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| Course | Level | Date | Clip# | Line | Interlocutor | Content |
|---|---|---|---|---|---|---|
| Oral Assessments | Graduate | 2024-07-19 | 3 | 197 | S01 | node to repres- to represent the (0.6) the |
| Oral Assessments | Graduate | 2024-07-19 | 3 | 198 | S01 | the last parameter (.) as in ∆artificial∆ |
| Oral Assessments | Graduate | 2024-07-19 | 3 | 199 | S01 | ∆neural networks∆↗ the model are- would |
| Oral Assessments | Graduate | 2024-07-19 | 3 | 200 | S01 | can be trained in the parameter↘ and uh |
| Oral Assessments | Graduate | 2024-07-19 | 3 | 201 | S01 | and this point means we have the parameter |
| Oral Assessments | Graduate | 2024-07-19 | 3 | 202 | S01 | become after the last training step↘ |
| Oral Assessments | Graduate | 2024-07-19 | 3 | 203 | S01 | and then we use this model with new xxx xxx |
| Oral Assessments | Graduate | 2024-07-19 | 3 | 204 | S01 | data↘ a more preferred one↘ and then if we |
| Oral Assessments | Graduate | 2024-07-19 | 3 | 205 | S01 | do not put uh aggre- aggregations on them |
| Oral Assessments | Graduate | 2024-07-19 | 3 | 206 | S01 | (0.5) the new parameter will xxx far away |
| Oral Assessments | Graduate | 2024-07-19 | 3 | 207 | S01 | from the last one↘ that will start from |
| Oral Assessments | Graduate | 2024-07-19 | 3 | 208 | S01 | this one and to change gradually to a new |
| Oral Assessments | Graduate | 2024-07-19 | 3 | 209 | S01 | parameter set↘ (3.0) and uh (0.7) as I |
| Oral Assessments | Graduate | 2024-07-19 | 3 | 210 | S01 | mentioned that the second way is to (1.3) |
| Oral Assessments | Graduate | 2024-07-19 | 3 | 211 | S01 | regulate the new parameter set uh to convert |
| Oral Assessments | Graduate | 2024-07-19 | 3 | 212 | S01 | to convert in the- in a (0.5) local area↘ |
| Oral Assessments | Graduate | 2024-07-19 | 3 | 213 | S01 | from the last one↘ (0.7) that is in the |
| Oral Assessments | Graduate | 2024-07-19 | 3 | 214 | S01 | second- second xxx xxx↘ and the fourth |
| Oral Assessments | Graduate | 2024-07-19 | 3 | 215 | S01 | way is that the parameter xxx have many |
| Oral Assessments | Graduate | 2024-07-19 | 3 | 216 | S01 | parameters↘ and the way fix some (0.6) |