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01:11:39 yi.ma.4.13 yi.ma.4.13	yi.ma.4.13 In this talk, we offer an entirely “white box’’ interpretation of deep (convolution) networks from the perspective of data compression (and group invariance). In particular, we show… network networks thing lot class layers things rate structure classes space question optimization channels convolution measure representation compression backpropagation layer From Stefan Schonsheck on April 22nd, 2021 0 likes 4 plays 0
44:55 EPS 131 Lecture 16 (Third Part) Lecture 17 EPS 131 Lecture 16 (Third Part) Lecture 17	EPS 131 Lecture 16 (Third Part) Lecture 17 EPS 131 Lecture 16 (Third Part) Lecture 17 atmosphere dioxide carbon layer kilometers eruption earth radiation lot valley ozone lake lava caldera stratosphere boundary eruptions yellowstone light layers From John Rundle on February 17th, 2021 0 likes 6 plays 0
27:13 SSC107_2020_26_L13_M SSC107_2020_26_L13_M	SSC107_2020_26_L13_M flow water equation force pipe pressure velocity potential movement delta cylinder viscosity p2 volume soil p1 layers mu rate thing From Majdi Abou Najm on November 1st, 2020 0 likes 40 plays 0
19:04 Discussion 10-30 Discussion 10-30	Discussion 10-30 network validation function layer neuron training model testing accuracy activation neurons values batch layers kind epochs width batches callback input From Dhruvatara Bhogishetty on October 30th, 2020 0 likes 168 plays 0