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1.Lecture 1 _ Introduction to Convolutio(Av925292805,P1)
57:57
2.Lecture 2 _ Image Classification(Av925292805,P2)
59:32
3.Lecture 3 _ Loss Functions and Optimiz(Av925292805,P3)
01:14:41
4.Lecture 4 _ Introduction to Neural Net(Av925292805,P4)
01:13:59
5.Lecture 5 _ Convolutional Neural Netwo(Av925292805,P5)
01:08:57
6.Lecture 6 _ Training Neural Networks I(Av925292805,P6)
01:20:20
7.Lecture 7 _ Training Neural Networks I(Av925292805,P7)
01:15:30
8.Lecture 8 _ Deep Learning Software(Av925292805,P8)
01:18:08
9.Lecture 9 _ CNN Architectures(Av925292805,P9)
01:17:40
10.Lecture 10 _ Recurrent Neural Network(Av925292805,P10)(1)
01:13:09
11.Lecture 11 _ Detection and Segmentati(Av925292805,P11)(1)
01:14:27
12.Lecture 12 _ Visualizing and Understa(Av925292805,P12)
01:15:48
13.Lecture 13 _ Generative Models(Av925292805,P13)
01:17:42
14.Lecture 14 _ Deep Reinforcement Learn(Av925292805,P14)
01:04:02
15.Lecture 15 _ Efficient Methods and Ha(Av925292805,P15)
01:16:53
16.Lecture 16 _ Adversarial Examples and(Av925292805,P16)
01:21:46
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