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Course Level Date Clip# Line Interlocutor Content
Oral Assessments Graduate 2024-07-19 3 97 T01 mm hmm
Oral Assessments Graduate 2024-07-19 3 98 S01 yes↘ (2.0) and uh:::: I get some examples
Oral Assessments Graduate 2024-07-19 3 99 S01 that like in artificial learning↘ we uh:::
Oral Assessments Graduate 2024-07-19 3 100 S01 (2.5) xxx the classical task called uh
Oral Assessments Graduate 2024-07-19 3 101 S01 semantic segmentation↘ that we have a
Oral Assessments Graduate 2024-07-19 3 102 S01 image↘ that the mos- that the xxx xxx xxx
Oral Assessments Graduate 2024-07-19 3 103 S01 will tell which party is what↘ that could
Oral Assessments Graduate 2024-07-19 3 104 S01 this is the person→ this is the road→
Oral Assessments Graduate 2024-07-19 3 105 S01 and it is a building↗ that type of things↘
Oral Assessments Graduate 2024-07-19 3 106 S01 this task is hard↘ (0.8) and the xxx xxx xxx
Oral Assessments Graduate 2024-07-19 3 107 S01 is hard to obtain↘ so many people will
Oral Assessments Graduate 2024-07-19 3 108 S01 start with digital model (0.4) to perform a
Oral Assessments Graduate 2024-07-19 3 109 S01 image computation task↘ you you will first
Oral Assessments Graduate 2024-07-19 3 110 S01 give you the a- average sum some xxx xxx xxx
Oral Assessments Graduate 2024-07-19 3 111 S01 continue pictures of dogs humans and
Oral Assessments Graduate 2024-07-19 3 112 S01 the buildings and the mechanism to (0.9)
Oral Assessments Graduate 2024-07-19 3 113 S01 decide which is which↘ and then it will
Oral Assessments Graduate 2024-07-19 3 114 S01 people will give this model a bigger image
Oral Assessments Graduate 2024-07-19 3 115 S01 can do other things and to make this
Oral Assessments Graduate 2024-07-19 3 116 S01 (1.3) algorithm to perform the segmentation↘
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