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Course Level Date Clip# Line Interlocutor Content
Oral Assessments Graduate 2019-08-14 3 116 S01 yeah↘
Oral Assessments Graduate 2019-08-14 3 117 T01 °mm hm°
Oral Assessments Graduate 2019-08-14 3 118 T01 you said something about adding noise to the image
Oral Assessments Graduate 2019-08-14 3 119 T01 what do you mean by noi:se
Oral Assessments Graduate 2019-08-14 3 120 S01 uh so that's a very specific details↘ uh i'll talk
Oral Assessments Graduate 2019-08-14 3 121 S01 about that
Oral Assessments Graduate 2019-08-14 3 122 S01 uh (0.3) ∇the noise is uh calculated by the gradient∇
Oral Assessments Graduate 2019-08-14 3 123 S01 uh of the loss↘ the machine learning uh s: models you
Oral Assessments Graduate 2019-08-14 3 124 S01 gives you give the input→ uh image of cat→ and it
Oral Assessments Graduate 2019-08-14 3 125 S01 will gives the output↘ the probability of each class↘ it
Oral Assessments Graduate 2019-08-14 3 126 S01 will classify↘ (.) and you calculate the output the
Oral Assessments Graduate 2019-08-14 3 127 S01 gradient (.) of output to the input and you moves the
Oral Assessments Graduate 2019-08-14 3 128 S01 input image (.) towards the direction of gradient then
Oral Assessments Graduate 2019-08-14 3 129 S01 the loss will increase and the original class probability
Oral Assessments Graduate 2019-08-14 3 130 S01 will decrease↘ so it will gradually misclassify↘
Oral Assessments Graduate 2019-08-14 3 131 UNK (0.7)
Oral Assessments Graduate 2019-08-14 3 132 S01 yeah↘
Oral Assessments Graduate 2019-08-14 3 133 T01 mm
Oral Assessments Graduate 2019-08-14 3 134 UNK (0.7)
Oral Assessments Graduate 2019-08-14 3 135 T01 ∆so why is it∆ important for us to be learning about
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