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Course Level Date Clip# Line Interlocutor Content
Oral Assessments Graduate 2019-08-16 4 36 S01 example (0.5) hm (0.9) here's a mountain↘
Oral Assessments Graduate 2019-08-16 4 37 UNK (7.1)
Oral Assessments Graduate 2019-08-16 4 38 S01 at first→ we will have a initial guess↘ (.) it's like
Oral Assessments Graduate 2019-08-16 4 39 S01 the start↘
Oral Assessments Graduate 2019-08-16 4 40 UNK (0.8)
Oral Assessments Graduate 2019-08-16 4 41 S01 For example you start here↘
Oral Assessments Graduate 2019-08-16 4 42 UNK (1.3)
Oral Assessments Graduate 2019-08-16 4 43 S01 we will fund a (.) local minimum↘ (.) which means
Oral Assessments Graduate 2019-08-16 4 44 S01 optimi- optimize↘
Oral Assessments Graduate 2019-08-16 4 45 UNK (0.7)
Oral Assessments Graduate 2019-08-16 4 46 S01 me- which means every every ⁇intuition⁇ will update our
Oral Assessments Graduate 2019-08-16 4 47 S01 data↘
Oral Assessments Graduate 2019-08-16 4 48 UNK (.)
Oral Assessments Graduate 2019-08-16 4 49 S01 and (.) so many intuition↘ after so many intuition→ (0.3)
Oral Assessments Graduate 2019-08-16 4 50 S01 we will get a minimum↘ (.) like we will (0.5) get the
Oral Assessments Graduate 2019-08-16 4 51 UNK (1.5) the minimum uh (.) here↘
Oral Assessments Graduate 2019-08-16 4 52 UNK (1.0)
Oral Assessments Graduate 2019-08-16 4 53 S01 so what's uh (0.5) this this is the (.) the result of
Oral Assessments Graduate 2019-08-16 4 54 S01 numerical optomization↘
Oral Assessments Graduate 2019-08-16 4 55 UNK (1.1)
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