oʊkeɪ? soʊ, ʌm, ʌnd- ʌnd- ʌnd wi' jus ðɪs,
|
very noisy
|
very confined sounding
|
slightly monotone
|
uh, in a little bit.
|
uh, in a little bit.
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 102.676575
| 17.043634
| 20.969772
| 23.507589
|
slightly fast
|
ʌ, ɪn ʌ lɪtʌl bɪt.
|
very noisy
|
very roomy sounding
|
very monotone
|
We'll- we'll actually end up using this.
|
We'll- we'll actually end up using this.
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 109.128983
| 31.1597
| 33.173496
| 59.820164
|
quite fast
|
wi'- wi' æktʃuʌli ɛnd ʌp juzɪŋ ðɪs.
|
very noisy
|
very confined sounding
|
quite monotone
|
excuse me, uh, to that mean.
|
excuse me, uh, to that mean.
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 91.072227
| 14.199455
| 11.640121
| 59.871483
|
slightly slowly
|
ɪkskjus mi, ʌ, tu ðæt min.
|
very noisy
|
very confined sounding
|
very monotone
|
Um, and, uh, again,
|
Um, and, uh, again,
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 124.239532
| 23.735489
| 15.788633
| 58.449867
|
quite slowly
|
ʌm, ʌnd, ʌ, ʌɡɛn,
|
very noisy
|
very confined sounding
|
quite monotone
|
for the strict probabilistic, you know,
|
for the strict probabilistic, you know,
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 129.748489
| 21.89114
| 28.308517
| 50.610458
|
very fast
|
fɔɹ ðʌ stɹɪkt pɹɑbʌbɪlɪstʌk, ju noʊ,
|
very noisy
|
slightly confined sounding
|
quite monotone
|
those of you that, I don't know,
|
those of you that, I don't know,
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 116.675926
| 28.409184
| 24.470964
| 43.401272
|
very fast
|
ðoʊz ʌv ju ðæt, aɪ dɑn'ti noʊ,
|
very noisy
|
moderate reverberation
|
quite monotone
|
take classes in advanced probability,
|
take classes in advanced probability,
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 105.452385
| 20.656923
| 28.499384
| 59.245499
|
quite fast
|
teɪk klæsʌz ɪn ʌdvænst pɹɑbʌbɪlʌti,
|
very noisy
|
very confined sounding
|
quite monotone
|
the- the technical way of saying x is a constant is,
|
the- the technical way of saying x is a constant is,
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 116.558067
| 29.466192
| 27.656233
| 57.432907
|
quite fast
|
ðʌ- ðʌ tɛknɪkʌl weɪ ʌv seɪɪŋ ɛks ɪz ʌ kɑnstʌnt ɪz,
|
very noisy
|
very confined sounding
|
quite monotone
|
uh, x is, um,
|
uh, x is, um,
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 121.822021
| 28.660778
| 5.996393
| 58.043816
|
quite slowly
|
ʌ, ɛks ɪz, ʌm,
|
very noisy
|
very confined sounding
|
quite monotone
|
equal to e x with probability one.
|
equal to e x with probability one.
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 103.334999
| 15.064314
| 7.293429
| 45.582806
|
slightly slowly
|
ikwʌl tu i ɛks wɪð pɹɑbʌbɪlʌti wʌn.
|
very noisy
|
moderate reverberation
|
very monotone
|
You know what? I think for all practical human purposes,
|
You know what? I think for all practical human purposes,
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 122.156448
| 25.62644
| 14.712461
| 59.778728
|
very fast
|
ju noʊ wʌt? aɪ θɪŋk fɔɹ ɔl pɹæktʌkʌl hjumʌn pɜ˞pʌsʌz,
|
very noisy
|
very confined sounding
|
quite monotone
|
you do not need to worry about this.
|
you do not need to worry about this.
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 91.413841
| 9.552069
| 36.769402
| 59.634892
|
very fast
|
ju du nɑt nid tu wɜ˞i ʌbaʊt ðɪs.
|
quite noisy
|
very confined sounding
|
very monotone
|
But I think if you- if you take a class in measure theory,
|
But I think if you- if you take a class in measure theory,
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 121.992386
| 27.804337
| 6.414793
| 54.778618
|
quite fast
|
bʌt aɪ θɪŋk ɪf ju- ɪf ju teɪk ʌ klæs ɪn mɛʒɜ˞ θɪɹi,
|
very noisy
|
quite confined sounding
|
quite monotone
|
Okay? Um, and so, uh,
|
Okay? Um, and so, uh,
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 114.976692
| 27.819569
| 29.392963
| 59.218681
|
quite slowly
|
oʊkeɪ? ʌm, ʌnd soʊ, ʌ,
|
very noisy
|
very confined sounding
|
quite monotone
|
the professor of measure theory will be happy if you say this and you say x is a constant.
|
the professor of measure theory will be happy if you say this and you say x is a constant.
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 96.694794
| 16.004807
| 17.258598
| 48.971844
|
very fast
|
ðʌ pɹʌfɛsɜ˞ ʌv mɛʒɜ˞ θɪɹi wɪl bi hæpi ɪf ju seɪ ðɪs ʌnd ju seɪ ɛks ɪz ʌ kɑnstʌnt.
|
very noisy
|
slightly confined sounding
|
very monotone
|
But maybe- maybe none of you are.
|
But maybe- maybe none of you are.
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 109.352097
| 22.952515
| 11.881605
| 15.373141
|
quite fast
|
bʌt meɪbi- meɪbi nʌn ʌv ju ɑɹ.
|
very noisy
|
very roomy sounding
|
quite monotone
|
Okay. Yes. Don't worry about it.
|
Okay. Yes. Don't worry about it.
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 83.819923
| 20.956263
| 11.810925
| 53.144257
|
very fast
|
oʊkeɪ. jɛs. dɑn'ti wɜ˞i ʌbaʊt ɪt.
|
very noisy
|
quite confined sounding
|
quite monotone
|
Um, oh, yes.
|
Um, oh, yes.
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 103.075211
| 37.097839
| 8.673595
| 53.73024
|
very slowly
|
ʌm, oʊ, jɛs.
|
very noisy
|
quite confined sounding
|
slightly monotone
|
Okay. Now, um, just one- one more addendum,
|
Okay. Now, um, just one- one more addendum,
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 138.28241
| 33.483135
| 20.200401
| 45.688606
|
moderate speed
|
oʊkeɪ. naʊ, ʌm, dʒʌst wʌn- wʌn mɔɹ ʌdɛndʌm,
|
very noisy
|
moderate reverberation
|
quite monotone
|
um, uh, to this is that,
|
um, uh, to this is that,
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 108.203102
| 11.837308
| 14.631748
| 29.347139
|
quite slowly
|
ʌm, ʌ, tu ðɪs ɪz ðæt,
|
very noisy
|
very roomy sounding
|
very monotone
|
um, the form of Jensen's equality we're gonna use is actually a form for a concave function.
|
um, the form of Jensen's equality we're gonna use is actually a form for a concave function.
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 117.306396
| 27.654591
| 23.664219
| 41.626904
|
quite fast
|
ʌm, ðʌ fɔɹm ʌv dʒɛnsʌn'ɛs ɪkwɑlʌti wi'ɹeɪ ɡɑnʌ jus ɪz æktʃuʌli ʌ fɔɹm fɔɹ ʌ kɑnkeɪv fʌŋkʃʌn.
|
very noisy
|
slightly roomy sounding
|
quite monotone
|
So instead of convex, um,
|
So instead of convex, um,
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 124.881363
| 13.911606
| 15.782792
| 55.095413
|
moderate speed
|
soʊ ɪnstɛd ʌv kʌnvɛks, ʌm,
|
very noisy
|
quite confined sounding
|
very monotone
|
I'm gonna say concave.
|
I'm gonna say concave.
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 120.547348
| 26.59157
| 13.983693
| 58.022144
|
moderate speed
|
aɪ'ɛm ɡɑnʌ seɪ kɑnkeɪv.
|
very noisy
|
very confined sounding
|
quite monotone
|
And so, you know,
|
And so, you know,
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 116.284134
| 8.205198
| 3.520383
| 59.762321
|
slightly slowly
|
ʌnd soʊ, ju noʊ,
|
very noisy
|
very confined sounding
|
very monotone
|
and it turns out if you keep running the algorithm, nothing changes.
|
and it turns out if you keep running the algorithm, nothing changes.
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 117.172752
| 27.558041
| 31.305824
| 59.392815
|
very fast
|
ʌnd ɪt tɜ˞nz aʊt ɪf ju kip ɹʌnɪŋ ðʌ ælɡɜ˞ɪðʌm, nʌθɪŋ tʃeɪndʒʌz.
|
very noisy
|
very confined sounding
|
quite monotone
|
a concave function is just a negative of a convex function, right?
|
a concave function is just a negative of a convex function, right?
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 105.255859
| 24.522535
| 18.486025
| 59.7444
|
quite fast
|
ʌ kɑnkeɪv fʌŋkʃʌn ɪz dʒʌst ʌ nɛɡʌtɪv ʌv ʌ kʌnvɛks fʌŋkʃʌn, ɹaɪt?
|
very noisy
|
very confined sounding
|
quite monotone
|
If you take a convex function and take negative of that,
|
If you take a convex function and take negative of that,
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 109.362549
| 18.163364
| 39.148342
| 27.523809
|
very fast
|
ɪf ju teɪk ʌ kʌnvɛks fʌŋkʃʌn ʌnd teɪk nɛɡʌtɪv ʌv ðæt,
|
quite noisy
|
very roomy sounding
|
quite monotone
|
it becomes concave.
|
it becomes concave.
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 118.591309
| 26.292986
| 31.091536
| 57.86079
|
moderate speed
|
ɪt bɪkʌmz kɑnkeɪv.
|
very noisy
|
very confined sounding
|
quite monotone
|
And so, uh, the whole thing works with the- with everything flipped around the other way.
|
And so, uh, the whole thing works with the- with everything flipped around the other way.
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 106.036224
| 27.441326
| 16.427866
| 54.438534
|
slightly fast
|
ʌnd soʊ, ʌ, ðʌ hoʊl θɪŋ wɜ˞ks wɪð ðʌ- wɪð ɛvɹiθɪŋ flɪpt ɜ˞aʊnd ðʌ ʌðɜ˞ weɪ.
|
very noisy
|
quite confined sounding
|
quite monotone
|
Okay? Yeah. Yep.
|
Okay? Yeah. Yep.
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 97.778572
| 33.033554
| null | null |
very fast
|
oʊkeɪ? jæ. jɛp.
|
very clear
|
very confined sounding
|
quite monotone
|
And so, uh, I have this strictly concave.
|
And so, uh, I have this strictly concave.
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 85.403267
| 16.994059
| 2.662857
| 43.846939
|
slightly slowly
|
ʌnd soʊ, ʌ, aɪ hæv ðɪs stɹɪktli kɑnkeɪv.
|
very noisy
|
moderate reverberation
|
very monotone
|
Okay? So the form of Jensen's equality we're gonna use is actually the,
|
Okay? So the form of Jensen's equality we're gonna use is actually the,
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 102.624741
| 34.803833
| 20.44162
| 57.362869
|
quite fast
|
oʊkeɪ? soʊ ðʌ fɔɹm ʌv dʒɛnsʌn'ɛs ɪkwɑlʌti wi'ɹeɪ ɡɑnʌ jus ɪz æktʃuʌli ðʌ,
|
very noisy
|
very confined sounding
|
quite monotone
|
um, uh, concave form of Jensen's equality.
|
um, uh, concave form of Jensen's equality.
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 112.212219
| 16.716595
| 36.716404
| 52.637074
|
moderate speed
|
ʌm, ʌ, kɑnkeɪv fɔɹm ʌv dʒɛnsʌn'ɛs ɪkwɑlʌti.
|
quite noisy
|
quite confined sounding
|
very monotone
|
And we're actually going to apply it to the log function.
|
And we're actually going to apply it to the log function.
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 106.11515
| 15.552091
| 36.795769
| 59.758327
|
quite fast
|
ʌnd wi'ɹeɪ æktʃuʌli ɡoʊɪŋ tu ʌplaɪ ɪt tu ðʌ lɔɡ fʌŋkʃʌn.
|
quite noisy
|
very confined sounding
|
very monotone
|
So the log function.
|
So the log function.
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 94.561607
| 6.983719
| 47.859108
| 59.928463
|
very fast
|
soʊ ðʌ lɔɡ fʌŋkʃʌn.
|
moderate ambient sound
|
very confined sounding
|
very monotone
|
So the algorithm has converged.
|
So the algorithm has converged.
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 100.211983
| 19.778416
| 33.650791
| 57.671642
|
very fast
|
soʊ ðʌ ælɡɜ˞ɪðʌm hæz kʌnvɜ˞dʒd.
|
very noisy
|
very confined sounding
|
quite monotone
|
Right? Log X looks like this.
|
Right? Log X looks like this.
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 93.956146
| 22.122108
| 24.487562
| 48.811329
|
quite fast
|
ɹaɪt? lɔɡ ɛks lʊks laɪk ðɪs.
|
very noisy
|
slightly confined sounding
|
quite monotone
|
And so that's a concave function.
|
And so that's a concave function.
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 74.796211
| 12.040864
| 14.83131
| 49.758339
|
quite fast
|
ʌnd soʊ ðæt'ɛs ʌ kɑnkeɪv fʌŋkʃʌn.
|
very noisy
|
slightly confined sounding
|
very monotone
|
And so the inequality we'll use will be in this direction that I have in orange.
|
And so the inequality we'll use will be in this direction that I have in orange.
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 91.474304
| 14.43178
| 12.965574
| 53.738617
|
quite fast
|
ʌnd soʊ ðʌ ɪnɪkwɑlʌti wi' jus wɪl bi ɪn ðɪs dɜ˞ɛkʃʌn ðæt aɪ hæv ɪn ɔɹʌndʒ.
|
very noisy
|
quite confined sounding
|
very monotone
|
All right.
|
All right.
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 121.820496
| 54.805389
| null | null |
very fast
|
ɔl ɹaɪt.
|
very clear
|
very confined sounding
|
moderate intonation
|
Just a moment.
|
Just a moment.
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 121.118546
| 49.894569
| null | null |
very fast
|
dʒʌst ʌ moʊmʌnt.
|
very clear
|
very confined sounding
|
slightly monotone
|
So here's the density estimation problem.
|
So here's the density estimation problem.
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 86.850967
| 37.410561
| 9.613345
| 57.577263
|
very fast
|
soʊ hiɹ'ɛs ðʌ dɛnsʌti ɛstʌmeɪʃʌn pɹɑblʌm.
|
very noisy
|
very confined sounding
|
slightly monotone
|
Um, meaning density estimation means you want to estimate P of X.
|
Um, meaning density estimation means you want to estimate P of X.
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 103.956871
| 25.70826
| 24.910009
| 57.305866
|
quite fast
|
ʌm, minɪŋ dɛnsʌti ɛstʌmeɪʃʌn minz ju wɑnt tu ɛstʌmʌt pi ʌv ɛks.
|
very noisy
|
very confined sounding
|
quite monotone
|
Right? So we have a model for P of X comma Z,
|
Right? So we have a model for P of X comma Z,
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 98.034584
| 39.879745
| 11.032364
| 33.145309
|
moderate speed
|
ɹaɪt? soʊ wi hæv ʌ mɑdʌl fɔɹ pi ʌv ɛks kɑmʌ zi,
|
very noisy
|
very roomy sounding
|
slightly monotone
|
um, with parameters,
|
um, with parameters,
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 108.890564
| 7.082969
| 2.56596
| 59.371422
|
slightly slowly
|
ʌm, wɪð pɜ˞æmʌtɜ˞z,
|
very noisy
|
very confined sounding
|
very monotone
|
Theta. And so, you know,
|
Theta. And so, you know,
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 146.830185
| 38.898563
| 15.732386
| 59.73193
|
slightly slowly
|
θeɪtʌ. ʌnd soʊ, ju noʊ,
|
very noisy
|
very confined sounding
|
slightly monotone
|
So if you look at this picture and you repeatedly color each point red or blue,
|
So if you look at this picture and you repeatedly color each point red or blue,
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 112.669571
| 18.394356
| 28.083261
| 58.903301
|
slightly fast
|
soʊ ɪf ju lʊk æt ðɪs pɪktʃɜ˞ ʌnd ju ɹɪpitɪdli kʌlɜ˞ itʃ pɔɪnt ɹɛd ɔɹ blu,
|
very noisy
|
very confined sounding
|
quite monotone
|
instead of, uh, writing out mu sigma,
|
instead of, uh, writing out mu sigma,
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 116.90387
| 16.648144
| 30.350761
| 53.370815
|
slightly slowly
|
ɪnstɛd ʌv, ʌ, ɹaɪtɪŋ aʊt mu sɪɡmʌ,
|
very noisy
|
quite confined sounding
|
very monotone
|
uh, mu sigma and phi like we did for the mixture of Gaussians,
|
uh, mu sigma and phi like we did for the mixture of Gaussians,
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 108.775574
| 18.937241
| 28.302486
| 40.135094
|
moderate speed
|
ʌ, mu sɪɡmʌ ʌnd faɪ laɪk wi dɪd fɔɹ ðʌ mɪkstʃɜ˞ ʌv ,
|
very noisy
|
slightly roomy sounding
|
quite monotone
|
I'm just gonna capture all the parameters you have,
|
I'm just gonna capture all the parameters you have,
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 104.254066
| 15.658262
| 37.687336
| 59.081726
|
very fast
|
aɪ'ɛm dʒʌst ɡɑnʌ kæptʃɜ˞ ɔl ðʌ pɜ˞æmʌtɜ˞z ju hæv,
|
quite noisy
|
very confined sounding
|
very monotone
|
whatever your parameters are, I'm just gonna capture them in one variable Theta.
|
whatever your parameters are, I'm just gonna capture them in one variable Theta.
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 100.317894
| 14.415561
| 17.289961
| 58.473034
|
slightly fast
|
wʌtɛvɜ˞ jɔɹ pɜ˞æmʌtɜ˞z ɑɹ, aɪ'ɛm dʒʌst ɡɑnʌ kæptʃɜ˞ ðɛm ɪn wʌn vɛɹiʌbʌl θeɪtʌ.
|
very noisy
|
very confined sounding
|
very monotone
|
And, uh, you only observe X.
|
And, uh, you only observe X.
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 108.374908
| 30.310671
| 10.522614
| 50.763245
|
very fast
|
ʌnd, ʌ, ju oʊnli ʌbzɜ˞v ɛks.
|
very noisy
|
slightly confined sounding
|
quite monotone
|
So your training set looks like that.
|
So your training set looks like that.
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 107.191055
| 40.224476
| 3.539959
| 44.859901
|
very fast
|
soʊ jɔɹ tɹeɪnɪŋ sɛt lʊks laɪk ðæt.
|
very noisy
|
moderate reverberation
|
slightly monotone
|
So the, um, log likelihood of the parameters Theta is equal
|
So the, um, log likelihood of the parameters Theta is equal
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 125.184189
| 42.117996
| 11.471537
| 36.573452
|
slightly slowly
|
soʊ ðʌ, ʌm, lɔɡ laɪklihʊd ʌv ðʌ pɜ˞æmʌtɜ˞z θeɪtʌ ɪz ikwʌl
|
very noisy
|
quite roomy sounding
|
slightly monotone
|
to sum over your training examples log p of X i parameterized by Theta.
|
to sum over your training examples log p of X i parameterized by Theta.
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 117.021408
| 41.927483
| 3.392992
| 40.076408
|
slightly slowly
|
tu sʌm oʊvɜ˞ jɔɹ tɹeɪnɪŋ ɪɡzæmpʌlz lɔɡ pi ʌv ɛks aɪ baɪ θeɪtʌ.
|
very noisy
|
slightly roomy sounding
|
slightly monotone
|
Um, and this in turn is log of sum over Z,
|
Um, and this in turn is log of sum over Z,
|
Lecture 14 - Expectation-Maximization Algorithms _ Stanford CS229 - Machine Learning (Autumn 2018)-rVfZHWTwXSA
| 112.990448
| 30.651232
| 8.019579
| 40.240692
|
quite slowly
|
ʌm, ʌnd ðɪs ɪn tɜ˞n ɪz lɔɡ ʌv sʌm oʊvɜ˞ zi,
|
very noisy
|
slightly roomy sounding
|
quite monotone
|
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