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Course Level Date Clip# Line Interlocutor Content
Oral Assessments Graduate 2019-08-16 4 196 S01 +≈ like take (0.4) for you one step but (0.3) this is the
Oral Assessments Graduate 2019-08-16 4 197 S01 ⁎⁇learning⁇ step⁎↘
Oral Assessments Graduate 2019-08-16 4 198 UNK (0.8)
Oral Assessments Graduate 2019-08-16 4 199 S01 and (1.0) uh because in Newton method→ (0.3) th- there is-
Oral Assessments Graduate 2019-08-16 4 200 S01 a- (0.3) stuff gotta be very small↘ (0.3) so (0.6) it will
Oral Assessments Graduate 2019-08-16 4 201 S01 take so (0.3) much time (0.6) to do that↘
Oral Assessments Graduate 2019-08-16 4 202 UNK (0.3)
Oral Assessments Graduate 2019-08-16 4 203 T02 mhm
Oral Assessments Graduate 2019-08-16 4 204 UNK (0.7)
Oral Assessments Graduate 2019-08-16 4 205 T02 ∬ok∬
Oral Assessments Graduate 2019-08-16 4 206 S01 but this method will→ (0.5) according to→ according to the
Oral Assessments Graduate 2019-08-16 4 207 S01 situation↘
Oral Assessments Graduate 2019-08-16 4 208 UNK (0.3)
Oral Assessments Graduate 2019-08-16 4 209 S01 for example the here↗ (1.2) the gradient (0.3) you can see
Oral Assessments Graduate 2019-08-16 4 210 S01 the the slope is not (0.8) it's not very steep so→ (.) maybe
Oral Assessments Graduate 2019-08-16 4 211 S01 like the ⁇learning steep⁇ will be (.) just small↘ (.) but
Oral Assessments Graduate 2019-08-16 4 212 S01 (0.3) for here↗ (1.7) it's the ⁇right⁇↘
Oral Assessments Graduate 2019-08-16 4 213 UNK (0.3)
Oral Assessments Graduate 2019-08-16 4 214 S01 so the learning steep (0.3) will be big↘ so it will like→
Oral Assessments Graduate 2019-08-16 4 215 S01 (0.7) go very quickly↘