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{"captions":[{"end":"00:00:03.000","start":"00:00:02.990","text":"in this video I would like to star(...TRUNCATED)
videos/9CGGh6ivg68/9CGGh6ivg68
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[{"end":"00:00:04.870","start":"00:00:02.990","text":"in this video I would like to start the discus(...TRUNCATED)
1 introduction
9CGGh6ivg68
"in this video I would like to start the discussion about convolutional new networks which is anothe(...TRUNCATED)
"in this video. I would like to start the discussion about convolutional new networks, which is anot(...TRUNCATED)
117
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{"captions":[{"end":"00:00:02.000","start":"00:00:01.990","text":"to start understanding what is rea(...TRUNCATED)
videos/WXoOohWU28Y/WXoOohWU28Y
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[{"end":"00:00:04.390","start":"00:00:01.990","text":"to start understanding what is really the uh f(...TRUNCATED)
2 convolution
WXoOohWU28Y
"to start understanding what is really the uh fundamental operation happen inside those convolutiona(...TRUNCATED)
"to start understanding what is really the uh fundamental operation happen inside those convolutiona(...TRUNCATED)
328
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{"captions":[{"end":"00:00:03.280","start":"00:00:03.270","text":"so this is basically what is happe(...TRUNCATED)
videos/TV-DjM8242s/TV-DjM8242s
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[{"end":"00:00:06.789","start":"00:00:03.270","text":"so this is basically what is happening um in a(...TRUNCATED)
3 cnn architectures
TV-DjM8242s
"so this is basically what is happening um in a uh an example over here where we have an input image(...TRUNCATED)
"so this is basically what is happening, um, in a uh an example over here, where we have an input im(...TRUNCATED)
851
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{"captions":[{"end":"00:00:03.960","start":"00:00:03.950","text":"in this Example The Notebook uh is(...TRUNCATED)
videos/rCVlIVKqqGE/rCVlIVKqqGE
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[{"end":"00:00:07.550","start":"00:00:03.950","text":"in this Example The Notebook uh is quite uh in(...TRUNCATED)
4 image classification
rCVlIVKqqGE
"in this Example The Notebook uh is quite uh instructive because it refers to a small data set um an(...TRUNCATED)
"in this Example, The Notebook uh is quite uh instructive because it refers to a small data set, um,(...TRUNCATED)
311
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{"captions":[{"end":"00:00:02.320","start":"00:00:02.310","text":"in an earlier video we saw the str(...TRUNCATED)
videos/lb_5AdUpfuA/lb_5AdUpfuA
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[{"end":"00:00:05.749","start":"00:00:02.310","text":"in an earlier video we saw the structure of th(...TRUNCATED)
5 what cnns learn
lb_5AdUpfuA
"in an earlier video we saw the structure of the convolutional neuron and how many of these neurons (...TRUNCATED)
"in an earlier video we saw the structure of the convolutional neuron and how many of these neurons,(...TRUNCATED)
168
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{"captions":[{"end":"00:00:02.919","start":"00:00:02.909","text":"in an earli video we have seen the(...TRUNCATED)
videos/FCQ-rih6cHY/FCQ-rih6cHY
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[{"end":"00:00:06.150","start":"00:00:02.909","text":"in an earli video we have seen the uh convolut(...TRUNCATED)
6 residual networks
FCQ-rih6cHY
"in an earli video we have seen the uh convolutional networks and the basic operation uh in this uh (...TRUNCATED)
"in an earli video. we have seen the uh convolutional networks and the basic operation uh in this uh(...TRUNCATED)
402
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[{"end":"00:00:05.150","start":"00:00:01.909","text":"we have seen earlier the regression problem uh(...TRUNCATED)
1 the learning problem classification
eQ6UE968Xe4
"we have seen earlier the regression problem uh where we have effectively to model a conditional pro(...TRUNCATED)
"we have seen earlier the regression problem uh, where we have effectively to model a conditional pr(...TRUNCATED)
849
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videos/eFgkZKhNUdM/eFgkZKhNUdM
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2 logistic regression
eFgkZKhNUdM
"we are in the trajectory where we are going to obtain a Blog diagram of our first classifier binary(...TRUNCATED)
"we are in the trajectory where we are going to obtain a Blog diagram of our first classifier, binar(...TRUNCATED)
379
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