19.1.1 introduction - part A25:14
29.1.2 introduction - part B18:59
39.1.3 introduction - available gpu18:10
49.2.1 ANN - introduction16:09
59.2.2 ANN - how it's work24:44
69.2.3 ANN - apllications, advantages and disadvantages19:47
79.3.1 tensorflow - mnist24:13
89.3.2 tensorflow - ANN exercise18:45
99.4.1 CNN - introduction17:27
109.4.2 CNN - cnn architecture28:05
119.4.3 CNN - example of implementation17:53
129.5.1 RNN - introduction and architectutre21:24
139.5.2 RNN - vanising and exploding gradient13:16
149.5.3 RNN - implementation example12:02
159.6.1 LSTM - introduction and architecture24:55
169.6.2 LSTM - bidirectional and implementation10:03
179.7.1 GAN - introduction19:42
189.7.2 GAN - gan architecture21:37
199.7.3 GAN - quick implementation14:15
209.8.1 autoencoders - therory25:13
219.8.2 autoencoders - implementation example23:19
