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Unreasonable Effectiveness of Learning Artificial Neural Networks
Published on Nov 28, 20162682 Views
Deep networks are some of the most widely used tools in data science. Learning is in principle a hard problem in these systems, but in practice heuristic algorithms often find solutions with good gene
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Chapter list
Unreasonable Effectiveness of Learning Artificial Neural Networks00:00
Plan of the talk00:40
Computational neuroscience & artificial neural system01:11
Dissecting Deep Neural Networks - 102:59
Dissecting Deep Neural Networks - 205:05
Dissecting Deep Neural Networks - 306:04
Convolutional layers07:33
Restricted Boltzmann Machines layers08:38
Auto-encoders layers - 109:18
Auto-encoders layers - 209:25
Auto-encoders layers - 310:12
Auto-encoders layers - 411:05
Auto-encoders layers - 512:56
Auto-encoders layers - 613:16
Speech recognition13:52
Deep translator - 114:58
Deep translator - 215:22
Current and short term applications - 116:22
Current and short term applications - 217:44
human vs. algorithms19:48
Search tree for Chess20:09
The game of GO20:56
Search tree for Go21:55
AlphaGo Overview - 122:15
AlphaGo Overview - 223:44
AlphaGo Overview - 324:03
Next challenges25:13
Still far away from AGI26:58
How does learning take place?29:01
Learning ~ energy minimisation problem29:15
The simplest neural device31:21
Geometry of the space of solutions32:53
Golf course34:36
Local entropy measure- 138:29
Local entropy measure - 238:49
Local entropy measure - 539:27
Principled algorithm40:51
Local entropy driven stochastic search41:15
Conclusions42:01