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Course Level Date Clip# Line Interlocutor Content
Oral Assessments Graduate 2024-07-19 3 57 S01 ⌊it is the tendency of artificial neural
Oral Assessments Graduate 2024-07-19 3 58 S01 networks↘ to forget abruptly and uh
Oral Assessments Graduate 2024-07-19 3 59 S01 completely what it had learned in previous
Oral Assessments Graduate 2024-07-19 3 60 S01 knowledge↘ (0.6) in the process of learning
Oral Assessments Graduate 2024-07-19 3 61 S01 new knowledge (1.0) it is very common
Oral Assessments Graduate 2024-07-19 3 62 S01 especially in current deep learning (0.8)
Oral Assessments Graduate 2024-07-19 3 63 S01 algorithms↘ (0.3) and it has been widely
Oral Assessments Graduate 2024-07-19 3 64 S01 discussed in the transfer learning domain↘
Oral Assessments Graduate 2024-07-19 3 65 UNK (0.5)
Oral Assessments Graduate 2024-07-19 3 66 T01 °mm hmm°
Oral Assessments Graduate 2024-07-19 3 67 S01 and xxx xxx xxx xxx xxx one of two reasons↘
Oral Assessments Graduate 2024-07-19 3 68 S01 (0.6) first↘ th- the xxx learning has many
Oral Assessments Graduate 2024-07-19 3 69 S01 applications↘ (0.3) as deep learning is uh
Oral Assessments Graduate 2024-07-19 3 70 S01 highly dependent on the quality of the data
Oral Assessments Graduate 2024-07-19 3 71 S01 set↘ however→ high quality data is not
Oral Assessments Graduate 2024-07-19 3 72 S01 always simple to obtain↘ so it has been
Oral Assessments Graduate 2024-07-19 3 73 S01 a common practice to utilize knowledge from
Oral Assessments Graduate 2024-07-19 3 74 UNK %err:PRO
Oral Assessments Graduate 2024-07-19 3 75 S01 other domain↘ other data sets to help
Oral Assessments Graduate 2024-07-19 3 76 S01 im- improve improvement uh the (0.9)
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